code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class snake_case_ :
UpperCAmelCase__ : Optional[Union[str, Path]] = None
UpperCAmelCase__ : bool = False
UpperCAmelCase__ :... | 240 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase_ ( UpperCamelCase_ ):
"""simple d... | 338 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Blipa... | 296 | 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__ : List[Any] = logging.get_logger(__name__)
lowercase__... | 338 | 0 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase : Any = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/model... | 13 | import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( UpperCamelCase_ ):
"""simple doc... | 338 | 0 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __lo... | 298 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
... | 338 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_comm... | 201 | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if i... | 338 | 0 |
lowerCAmelCase : Optional[int] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def A_ ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = input('Enter message: ' )
SCREAMING_SNAKE_CASE_ : Any = input('Enter key [alphanumeric]: ' )
S... | 253 | from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ : int = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowercase__ : Option... | 338 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class _a :
__a : Optional[str] = field(
default="""codeparrot/codeparrot""" , metadata={"""help""": """Model name or path of model to be trained."""} )
... | 34 | lowercase__ : Optional[int] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def SCREAMING_SNAKE_CASE_ ( ) -> None:
lowerCAmelCase = input('''Enter message: ''' )
lowerCAmelCase = input('''Enter key [alphanumeric]: ''' )
lowerCAmelCase = input('''Encrypt/Decrypt [... | 338 | 0 |
'''simple docstring'''
def a_ ( _lowerCAmelCase ) -> int:
if n == 1 or not isinstance(snake_case__ ,snake_case__ ):
return 0
elif n == 2:
return 1
else:
__lowerCamelCase : int = [0, 1]
for i in range(2 ... | 208 | from collections import defaultdict
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case__ = 1_0_0_0_0_0_0 , snake_case__ = 1_0 ) -> int:
lowerCAmelCase = defaultdict(snake_case__ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
... | 338 | 0 |
'''simple docstring'''
def lowercase_ ( _lowercase , _lowercase ) -> int:
'''simple docstring'''
return 1 if input_a == input_a else 0
def lowercase_ ( ) -> None:
'''simple docstring'''
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
... | 318 | import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> Union... | 338 | 0 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : Dict , lowerCAmelCase_ : List[str] ):
if len(snake_case__ ) == 0:
return False
__lowercase : str = len(snake_case__ ) // 2
if a_list[midpoint] == item:
... | 233 | def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> str:
if isinstance(snake_case__ , snake_case__ ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(snake_case__ , snake_case__ ):
raise Ty... | 338 | 0 |
'''simple docstring'''
from math import loga
def lowerCamelCase__ ( _A ):
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(snake_case__ , snake_case__ ):
raise TypeError('Input value must be a \'int\' type' )
return 0 if (a == 0... | 297 | class lowercase_ :
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ->Any:
lowerCAmelCase = name
lowerCAmelCase = value
lowerCAmelCase = weight
def __repr__( ... | 338 | 0 |
def __lowercase ( __lowerCAmelCase : Dict , __lowerCAmelCase : str ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def __lowercase ( __lowerCAmelCase : int , __lowerCAmelCase : str=0 ):
return sorted(sna... | 240 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowercase__ : Dict = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(... | 338 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
... | 296 | import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE_ ( self , **__SCREA... | 338 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from ... | 13 | import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowercase__ : str = lo... | 338 | 0 |
'''simple docstring'''
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
... | 298 | import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 338 | 0 |
def lowerCAmelCase_ ( __UpperCAmelCase: List[Any] , __UpperCAmelCase: Union[str, Any] ) -> list[int]:
UpperCamelCase__ : Optional[int] = int(snake_case__ )
# Initialize Result
UpperCamelCase__ : Optional[Any] = []
... | 201 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase__ : List[str] = 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
# This ... | 338 | 0 |
from __future__ import annotations
lowerCAmelCase : Optional[int] = tuple[int, int, int]
lowerCAmelCase : str = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# ---... | 253 | import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name='''my_d... | 338 | 0 |
'''simple docstring'''
from math import sqrt
def snake_case_ (_a : Optional[Any] = 1_0_0_0_0_0_0 ):
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 4_2
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_sh... | 34 | import unittest
import numpy as np
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ) -> np.ndarray:
lowerCAmelCase = np.shape(snake_case__ )
lowerCAmelCase = np.shape(snake_case__ )
lowerCAmelCase ... | 338 | 0 |
'''simple docstring'''
import sys
import turtle
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ... | 208 | import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowercase__ : Any = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/model... | 338 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__lowercase : List[Any] = 4
__lowercase : Dict = 3
class __low... | 318 | from ...processing_utils import ProcessorMixin
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = ["""image_processor""", """feature_extractor"""]
UpperCAmelCase_ : Optional[int] = """TvltImageProcessor"""
UpperCAmelC... | 338 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCamelCase : Dict = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP... | 233 | def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> List[str]:
lowerCAmelCase = len(snake_case__ )
for i in range(length - 1 ):
lowerCAmelCase = i
for k in range(i + 1 , snake_case__ ):
