code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'Conv... | 646 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : Tuple = {
'sai... | 15 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Tuple = logging.get_logger(__name__)
__A : Optional[Any] = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}... | 334 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class A ( Uppe... | 15 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( UpperCAmelCase__ : List[Any] ... | 320 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Union[str, Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvN... | 15 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
... | 291 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : List[str]=7 ) -> Dict:
"""simple docstring"""
lowercase__ = ... | 15 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/de... | 536 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 15 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Datase... | 558 |
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 Ac... | 15 | 0 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __lowerCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''s... | 391 |
import os
import sys
A : Tuple = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassific... | 15 | 0 |
'''simple docstring'''
UpperCAmelCase_ : List[Any] = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git... | 120 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 15 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFOR... | 96 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = [
"""encoder.version""",
... | 15 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_ve... | 140 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 | 0 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_UpperCamelCase : Union... | 396 |
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.'
)
| 15 | 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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAG... | 646 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A ( UpperCAmelCase__ ):
'''simple docstring'''
... | 15 | 0 |
'''simple docstring'''
import qiskit
def UpperCAmelCase ( lowerCamelCase_ :int = 2 ):
'''simple docstring'''
snake_case_ : int = qubits
# Using Aer's simulator
snake_case_ : List[str] = qiskit.Aer.get_backend("""aer_simulator""" )
# Creatin... | 334 |
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 A ( unittest.TestCase ):
'''simple docstring'''
... | 15 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import DistilBertConfig, 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_avail... | 320 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 15 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowerCAmelCase ( UpperCAmelCase__ ):
def A__ ( self , lowerCAmelCase ) -> float:
'''simple docstring''... | 291 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase__ = len(__magic_name__ )
# If the array contains only one element, we return it (it's the sto... | 15 | 0 |
'''simple docstring'''
import copy
import re
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
_a : List[Any] = '''hp'''
_a : int = {}
_a : Tuple = None
@classmethod
def _a ( cls , UpperCamelCase__ , UpperCamelCas... | 536 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A : ... | 15 | 0 |
import numpy as np
import datasets
_UpperCAmelCase = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Pr... | 558 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedin... | 15 | 0 |
"""simple docstring"""
import argparse
import struct
import unittest
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self: Union[str, Any] , UpperCamelCase_: bytes ):
UpperCamelCase_ =data
# Initialize hash values
UpperCamelCase_ ... | 391 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 15 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCAmelCase_ : str = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def Up... | 120 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : Any , __magic_name__ : List[str] , __magic_name__... | 15 | 0 |
"""simple docstring"""
def a ( __UpperCAmelCase : int , __UpperCAmelCase : list ) -> str:
_enforce_args(__UpperCAmelCase , __UpperCAmelCase )
if n == 0:
return 0
__magic_name__: int = float("""-inf""... | 96 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Tuple = {
'kssteven/ibert-roberta-base': ... | 15 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipeline... | 140 |
from math import log
from scipy.constants import Boltzmann, physical_constants
A : Any = 3_0_0 # TEMPERATURE (unit = K)
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , ) -> ... | 15 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers... | 396 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = N... | 15 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase_ ( _lowerCamelCase: list[int] , _lowerCamelCase: int ) -> list[int]:
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = 0
__lowerCamelCase : int = len(_lowerCamelCase ... | 646 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : Tuple = {
'sai... | 15 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def UpperCAmelCase ( lowerCamelCase_ :Callable[[float], float] , lowerCamelCase_ :float , lowerCamelCase_ :float ):
'''simple docstring'''
snake_case_ : Any = xa
snake_case_ : Opt... | 334 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class A ( Uppe... | 15 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {'vocab_file': 'vocab.json'}
A = {
'vocab_file': {
... | 320 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Union[str, Any] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvN... | 15 | 0 |
import math
import random
def a ( A__ : float , A__ : bool = False ) -> float:
"""simple docstring"""
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowercase_ = 0.0_2
def a ( A... | 291 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : List[str]=7 ) -> Dict:
"""simple docstring"""
lowercase__ = ... | 15 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCAmelCase = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfi... | 536 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplif... | 15 | 0 |
def UpperCamelCase ( __lowercase : str ):
'''simple docstring'''
if not all(x.isalpha() for x in string ):
raise ValueError('String must only contain alphabetic characters.' )
A_ : int = sorted(string.lower() )
return len(__lowercase ) =... | 558 |
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 Ac... | 15 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
A_ = get_tests_dir("fixtures/test_sentencepiece_wi... | 391 |
import os
import sys
A : Tuple = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassific... | 15 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_devi... | 120 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, id... | 15 | 0 |
"""simple docstring"""
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import Combined... | 96 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Tuple:
"""simple docstring"""
lowercase__ = [
"""encoder.version""",
... | 15 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 140 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizer... | 15 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCamelCase : Optional[Any] = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'to... | 396 |
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.'
