code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesser... | 691 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 1 |
from __future__ import annotations
import math
class A__ :
def __init__( self : Any , _a : int ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =size
# approximate the overall size of segment tree with given value
... | 691 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 691 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = ["image_processor", "tokenizer"]
UpperCAmelCase = "ViTImageProcessor"
UpperCAmelCase = (... | 691 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class A__ ( unittest.TestCase ):
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
... | 691 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_ava... | 691 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
snake_case_ : List[str] = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Pro... | 691 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 1 |
def lowerCamelCase( a__):
_SCREAMING_SNAKE_CASE =[int(a__) for i in ip_va_address.split('''.''') if i.isdigit()]
return len(a__) == 4 and all(0 <= int(a__) <= 254 for octet in octets)
if __name__ == "__main__":
snake_case_ : List[Any] = input().strip()
snake_case_ ... | 691 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils ... | 691 | 1 |
def lowerCamelCase( a__ ,a__ ,a__):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a__ ,n - 1 ,a__) * a) % mod
else:
_SCREAMING_SNAKE_CASE =binary_exponentiation(a__ ,n / 2 ,a__)
return (b * b) % mod
# a prime... | 691 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Confi... | 691 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = (DDPMParallelScheduler,)
def __UpperCamelCase ( self : Dict , **_a : List[str] ) -... | 691 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 1 |
# using dfs for finding eulerian path traversal
def lowerCamelCase( a__ ,a__ ,a__ ,a__=None):
_SCREAMING_SNAKE_CASE =(path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =True, True
... | 691 |
def lowerCamelCase( a__ ,a__ ,a__):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a__ ,n - 1 ,a__) * a) % mod
else:
_SCREAMING_SNAKE_CASE =binary_exponentiation(a__ ,n / 2 ,a__)
return (b * b) % mod
# a prime... | 691 | 1 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 691 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 691 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase( a__):
return (data["data"], data["target"])
... | 691 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case_ : Optional[Any] = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mode... | 691 | 1 |
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class A__ ( UpperCamelCase__ ):
def __lt__( self : int , _a : Union[str, Any] ) -> Any:
"""simple docstring"... | 691 |
from manim import *
class A__ ( UpperCamelCase__ ):
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Rectangle(height=0.5 , width=0.5 )
_SCREAMING_SNAKE_CASE =Rectangle(heig... | 691 | 1 |
from datetime import datetime as dt
import os
from github import Github
snake_case_ : Dict = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def lowerCamelCase( ):
_SCREAMIN... | 691 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Ble... | 691 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case_ : Optional[Any] = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mode... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 691 | 1 |
snake_case_ : Union[str, Any] = '''Tobias Carryer'''
from time import time
class A__ :
def __init__( self : List[Any] , _a : Any , _a : Union[str, Any] , _a : List[Any] , _a : Dict=int(time() ) ) -> ... | 691 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
... | 691 | 1 |
def lowerCamelCase( a__ ,a__ ,a__):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(a__))
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
# Base Case
if index == len(a__):
return True
# Recursive ... | 691 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case_ : Dict = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case_ : Union[str, Any] = ... | 691 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowerCamelCase( a__ ,a__):
_SCREAMING_SNAKE_CASE =list(a__)
_SCREAMING_SNAKE_CASE =list(a__)
_SCREAMING_SNAKE_CASE =0
for i in range(len(a__)):
if lista[i]... | 691 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase( a__):
def wrapper(*a__ ,**a__):
_SCREAMING_SNAKE_CASE =timeit.default_timer()
_SCREAMING_SNAKE_CASE =fun... | 691 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class A__ :
def __init__( self : Optional[Any] , _a : Any ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =str(id_ )
_SCREAMING_SNAKE_CASE ... | 691 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ : int = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''... | 691 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : str = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
''... | 691 | 1 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput,... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils i... | 691 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase( a__ ,a__ ,a__ ... | 691 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 1 |
def lowerCamelCase( a__ ,a__):
return base * power(a__ ,(exponent - 1)) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
snake_case_ : Dict = int(input('''Enter the base: ''').strip())
snake_ca... | 691 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (... | 691 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 | 1 |
import math
def lowerCamelCase( a__):
if not isinstance(a__ ,a__):
_SCREAMING_SNAKE_CASE =f"Input value of [number={number}] must be an integer"
raise TypeError(a__)
if number < 1:
_SCREAMING_SNAKE_CASE =f"Input value of [number={number}] must be > 0"
