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 |
|---|---|---|---|---|
"""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
#
# Unle... | 256 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase = 299_792_458
# Symbols
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = symbols("""ct x y z""")
def lowercas... | 256 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowercase ( a__ : Optional[int] ) -> Optional[... | 256 | """simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrog... | 256 | 1 |
"""simple docstring"""
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase = logging.getLogger()
def lowercase ( a... | 256 | """simple docstring"""
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_obj... | 256 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
UpperCAmelCase ... | 256 | """simple docstring"""
def lowercase ( a__ : float , a__ : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) **... | 256 | 1 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ ( _lowercase... | 256 | """simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 256 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( a__ : int ) -> list[int]:
if num <= 0:
_UpperCamelCase = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(a__ )
_UpperCamelCas... | 256 | """simple docstring"""
def lowercase ( a__ : int , a__ : int ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def lowercase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == ... | 256 | 1 |
"""simple docstring"""
import math
def lowercase ( a__ : float , a__ : float ) -> float:
return math.pow(a__ , 2 ) - a
def lowercase ( a__ : float ) -> float:
return 2 * x
def lowercase ( a__ : float ) -> float:
... | 256 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 256 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
UpperCAmelCase = 0
UpperCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, ... | 256 | """simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_devi... | 256 | 1 |
"""simple docstring"""
def lowercase ( a__ : int ) -> "list[int]":
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
_UpperCamelCase = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
_UpperCamelCase ... | 256 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CON... | 256 | """simple docstring"""
from typing import List
import numpy as np
def lowercase ( a__ : dict ) -> int:
_UpperCamelCase = {key: len(a__ ) for key, value in gen_kwargs.items() if isinstance(a__ , a__ )}
if len(set(lists_lengths.values() ) ) > 1:
... | 256 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase = {
"""configuration_vision_text_dual_encoder""": ["""VisionTextDualEncoderConfig"""],
... | 256 | """simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS... | 256 | 1 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCAmelCase_ ( yaml.SafeLoader):
def _UpperCamelCase ( self : Optional[int] , __UpperCamelCase : Optional[Any] ) ... | 256 | """simple docstring"""
import logging
from transformers import PretrainedConfig
UpperCAmelCase = logging.getLogger(__name__)
UpperCAmelCase = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
... | 256 | 1 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def lowercase ( a__ : Optional[int] ) -> List[Any]:
return 1 / (1 + np.exp(-z ... | 256 | """simple docstring"""
def lowercase ( a__ : str ) -> list[int]:
_UpperCamelCase = [0 for i in range(len(a__ ) )]
# initialize interval's left pointer and right pointer
_UpperCamelCase , _UpperCamelCase = 0, 0
for i in range(1 , len... | 256 | 1 |
"""simple docstring"""
from math import factorial
UpperCAmelCase = {str(d): factorial(d) for d in range(10)}
def lowercase ( a__ : int ) -> int:
return sum(DIGIT_FACTORIAL[d] for d in str(a__ ) )
def lowercase ( ) -> int:
_UpperCamelCase ... | 256 | """simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ ( _lowercase... | 256 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
UpperCAmelCase = (720, 1_280) # Height, Width
UpperCAmelCase = (0.4, 0.6) # if height or width lower than this scale, drop it.
UpperCAmelCase = 1 / ... | 256 | """simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase ( a__ : str , a__ : bool = True , a__ : float = math.inf , a__ : float = -math.inf , a__ : float = math.inf , a__ : float = -math.inf , a__ : bool = False , a__... | 256 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_... | 256 | """simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCAmelCase = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targe... | 256 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorTyp... | 256 | """simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : Any ) -> Tu... | 256 | 1 |
"""simple docstring"""
def lowercase ( a__ : Union[str, Any] ) -> Optional[Any]:
_UpperCamelCase = len(a__ )
while cur > 1:
# Find the maximum number in arr
_UpperCamelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 t... | 256 | """simple docstring"""
def lowercase ( a__ : Union[str, Any] ) -> Optional[Any]:
_UpperCamelCase = len(a__ )
while cur > 1:
# Find the maximum number in arr
_UpperCamelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 t... | 256 | 1 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase... | 256 | """simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffu... | 256 | 1 |
"""simple docstring"""
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_ava... | 256 | """simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_fil... | 256 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_file""": """vocab.jso... | 256 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_lowercase):
snake_case__ = ['''flax''']
def __init__( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : ... | 256 | 1 |
"""simple docstring"""
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassificatio... | 256 | """simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_... | 256 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DD... | 256 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase = 299_792_458
# Symbols
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = symbols("""ct x y z""")
def lowercas... | 256 | 1 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowercase ( a__ : List[str] ) -> List[str]:
def wrapper(*a__ : Optional[Any] , **a__ : Dict ):
_... | 256 | """simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrog... | 256 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, Pat... | 256 | """simple docstring"""