)
| 15 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaToke... | 646 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class A ( UpperCAmelCase__ ):
'''simple docstring'''
... | 15 | 0 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Any = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/m... | 334 |
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 A ( unittest.TestCase ):
'''simple docstring'''
... | 15 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTester... | 320 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 15 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
lowercase_ = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'),
... | 291 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
lowercase__ = len(__magic_name__ )
# If the array contains only one element, we return it (it's the sto... | 15 | 0 |
'''simple docstring'''
import os
import string
import sys
__lowerCAmelCase = 1 << 8
__lowerCAmelCase = {
'tab': ord("\t"),
'newline': ord("\r"),
'esc': 27,
'up': 65 + ARROW_KEY_FLAG,
'down': 66 + ARROW_KEY_FLAG,
'right': 67 + ARROW_KEY_FLAG,
'left':... | 536 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
A : ... | 15 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOut... | 558 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
A : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedin... | 15 | 0 |
"""simple docstring"""
from math import factorial, radians
def _UpperCamelCase ( A , A = 18 , A = 10 ):
UpperCamelCase_ =angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
UpperCamelCase_ =radians(A ... | 391 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 15 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from dataset... | 16 |
from __future__ import annotations
def __a ( A__ : list[int | str] ):
create_state_space_tree(A__ , [] , 0 , [0 for i in range(len(A__ ) )] )
def __a ( A__ : list[int | str] , A__ : list[int | str] , A__ : int , A__... | 16 | 1 |
from math import factorial
def __a ( A__ : int , A__ : int , A__ : float ):
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
raise ValueError("the function is defined for... | 16 |
def __a ( A__ : int = 1000 ):
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ ... | 16 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Optional[Any] = {
'vocab_file': 'vocab.json',
'tokenizer_config_f... | 16 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Dict = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxCon... | 16 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[str] = {
'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
'configuration_maskformer_swin':... | 16 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 16 | 1 |
import logging
import os
from .state import PartialState
class _SCREAMING_SNAKE_CASE ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def _snake_case ( __lowerCamelCase : int ):
SCREAMING_SNAKE_CASE = Partia... | 16 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 16 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[Any] , __lowerCamelCase : Collection[... | 16 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__A : Optional[Any] = loggin... | 16 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 16 |
import os
def __a ( ):
SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(A__ ) , "num.txt" )
with open(A__ ) as file_hand:
return str(sum(int(A__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(so... | 16 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def __a ( A__ : Tuple ):
for param in module.parameters():
SCREAMING_SNAKE_CASE = False
def __a ( ):
SCREAMING_SNAKE_CASE = "cuda" if torch.cuda.... | 16 |
import pytest
__A : Optional[Any] = '__dummy_dataset1__'
__A : Optional[int] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "valida... | 16 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 16 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__A : str = logging.get_logger(__name__)
_... | 16 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from t... | 16 |
from typing import Any
import numpy as np
def __a ( A__ : np.ndarray ):
return np.array_equal(A__ , matrix.conjugate().T )
def __a ( A__ : np.ndarray , A__ : np.ndarray ):
SCREAMING_SNAKE_CASE = v.conjugate().T
S... | 16 | 1 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils im... | 16 |
from __future__ import annotations
__A : str = list[tuple[int, int]]
__A : Optional[int] = [
[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, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[... | 16 | 1 |
from ..utils import DummyObject, requires_backends
class _SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
'''simple docstring'''
lowerCamelCase__ = ["sentencepiece"]
def __init__( self : List[Any] , *__lowerCamelCase : str ... | 16 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__A : int = logging.get_logger(__name__)
__A : List[str] = OrderedDict(
... | 16 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : int = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'... | 16 |
def __a ( A__ : float , A__ : float ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(A__ ) * abs(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 16 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.... | 16 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A : Dict = logging.get_logger(__nam... | 16 | 1 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
Reques... | 16 |
from collections.abc import Callable