if collection[k] < coll... | 338 | 0 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import T... | 297 | import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 338 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub... | 240 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase_ ( UpperCamelCase_ ):
"""simple d... | 338 | 0 |
import numpy as np
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : List[Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = (0, 0)
SCREAMING_SNAKE_CASE = None
... | 296 | 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__ : List[Any] = logging.get_logger(__name__)
lowercase__... | 338 | 0 |
from __future__ import annotations
def A_ ( _UpperCAmelCase , _UpperCAmelCase ):
if len(snake_case__ ) < k or k < 0:
raise ValueError("Invalid Input" )
SCREAMING_SNAKE_CASE_: List[Any] = sum(array[:k] )
for i in range(len(snake_case__ ) - k ... | 13 | import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( UpperCamelCase_ ):
"""simple doc... | 338 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class A :
'''simple docstring'''
def __init__(self , _UpperCAmelCase ) -> None:
__UpperCamelCase : Dict = num_of_nodes
__UpperCame... | 298 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
... | 338 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
... | 201 | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if i... | 338 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtract... | 253 | from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ : int = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowercase__ : Option... | 338 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig... | 34 | lowercase__ : Optional[int] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def SCREAMING_SNAKE_CASE_ ( ) -> None:
lowerCAmelCase = input('''Enter message: ''' )
lowerCAmelCase = input('''Enter key [alphanumeric]: ''' )
lowerCAmelCase = input('''Encrypt/Decrypt [... | 338 | 0 |
'''simple docstring'''
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_comm... | 208 | from collections import defaultdict
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case__ = 1_0_0_0_0_0_0 , snake_case__ = 1_0 ) -> int:
lowerCAmelCase = defaultdict(snake_case__ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
... | 338 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase : int = logging.get_logger(__name__)
__lowercase : int = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',... | 318 | import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> Union... | 338 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.... | 233 | def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> str:
if isinstance(snake_case__ , snake_case__ ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(snake_case__ , snake_case__ ):
raise Ty... | 338 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
... | 297 | class lowercase_ :
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ->Any:
lowerCAmelCase = name
lowerCAmelCase = value
lowerCAmelCase = weight
def __repr__( ... | 338 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NA... | 240 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowercase__ : Dict = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(... | 338 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestC... | 296 | import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE_ ( self , **__SCREA... | 338 | 0 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : List[Any] = logging.get_logger(__name__)
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CAS... | 13 | import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowercase__ : str = lo... | 338 | 0 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class A ( UpperCamelCase_ ):
'''simple docstring'''
A = (CMStochasticIterativeScheduler,)
A ... | 298 | import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 338 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nl... | 201 | import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase__ : List[str] = 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
# This ... | 338 | 0 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _A ... | 253 | import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_3_3_7 , num_examples=4_2 , dataset_name='''my_d... | 338 | 0 |
'''simple docstring'''
import random
def snake_case_ (_a : int , _a : Optional[int] , _a : Tuple = False ):
UpperCAmelCase = {i: [] for i in range(snake_case__ )}
# if probability is greater or equal than 1, then generate a complete gra... | 34 | import unittest
import numpy as np
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ) -> np.ndarray:
lowerCAmelCase = np.shape(snake_case__ )
lowerCAmelCase = np.shape(snake_case__ )
lowerCAmelCase ... | 338 | 0 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
_UpperCamelCase = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Bu... | 208 | import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowercase__ : Any = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/model... | 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Optional[int] = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Bl... | 318 | from ...processing_utils import ProcessorMixin
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = ["""image_processor""", """feature_extractor"""]
UpperCAmelCase_ : Optional[int] = """TvltImageProcessor"""
UpperCAmelC... | 338 | 0 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from tran... | 233 | def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> List[str]:
lowerCAmelCase = len(snake_case__ )
for i in range(length - 1 ):
lowerCAmelCase = i
for k in range(i + 1 , snake_case__ ):
if collection[k] < coll... | 338 | 0 |
'''simple docstring'''
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.pi... | 297 | import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 338 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
snake_case : L... | 240 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase_ ( UpperCamelCase_ ):
"""simple d... | 338 | 0 |
import unittest
import numpy as np
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , ) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = np.shape(snake_case... | 296 | 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__ : List[Any] = logging.get_logger(__name__)
lowercase__... | 338 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STAND... | 13 | import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( UpperCamelCase_ ):
"""simple doc... | 338 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipe... | 298 | import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