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_obj... | 256 | 1 |
"""simple docstring"""
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 BartForConditionalGen... | 256 | """simple docstring"""
def lowercase ( a__ : float , a__ : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) **... | 256 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ... | 256 | """simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 256 | 1 |
"""simple docstring"""
def lowercase ( a__ : int , a__ : int ) -> int:
return abs(a__ ) if a == 0 else greatest_common_divisor(b % a , a__ )
def lowercase ( a__ : int , a__ : int ) -> int:
while y: # --> when y=0 then loop will terminate and r... | 256 | """simple docstring"""
def lowercase ( a__ : int , a__ : int ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def lowercase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == ... | 256 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
UpperCAmelCase = logging.get_lo... | 256 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 256 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transforme... | 256 | """simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_devi... | 256 | 1 |
"""simple docstring"""
from math import factorial
def lowercase ( a__ : int = 100 ) -> int:
return sum(int(a__ ) for x in str(factorial(a__ ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 256 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Co... | 256 | """simple docstring"""
from typing import List
import numpy as np
def lowercase ( a__ : dict ) -> int:
_UpperCamelCase = {key: len(a__ ) for key, value in gen_kwargs.items() if isinstance(a__ , a__ )}
if len(set(lists_lengths.values() ) ) > 1:
... | 256 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
UpperCAmelCase = """2020.9.26"""
UpperCAmelCase = """xcodz-dot, cclaus, dhruvmanila"""
def lowercase ( a__ : float , a__ : float , a__ : float , a__ : float , a__ : float ) -> tuple[float, float]:... | 256 | """simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS... | 256 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCAmelCase = TypeVar("""KEY""")
UpperCAmelCase = TypeVar("""VAL""")
@dataclass(frozen=_lowercase , slots=_lowercase)
class UpperCAmelC... | 256 | """simple docstring"""
import logging
from transformers import PretrainedConfig
UpperCAmelCase = logging.getLogger(__name__)
UpperCAmelCase = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
... | 256 | 1 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torc... | 256 | """simple docstring"""
def lowercase ( a__ : str ) -> list[int]:
_UpperCamelCase = [0 for i in range(len(a__ ) )]
# initialize interval's left pointer and right pointer
_UpperCamelCase , _UpperCamelCase = 0, 0
for i in range(1 , len... | 256 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
UpperCAmelCase = 3
def lowercase ( a__ : int ) -> int:
print('''Generating primitive root of p''' )
while True:
_UpperCamelCas... | 256 | """simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ ( _lowercase... | 256 | 1 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def lowercase ... | 256 | """simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase ( a__ : str , a__ : bool = True , a__ : float = math.inf , a__ : float = -math.inf , a__ : float = math.inf , a__ : float = -math.inf , a__ : bool = False , a__... | 256 | 1 |
"""simple docstring"""
import numpy as np
import datasets
UpperCAmelCase = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduc... | 256 | """simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCAmelCase = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targe... | 256 | 1 |
"""simple docstring"""
from __future__ import annotations
UpperCAmelCase = """#"""
class UpperCAmelCase_ :
def __init__( self : Union[str, Any] ) -> None:
_UpperCamelCase = {}
def _UpperCamelCase ( self : Any ... | 256 | """simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : Any ) -> Tu... | 256 | 1 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.... | 256 | """simple docstring"""
def lowercase ( a__ : Union[str, Any] ) -> Optional[Any]:
_UpperCamelCase = len(a__ )
while cur > 1:
# Find the maximum number in arr
_UpperCamelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 t... | 256 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """... | 256 | """simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffu... | 256 | 1 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 256 | """simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_fil... | 256 | 1 |
"""simple docstring"""
UpperCAmelCase = [0, 2, 4, 6, 8]
UpperCAmelCase = [1, 3, 5, 7, 9]
def lowercase ( a__ : int , a__ : int , a__ : list[int] , a__ : int ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
... | 256 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_lowercase):
snake_case__ = ['''flax''']
def __init__( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : ... | 256 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
UpperCAmelCase = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
""... | 256 | """simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_... | 256 | 1 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.uti... | 256 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase = 299_792_458
# Symbols
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = symbols("""ct x y z""")
def lowercas... | 256 | 1 |
"""simple docstring"""
def lowercase ( a__ : int ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(a__ , a__ ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(a__ ).count(... | 256 | """simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrog... | 256 | 1 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import T... | 256 | """simple docstring"""
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_obj... | 256 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/reso... | 256 | """simple docstring"""
def lowercase ( a__ : float , a__ : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) **... | 256 | 1 |
"""simple docstring"""
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data im... | 256 | """simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 256 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
def lowercase ( a__ : str , a__ : str , a__ : int ) -> Optional[int]:
_UpperCamelCase = Path(a__ )
_UpperCamelCase = Path(a__ )
dest_dir.mkdir(exist_ok=a__ )
for path in s... | 256 | """simple docstring"""