... | 691 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 | 1 |
from __future__ import annotations
snake_case_ : int = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
snake_case_ : Dict = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCamelCase( a__):
_SCREAMING_SNAKE_CASE =[]
_SCREAMI... | 691 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_ava... | 691 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
snake_case_ : List[str] = logging.getLogger(__name__)
class A__ ( UpperCamelCase__ ):
def __init__( self ... | 691 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils ... | 691 | 1 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A__ ( unittest.TestCase ):
def __UpperCamelCase ( ... | 691 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 691 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 1 |
from __future__ import annotations
snake_case_ : Dict = list[list[int]]
# assigning initial values to the grid
snake_case_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, ... | 691 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_v... | 691 |
def lowerCamelCase( a__ ,a__ ,a__):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a__ ,n - 1 ,a__) * a) % mod
else:
_SCREAMING_SNAKE_CASE =binary_exponentiation(a__ ,n / 2 ,a__)
return (b * b) % mod
# a prime... | 691 | 1 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case_ : Dict = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case_ : Union[str, Any] = ... | 691 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 691 | 1 |
from scipy.stats import pearsonr
import datasets
snake_case_ : List[str] = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumpti... | 691 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case_ : Optional[Any] = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mode... | 691 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Union[str, Any] = logging.get_logger(__name__)
snake_case_ : List[Any] = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config... | 691 |
from manim import *
class A__ ( UpperCamelCase__ ):
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Rectangle(height=0.5 , width=0.5 )
_SCREAMING_SNAKE_CASE =Rectangle(heig... | 691 | 1 |
import os
from math import logaa
def lowerCamelCase( a__ = "base_exp.txt"):
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
for i, line in enumerate(open(os.path.join(os.path.dirname(a__) ,a__))):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =list(map(a__ ... | 691 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Ble... | 691 | 1 |
from __future__ import annotations
def lowerCamelCase( a__ ,a__):
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =len(a__) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
_SCREAMING_SNAKE_... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 691 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 691 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
... | 691 | 1 |
def lowerCamelCase( a__):
if len(a__) < 2:
return collection
def circle_sort_util(a__ ,a__ ,a__) -> bool:
_SCREAMING_SNAKE_CASE =False
if low == high:
return swapped
_SCREAMING_SNAKE_CASE =low
_SCREAMING_SNAKE_CASE =high
... | 691 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case_ : Dict = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case_ : Union[str, Any] = ... | 691 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybrid... | 691 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase( a__):
def wrapper(*a__ ,**a__):
_SCREAMING_SNAKE_CASE =timeit.default_timer()
_SCREAMING_SNAKE_CASE =fun... | 691 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
snake_case_ : str = {
'''configuration_audio_spectrogram_transformer''': [
'''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 691 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : str = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
''... | 691 | 1 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenize... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils i... | 691 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
snake_case_ : Dict = logging.getLo... | 691 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 1 |
import math
class A__ :
def __init__( self : Optional[Any] , _a : Any=0 ) -> Dict: # a graph with Node 0,1,...,N-1
"""simple docstring"""
_SCREAMING_SNAKE_CASE =n
_SCREAMING_SNAKE_CASE =[
[math.inf for... | 691 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 | 1 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zs... | 691 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 | 1 |
def lowerCamelCase( a__ = 3 ,a__ = 7 ,a__ = 100_0000):
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =1
for current_denominator in range(1 ,limit + 1):
_SCREAMING_SNAKE_CASE =current_denominator * numerator // denominator
if current_denominator % den... | 691 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common i... | 691 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_ava... | 691 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = "Wav2Vec2FeatureE... | 691 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 1 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArgument... | 691 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils ... | 691 | 1 |
snake_case_ : int = range(2, 20 + 1)
snake_case_ : Any = [10**k for k in range(ks[-1] + 1)]
snake_case_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE =sum(a_i[j] for... | 691 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 | 1 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import Ar... | 691 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import... | 691 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 1 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 |
def lowerCamelCase( a__ ,a__ ,a__):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a__ ,n - 1 ,a__) * a) % mod