import numpy as np
def __a ( A__ : Callable , A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = int(np.ceil((x_end - xa) / step_size ) )
SCREAMING_SNAKE_CASE ... | 16 | 1 |
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,
SkipDat... | 16 |
def __a ( A__ : int ):
if not isinstance(A__ , A__ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ... | 16 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _SCREAMING_SNAKE_CASE ( __snake_case , ... | 16 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__A : List[Any] = {'UserAgent': UserAgent().random}
def __a ( A__ : List[Any] ):
SCREAMING_SNAKE_CASE = script.conte... | 16 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
__A : Dict = get_logger(__name__)
__A : Tuple = r'\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, ... | 16 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 16 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Optional[int] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
... | 16 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( A__ : str , A__ : List[Any]=None ):
SCREAMING_SNAKE_CASE = None
if token is not None:
SC... | 16 | 1 |
def __a ( A__ : int ):
if not isinstance(A__ , A__ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ... | 16 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[Any] = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPT... | 16 | 1 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
__A : List[... | 16 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
if ... | 16 | 1 |
from math import isqrt
def __a ( A__ : int ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(A__ ) + 1 ) )
def __a ( A__ : int = 10**6 ):
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE ... | 16 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSe... | 16 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _SCREAMING... | 16 |
from __future__ import annotations
def __a ( A__ : list[int | str] ):
create_state_space_tree(A__ , [] , 0 , [0 for i in range(len(A__ ) )] )
def __a ( A__ : list[int | str] , A__ : list[int | str] , A__ : int , A__... | 16 | 1 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerT... | 16 |
def __a ( A__ : int = 1000 ):
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ ... | 16 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('>=', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpo... | 16 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Dict = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxCon... | 16 | 1 |
def __a ( A__ : int ):
if num <= 0:
raise ValueError("Input must be a positive integer" )
SCREAMING_SNAKE_CASE = [True] * (num + 1)
SCREAMING_SNAKE_CASE = 2
while p * p <= num:
if primes[p]:
for i in range(p * p ... | 16 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 16 | 1 |
import random
def __a ( A__ : int , A__ : float , A__ : bool = False ):
SCREAMING_SNAKE_CASE = {i: [] for i in range(A__ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return com... | 16 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 16 | 1 |
from __future__ import annotations
import math
def __a ( A__ : int ):
if num <= 0:
SCREAMING_SNAKE_CASE = F"{num}: Invalid input, please enter a positive integer."
raise ValueError(A__ )
SCREAMING_SNAKE_CASE = [True] * (num... | 16 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__A : Optional[Any] = loggin... | 16 | 1 |
import re
def __a ( A__ : str ):
SCREAMING_SNAKE_CASE = re.compile(R"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(A__ , A__ ):
return match.string == phone
return False
if __name__ == "__main__":
print(indian_p... | 16 |
import os
def __a ( ):
SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(A__ ) , "num.txt" )
with open(A__ ) as file_hand:
return str(sum(int(A__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(so... | 16 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from... | 16 |
import pytest
__A : Optional[Any] = '__dummy_dataset1__'
__A : Optional[int] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "valida... | 16 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full... | 16 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__A : str = logging.get_logger(__name__)
_... | 16 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A : Dict = logging.get_logger(__nam... | 16 |
from typing import Any
import numpy as np
def __a ( A__ : np.ndarray ):
return np.array_equal(A__ , matrix.conjugate().T )
def __a ( A__ : np.ndarray , A__ : np.ndarray ):
SCREAMING_SNAKE_CASE = v.conjugate().T
S... | 16 | 1 |
def __a ( A__ : int ):
SCREAMING_SNAKE_CASE = [1]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0, 0, 0
SCREAMING_SNAKE_CASE = ugly_nums[ia] * 2
SCREAMING_SNAKE_CASE = ugly_nums[ia] * 3
SCREA... | 16 |
from __future__ import annotations
__A : str = list[tuple[int, int]]
__A : Optional[int] = [
[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, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[... | 16 | 1 |
from __future__ import annotations
import math
__A : Optional[int] = '2020.9.26'
__A : Any = 'xcodz-dot, cclaus, dhruvmanila'
def __a ( A__ : float , A__ : float , A__ : float , A__ : float , A__ : float ):
if not all(isinst... | 16 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__A : int = logging.get_logger(__name__)
__A : List[str] = OrderedDict(
... | 16 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDete... | 16 |