def lowercase ( a__ : int , a__ : int ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def lowercase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == ... | 256 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
import random
from typing import Any
class UpperCAmelCase_ :
def __init__( self : Optional[int] ) -> None:
_UpperCamelCase = []
_UpperCamelCase = 0
... | 256 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 256 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowercase ( a__ : List[str] ) -> List[str]:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class UpperCAmelCase_ ... | 256 | """simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_devi... | 256 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpee... | 256 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | """simple docstring"""
from typing import List
import numpy as np
def lowercase ( a__ : dict ) -> int:
_UpperCamelCase = {key: len(a__ ) for key, value in gen_kwargs.items() if isinstance(a__ , a__ )}
if len(set(lists_lengths.values() ) ) > 1:
... | 256 | 1 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class UpperCAmelCase_ ( _lowercase):
def __init__( self : Union[str, Any] , *__UpperCamelCase : Any , **__UpperCamelCase : Any ) -> Optional[int]:
... | 256 | """simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS... | 256 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.jso... | 256 | """simple docstring"""
import logging
from transformers import PretrainedConfig
UpperCAmelCase = logging.getLogger(__name__)
UpperCAmelCase = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
... | 256 | 1 |
"""simple docstring"""
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
UpperCAmelCase = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesyst... | 256 | """simple docstring"""
def lowercase ( a__ : str ) -> list[int]:
_UpperCamelCase = [0 for i in range(len(a__ ) )]
# initialize interval's left pointer and right pointer
_UpperCamelCase , _UpperCamelCase = 0, 0
for i in range(1 , len... | 256 | 1 |
"""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
from sagem... | 256 | """simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ ( _lowercase... | 256 | 1 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffu... | 256 | """simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase ( a__ : str , a__ : bool = True , a__ : float = math.inf , a__ : float = -math.inf , a__ : float = math.inf , a__ : float = -math.inf , a__ : bool = False , a__... | 256 | 1 |
from importlib import import_module
from .logging import get_logger
UpperCAmelCase__ = get_logger(__name__)
class lowercase_ :
'''simple docstring'''
def __init__( self : int , __UpperCAmelCase : Optional[int]... | 0 | """simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCAmelCase = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targe... | 256 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int = 50_00_00_00 ) -> int:
'''simple docstring'''
UpperCAmelCase_ = set()
UpperCAmelCase_ = int((limit - 24) ** (1 / 2) )
UpperCAmelCase_ = set(range(3 , prime... | 1 | """simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : Any ) -> Tu... | 256 | 0 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
lowerCamelCase : Optional[Any] = 'docs/source/en/_toctree.yml'
def _SCREAMING_SNAKE_CASE (A ) -> List[Any]:
"""simple docstring"""
lowercase__ = default... | 2 | """simple docstring"""
def lowercase ( a__ : Union[str, Any] ) -> Optional[Any]:
_UpperCamelCase = len(a__ )
while cur > 1:
# Find the maximum number in arr
_UpperCamelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 t... | 256 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICom... | 3 | """simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffu... | 256 | 0 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__snake_case =0B1011_0011_1110_1100_1001_000... | 4 | """simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_fil... | 256 | 0 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 5 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_lowercase):
snake_case__ = ['''flax''']
def __init__( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : ... | 256 | 0 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __A( a ):
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
if to... | 6 | """simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_... | 256 | 0 |
lowercase_ = "Alexander Joslin"
import operator as op
from .stack import Stack
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> int:
'''simple docstring'''
A__ = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
A_... | 7 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase = 299_792_458
# Symbols
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = symbols("""ct x y z""")
def lowercas... | 256 | 0 |
import json
import sys
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
with open(SCREAMING_SNAKE_CASE__ , encoding='''utf-8''' ) as f:
snake_case_ = json.load(SCREAMING_SNAKE_CASE__ )
snake_case_ = ['''<details>''', ''... | 8 | """simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrog... | 256 | 0 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Dict = 0.00
__SCREAMING_SNAKE_CASE : List[str] = 0
for resistor in resistors:
if resistor <= 0:
__SCREAMING_SNAKE_CASE ... | 9 | """simple docstring"""
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_obj... | 256 | 0 |
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,
Da... | 10 | """simple docstring"""
def lowercase ( a__ : float , a__ : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) **... | 256 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCA... | 11 | """simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 256 | 0 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase... | 12 | """simple docstring"""
def lowercase ( a__ : int , a__ : int ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def lowercase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == ... | 256 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNe... | 13 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 256 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ = "" ) -> dict[str, float]:
"""simple docstring"""
A__ = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
A__... | 14 | """simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_devi... | 256 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