else:
_SCREAMING_SNAKE_CASE =binary_exponentiation(a__ ,n / 2 ,a__)
return (b * b) % mod
# a prime... | 691 | 1 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
snake_case_ : Optional[int] = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
... | 691 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 691 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class A__ ( pl.LightningModule ):
def __init__( self : List[str] , _a : Dict ) -> Optional[Any]:
... | 691 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case_ : Optional[Any] = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mode... | 691 | 1 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.robe... | 691 |
from manim import *
class A__ ( UpperCamelCase__ ):
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Rectangle(height=0.5 , width=0.5 )
_SCREAMING_SNAKE_CASE =Rectangle(heig... | 691 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.... | 691 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Ble... | 691 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
snake_case_ : List[Any] = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
snake_case_ :... | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 691 | 1 |
SCREAMING_SNAKE_CASE__ : Tuple = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""... | 0 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
... | 691 | 0 |
from math import pi, sqrt
def _A ( _lowercase ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError('math domain error' )
if num > 1_71.5:
raise OverflowError('math range error' )
elif num - int(_lowercase ) not in (0, 0.5):
... | 1 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case_ : Dict = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case_ : Union[str, Any] = ... | 691 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main... | 2 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase( a__):
def wrapper(*a__ ,**a__):
_SCREAMING_SNAKE_CASE =timeit.default_timer()
_SCREAMING_SNAKE_CASE =fun... | 691 | 0 |
'''simple docstring'''
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : List[str] = logging.get_logger(__name__)
... | 3 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 | 0 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ... | 4 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : str = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
''... | 691 | 0 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np... | 5 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils i... | 691 | 0 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 6 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : float ) -> float:
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(100, 0.2_5) = }''')
print(F'''{price_plus_t... | 7 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase):
'''simple docstring'''
__A : int = size
# approximate the overall ... | 8 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 | 0 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ = Lock()
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ... | 9 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 | 0 |
from __future__ import annotations
import math
def _snake_case ( __snake_case ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 10 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_ava... | 691 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase_ = logging.get_logger(__name__)... | 11 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 0 |
import unittest
import numpy as np
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ = None , ) -> np.ndarray:
'''simple docstring'''
lowercase__ : Tuple = np.shape(lowercase_ )
lowercase__ : Tuple = np.shape(lowercase_... | 12 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils ... | 691 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import gcd
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 1_50_00_00 ) -> int:
__lowerCamelCase : defaultdict = defaultdict(UpperCAmelCase_ )
__lowerCamelCase : Any ... | 13 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class UpperCAmelCase_ ... | 14 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 0 |
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
lowercase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstrin... | 15 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ..... | 16 |
def lowerCamelCase( a__ ,a__ ,a__):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a__ ,n - 1 ,a__) * a) % mod
else:
_SCREAMING_SNAKE_CASE =binary_exponentiation(a__ ,n / 2 ,a__)
return (b * b) % mod
# a prime... | 691 | 0 |
import math
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> str:
__A : Optional[int] = 0
__A : List[str] = 0
while num > 0:
__A : Optional[int] = num % 8
__A : List[Any] = octal + (remainder * math.floor(math.pow(10 ,a__ ) ))
counte... | 17 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 691 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_A... | 18 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
snake_case_ : Optional[Any] = R'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the mode... | 691 | 0 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_a... | 19 |
from manim import *
class A__ ( UpperCamelCase__ ):
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =Rectangle(height=0.5 , width=0.5 )
_SCREAMING_SNAKE_CASE =Rectangle(heig... | 691 | 0 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_lowerCAmelCase: Optional[Any] = datasets.logging.get_logger(__name__)
_lowerCAmelCase: int = '\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for ... | 20 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Ble... | 691 | 0 |
import argparse
import os
import re
UpperCAmelCase_ : Union[str, Any] = "src/transformers"