... | 338 | 0 |
import math
def lowerCAmelCase_ ( __UpperCAmelCase: List[str] ) -> bool:
UpperCamelCase__ : Union[str, Any] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(snake_case__ )
def lowerCA... | 201 | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if i... | 338 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(__name__)
lowerCAmelCase : List[Any] = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}
c... | 253 | from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ : int = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowercase__ : Option... | 338 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMix... | 34 | lowercase__ : Optional[int] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def SCREAMING_SNAKE_CASE_ ( ) -> None:
lowerCAmelCase = input('''Enter message: ''' )
lowerCAmelCase = input('''Enter key [alphanumeric]: ''' )
lowerCAmelCase = input('''Encrypt/Decrypt [... | 338 | 0 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_UpperCamelCase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defi... | 208 | from collections import defaultdict
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case__ = 1_0_0_0_0_0_0 , snake_case__ = 1_0 ) -> int:
lowerCAmelCase = defaultdict(snake_case__ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
... | 338 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowercase : Any = logging.get_logger(__name__)
__lowercase : Any = {
'''mi... | 318 | import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> Union... | 338 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCamelCase : Any = logging.get_logger(__name__)
class lowerCAmelCase ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self : Optional[Any] , __a : Dict=None ... | 233 | def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> str:
if isinstance(snake_case__ , snake_case__ ):
raise TypeError('''\'float\' object cannot be interpreted as an integer''' )
if isinstance(snake_case__ , snake_case__ ):
raise Ty... | 338 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase: int = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try... | 297 | class lowercase_ :
"""simple docstring"""
def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ->Any:
lowerCAmelCase = name
lowerCAmelCase = value
lowerCAmelCase = weight
def __repr__( ... | 338 | 0 |
# Imports
import numpy as np
class snake_case_ :
def __init__( self :Optional[Any] ,__snake_case :Union[str, Any]=None ,__snake_case :Dict=None ,__snake_case :Dict=None ,__snake_case :Dict=None ,__snake_case :List[str]=None ) -> int:
self.set_ma... | 240 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowercase__ : Dict = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(... | 338 | 0 |
from datetime import datetime as dt
import os
from github import Github
SCREAMING_SNAKE_CASE_ = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''feature request''',
'''new model''',
'''wip''',
]
def __lowercase ... | 296 | import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE_ ( self , **__SCREA... | 338 | 0 |
import math
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = [True] * n
SCREAMING_SNAKE_CASE_: int = False
SCREAMING_SNAKE_CASE_: int = False
SCREAMING_SNAKE_CASE_: Optional[int] = True
for i in range(3 , int(n**0.5 + 1... | 13 | import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
lowercase__ : str = lo... | 338 | 0 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import l... | 298 | import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 338 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def A ( _UpperCAmelCase : str = "isbn/0140328726" ) -> dict:
'''simple docstring'''
_UpperCAmelCase = olid.strip().strip('/' ) # Remove leading/trailing white... | 339 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and... | 339 | 1 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[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, 0, 8, 6, 3, 0, 0, 5],
... | 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import ... | 339 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...te... | 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
... | 339 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : dict , _UpperCAmelCase : str ) -> set[str]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = set(_UpperCAmelCase ), [start]
while stack:
_UpperCAmelCase ... | 339 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_ca... | 339 | 1 |
def A ( _UpperCAmelCase : dict ) -> set:
'''simple docstring'''
_UpperCAmelCase = set()
# edges = list of graph's edges
_UpperCAmelCase = get_edges(_UpperCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_... | 339 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504452445231617... | 339 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __lowerCAmelCase ( A ):
... | 339 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAme... | 339 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoToke... | 339 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_to... | 339 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1... | 339 | 1 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid... | 339 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'h... | 339 | 1 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def A ( _UpperCAmelCase : Optional[int] ) -> List[Any]... | 339 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 339 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : list , _UpperCAmelCase : int | None = None , _UpperCAmelCase : int | None = None ) -> None:
'''simple docstring'''
if start is None:
_UpperCAmelCase = 0
if end is None:
... | 339 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late ... | 339 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A ( _UpperCAmelCase : Tuple ... | 339 |
import os
import sys
import unittest
UpperCAmelCase__ = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, re... | 339 | 1 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase__ = logging.getLogger()
def A ( ... | 339 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
Data... | 339 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class __lowerCAmelCase ( A ):
def _lowerCamelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
return [
{"col... | 339 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] ... | 339 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembe... | 339 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
f... | 339 | 1 |
def A ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