SCREAMING_SNAKE_CASE :Optional[int] = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n ... | 15 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | 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 SPIECE_UNDERLINE, logging
lowerCAme... | 16 | """simple docstring"""
from typing import List
import numpy as np
def lowercase ( a__ : dict ) -> int:
_UpperCamelCase = {key: len(a__ ) for key, value in gen_kwargs.items() if isinstance(a__ , a__ )}
if len(set(lists_lengths.values() ) ) > 1:
... | 256 | 0 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_a = logging.getLogger()
@unittest.skip("Temporarily disable the doc... | 17 | """simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS... | 256 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def _snake_case ( lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Tuple , lowerCAmelCase : ... | 18 | """simple docstring"""
import logging
from transformers import PretrainedConfig
UpperCAmelCase = logging.getLogger(__name__)
UpperCAmelCase = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
... | 256 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 19 | """simple docstring"""
def lowercase ( a__ : str ) -> list[int]:
_UpperCamelCase = [0 for i in range(len(a__ ) )]
# initialize interval's left pointer and right pointer
_UpperCamelCase , _UpperCamelCase = 0, 0
for i in range(1 , len... | 256 | 0 |
import random
from typing import Any
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> list[Any]:
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
lowercase : List[Any] = random.randint(0 , len(SCREAMING_SNAKE_CASE__ ) - 1 )
lowercas... | 20 | """simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ ( _lowercase... | 256 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCamelCase_( lowerCamelCase_ ) -> str:
return "".join(sorted(lowerCamelCase_ ) )
def UpperCamelCase_( lowerCamelCase_ ) -> list[str]:
return word_by_signature[signatur... | 21 | """simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase ( a__ : str , a__ : bool = True , a__ : float = math.inf , a__ : float = -math.inf , a__ : float = math.inf , a__ : float = -math.inf , a__ : bool = False , a__... | 256 | 0 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__SCREAMING_SNAKE_CASE :List[Any] = 0b1_0_1_1_0_0_1... | 22 | """simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCAmelCase = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targe... | 256 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.... | 23 | """simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
def _UpperCamelCase ( self : List[str] , __UpperCamelCase : Any ) -> Tu... | 256 | 0 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_pr... | 24 | """simple docstring"""
def lowercase ( a__ : Union[str, Any] ) -> Optional[Any]:
_UpperCamelCase = len(a__ )
while cur > 1:
# Find the maximum number in arr
_UpperCamelCase = arr.index(max(arr[0:cur] ) )
# Reverse from 0 t... | 256 | 0 |
"""simple docstring"""
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 transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def lowercase_ ( _sn... | 25 | """simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffu... | 256 | 0 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaF... | 26 | """simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_fil... | 256 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 27 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_lowercase):
snake_case__ = ['''flax''']
def __init__( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : ... | 256 | 0 |
'''simple docstring'''
import os
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
_lowerCamelCase : str = logging.get_logger(__... | 28 | """simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_... | 256 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_c... | 29 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase = 299_792_458
# Symbols
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = symbols("""ct x y z""")
def lowercas... | 256 | 0 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_dat... | 30 | """simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrog... | 256 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> str | Literal[False]:
"""simple docstring"""
_UpperCAmelCase ... | 31 | """simple docstring"""
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_obj... | 256 | 0 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 32 | """simple docstring"""
def lowercase ( a__ : float , a__ : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) **... | 256 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 33 | """simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 256 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://huggingfac... | 34 | """simple docstring"""
def lowercase ( a__ : int , a__ : int ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def lowercase ( ) -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == ... | 256 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
... | 35 | """simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeature... | 256 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = FileLock(str(tmpdir / "foo.lock" ) )
_lowerCAmelCase : Union[str, Any] ... | 36 | """simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_devi... | 256 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : list[list[int]] = [[0 for _ in range(UpperCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowerCAmelCase__ : Tuple ... | 37 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | 0 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : Optional[Any] , *__lowerCamelCas... | 38 | """simple docstring"""
from typing import List
import numpy as np
def lowercase ( a__ : dict ) -> int:
_UpperCamelCase = {key: len(a__ ) for key, value in gen_kwargs.items() if isinstance(a__ , a__ )}
if len(set(lists_lengths.values() ) ) > 1:
... | 256 | 0 |
from heapq import heappop, heappush
import numpy as np
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )-> tuple[float | int, list[tuple[int, int]]]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase ... | 39 | """simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS... | 256 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):... | 40 | """simple docstring"""
import logging
from transformers import PretrainedConfig
UpperCAmelCase = logging.getLogger(__name__)
UpperCAmelCase = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
... | 256 | 0 |
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