# Pattern that looks at the indentation in a line.
UpperCAmelCase_ : str = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCAmelCase_ :... | 21 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 691 | 0 |
'''simple docstring'''
import baseaa
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
return baseaa.baaencode(string.encode('''utf-8''' ) )
def snake_case_ (UpperCamelCase : bytes ):
'''simple docstring'''
... | 22 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCamelCase( a__ ,a__ ,a__ ,a__):
_SCREAMING_SNAKE_CASE ={
'''en''': '''Machine learning is great, isn\'t it?''',
'''ru''': '''Машинное обучение - это здорово, не так ли?''',
... | 691 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case__ : Dict = TypeVar("""T""")
class _a ( Generic[T] ):
"""simple docstring"""
A_ = 42 # Cache st... | 23 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
snake_case_ : Dict = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
snake_case_ : Union[str, Any] = ... | 691 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 24 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase( a__):
def wrapper(*a__ ,**a__):
_SCREAMING_SNAKE_CASE =timeit.default_timer()
_SCREAMING_SNAKE_CASE =fun... | 691 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a = None , _a = None):
if start is None:
SCREAMING_SNAKE_CASE : List[str] = 0
if end is None:
SCREAMING_SNAKE_CASE : List[Any] = len(_a) - 1
if start >= end:
return
SCREAMING_SNAKE_CASE... | 25 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
... | 691 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependenc... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : str = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransformerConfig''',
''... | 691 | 0 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(_SCREAM... | 27 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils i... | 691 | 0 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int = 10_00 ):
"""simple docstring"""
return sum(e for e in range(3 ,__UpperCamelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 28 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup... | 691 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
if len(lowerCAmelCase__ ) != 2 or len(a[0] ) != 2 or len(lowerCAmelCase__ ) != 2 or len(b[0] ) != 2:
raise Exception('''Matrices are not 2x2''' ... | 29 |
def lowerCamelCase( a__ ,a__):
return number | (1 << position)
def lowerCamelCase( a__ ,a__):
return number & ~(1 << position)
def lowerCamelCase( a__ ,a__):
return number ^ (1 << position)
def lowerCamelCase( a__ ,a__):
return ((number >> posi... | 691 | 0 |
from collections import deque
from .hash_table import HashTable
class __a( _a ):
"""simple docstring"""
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> List[Any]:
super().__init__(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
... | 30 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bar... | 691 | 0 |
def UpperCAmelCase_ ( __UpperCAmelCase : int ) -> int:
assert isinstance(__UpperCAmelCase , __UpperCAmelCase ), f"The input value of [n={number}] is not an integer"
if number == 1:
return 2
elif number < 1:
SCREAMING_SNAKE_CASE_ = ... | 31 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import ... | 691 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
... | 32 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_ava... | 691 | 0 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ... | 33 |
def lowerCamelCase( a__ ,a__):
return int((input_a, input_a).count(0) == 0)
def lowerCamelCase( ):
assert and_gate(0 ,0) == 0
assert and_gate(0 ,1) == 0
assert and_gate(1 ,0) == 0
assert and_gate(1 ,1) == 1
if __name__ == "__main__":
test_and_gate(... | 691 | 0 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_C... | 34 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transformers.file_utils ... | 691 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
a_ :Tuple = logging.get_logger(__name__)
a_ :Optional[An... | 35 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 691 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe i... | 36 |
class A__ :
def __init__( self : List[str] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE ={}
def __UpperCamelCase ( self :... | 691 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCamelCase : Dict = logging.get_logger(__name__)
def UpperCamelCase_ ( __a=None , __a=None ) -> List[str]:
... | 37 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 691 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : int , __magic_name__ : int ) -> int:
'''simple docstring'''
return int(input_a == input_a == 0 )
def UpperCamelCase__ ( ) -> None:
'''simple docstring'''
print("""Trut... | 38 |
def lowerCamelCase( a__ ,a__ ,a__):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(a__ ,n - 1 ,a__) * a) % mod
else:
_SCREAMING_SNAKE_CASE =binary_exponentiation(a__ ,n / 2 ,a__)
return (b * b) % mod
# a prime... | 691 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowerCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# Th... | 39 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_con... | 691 | 0 |
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