... | 339 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[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, 0, 8, 6, 3, 0, 0, 5],
... | 339 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
UpperCAmelCase__ = _LazyMod... | 339 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ =... | 339 | 1 |
from ...configuration_utils import PretrainedConfig
class __lowerCAmelCase ( A ):
UpperCamelCase = '''bert-generation'''
def __init__( self : Optional[Any] , A : Dict=5_03_58 , A : Any=10_24 , A : Optional[int]=24 , A : int=16 , A : Any=40_96 , A ... | 339 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32acce... | 339 | 1 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are prim... | 339 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
U... | 339 | 1 |
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> str:
'''simple docstring'''
return "\n".join(
F"{number} * {i} = {number * i}" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(num... | 339 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
def __init__( self : int) -> str:
"""simple docstring"""
_UpperCAmelCase = []
def _lowerCamelCase ( self : Any , A : List[str]) -> int:
"""... | 339 | 1 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
_UpperCAmelCase = sorted(string.lower() )
return len(_UpperCAmelCase ) ... | 339 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def A ( _UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=5 ) -> List[Any]:
'''simple docstring'''
... | 339 | 1 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1... | 339 |
import math
import unittest
def A ( _UpperCAmelCase : int ) -> bool:
'''simple docstring'''
assert isinstance(_UpperCAmelCase , _UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are prim... | 339 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer... | 339 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase__ = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and... | 339 | 1 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib... | 339 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import ... | 339 | 1 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
def _lowerCamelCase ( se... | 339 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class __lowerCAmelCase ( A ):
... | 339 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 339 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_ca... | 339 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 339 |
from functools import reduce
UpperCAmelCase__ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504452445231617... | 339 | 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 __lowerCAmelCase ( unittest.TestCase ):
def _l... | 339 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def A ( _UpperCAmelCase : Matrix , _UpperCAmelCase : Matrix ) -> Matrix:
'''simple docstring'''
_UpperCAmelCase = len(_UpperCAme... | 339 | 1 |
from __future__ import annotations
def A ( _UpperCAmelCase : int ) -> list[int]:
'''simple docstring'''
_UpperCAmelCase = [True] * limit
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = True
for i in range(... | 339 |
from __future__ import annotations
def A ( _UpperCAmelCase : list[int] ) -> bool:
'''simple docstring'''
return len(set(_UpperCAmelCase ) ) == len(_UpperCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 339 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __lowerCAmelCase ( A ):
def _lowerCamelCase ( self : Any , A : str) -> Optional[Any]:
"""simple docstring"""
with open(A , encodin... | 339 |
import os
UpperCAmelCase__ = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def A ( _UpperCAmelCase : str ) -> int:
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(_UpperCAmelCase ) - 1... | 339 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase ( A ):
UpperCamelCase = '''ClapFeatureExtractor'''
UpperCamelCase = ('''RobertaTokenizer''', '''RobertaTokenizerFast''')
def __init__( ... | 339 |
import requests
from bsa import BeautifulSoup
def A ( _UpperCAmelCase : str , _UpperCAmelCase : dict ) -> str:
'''simple docstring'''
_UpperCAmelCase = BeautifulSoup(requests.get(_UpperCAmelCase , params=_UpperCAmelCase ).content , 'h... | 339 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"YituTech/conv-bert-base": "https://huggingface.... | 339 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 339 | 1 |
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def A ( _UpperCAmelCase : str ) -> bool:
'''simple docstring'''
_UpperCAmelCase = credit_ca... | 339 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late ... | 339 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ... | 339 |
import os
import sys
import unittest
UpperCAmelCase__ = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, re... | 339 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( A ):
UpperCamelCase = (EulerDiscreteScheduler,)
UpperCamelCase = 1_0
def _lowerCame... | 339 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
Data... | 339 | 1 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
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 import ConfigTester
f... | 339 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def A ( _UpperCAmelCase : Union[str, Any] ... | 339 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils... | 339 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
f... | 339 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCAmelCase__ = TypeVar("KEY")
UpperCAmelCase__ = TypeVar("VAL")
@dataclass(frozen=A , slots=A )
class __lowerCAmelCase ( Generic[KEY, VAL] ... | 339 |
from __future__ import annotations
UpperCAmelCase__ = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ = [
[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, 0, 8, 6, 3, 0, 0, 5],
... | 339 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all ... | 339 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
UpperCAmelCase__ = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
UpperCAmelCase__ =... | 339 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
f... | 339 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
UpperCAmelCase__ = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32acce... | 339 | 1 |
import argparse
from collections import defaultdict
def A ( _UpperCAmelCase : int , _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : int , _UpperCAmelCase : str ) -> Union[str, Any]:
'''simple docstring'''
... | 339 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
U... | 339 | 1 |
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