def __a ( A__ : float , A__ : float ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(A__ ) * abs(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 16 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : List[... | 16 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A : Dict = logging.get_logger(__nam... | 16 | 1 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __a ( A__ : Any ):
SCREAMING_SNAKE_CASE ... | 16 |
from collections.abc import Callable
import numpy as np
def __a ( A__ : Callable , A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = int(np.ceil((x_end - xa) / step_size ) )
SCREAMING_SNAKE_CASE ... | 16 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils imp... | 16 |
def __a ( A__ : int ):
if not isinstance(A__ , A__ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ... | 16 | 1 |
from string import ascii_lowercase, ascii_uppercase
def __a ( A__ : str ):
if not sentence:
return ""
SCREAMING_SNAKE_CASE = dict(zip(A__ , A__ ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) + sentence[1:]
if __... | 16 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__A : List[Any] = {'UserAgent': UserAgent().random}
def __a ( A__ : List[Any] ):
SCREAMING_SNAKE_CASE = script.conte... | 16 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __a ( A__ : str , A__ : Optional[int] , A__ : int ):
# Initialise PyTorch model
SCREA... | 16 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conv... | 16 | 1 |
from __future__ import annotations
from random import choice
def __a ( A__ : int ):
return choice(A__ )
def __a ( A__ : list[int] , A__ : int ):
SCREAMING_SNAKE_CASE = random_pivot(A__ )
# partition based on... | 16 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __a ( A__ : str , A__ : List[Any]=None ):
SCREAMING_SNAKE_CASE = None
if token is not None:
SC... | 16 | 1 |
def __a ( A__ : int ):
if not isinstance(A__ , A__ ):
SCREAMING_SNAKE_CASE = F"Input value of [number={number}] must be an integer"
raise TypeError(A__ )
if number < 0:
return False
SCREAMING_SNAKE_CASE = number * numb... | 16 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Optional[Any] = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPT... | 16 | 1 |
import os
def __a ( ):
with open(os.path.dirname(A__ ) + "/p022_names.txt" ) as file:
SCREAMING_SNAKE_CASE = str(file.readlines()[0] )
SCREAMING_SNAKE_CASE = names.replace("\"" , "" ).split("," )
names.sort()
... | 16 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
if ... | 16 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : List[Any] = ... | 16 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSe... | 16 | 1 |
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassif... | 16 |
from __future__ import annotations
def __a ( A__ : list[int | str] ):
create_state_space_tree(A__ , [] , 0 , [0 for i in range(len(A__ ) )] )
def __a ( A__ : list[int | str] , A__ : list[int | str] , A__ : int , A__... | 16 | 1 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_to... | 16 |
def __a ( A__ : int = 1000 ):
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ ... | 16 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class _SCREAMING_SNAKE_CASE ( nn.Module ):
'''simple docstring'''
lowerCamelCase__ = 42
lowerCamelCase__ = jnp.floataa
def _snake_case ( self : Tuple ):
... | 16 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : Dict = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'BigBirdPegasusOnnxCon... | 16 | 1 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple , __lowerCamelCase : list[tuple[float, float]] ):
SCREAMING_SNAKE_CASE ... | 16 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@s... | 16 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __a ... | 16 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 16 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__A : Optional[Any] = loggin... | 16 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__A : Optional[Any] = loggin... | 16 | 1 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 16 |
import os
def __a ( ):
SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(A__ ) , "num.txt" )
with open(A__ ) as file_hand:
return str(sum(int(A__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(so... | 16 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__A : Union[str, Any]... | 16 |
import pytest
__A : Optional[Any] = '__dummy_dataset1__'
__A : Optional[int] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "valida... | 16 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : Optional[Any] = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class _SCREAMING_SNAKE... | 16 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__A : str = logging.get_logger(__name__)
_... | 16 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A : Optional[Any] = logging.get_logger(__name__)
__A : List[str] = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
# See all M... | 16 |
from typing import Any
import numpy as np
def __a ( A__ : np.ndarray ):
return np.array_equal(A__ , matrix.conjugate().T )
def __a ( A__ : np.ndarray , A__ : np.ndarray ):
SCREAMING_SNAKE_CASE = v.conjugate().T
S... | 16 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if... | 16 |
from __future__ import annotations
__A : str = list[tuple[int, int]]
__A : Optional[int] = [
[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, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[... | 16 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 16 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__A : int = logging.get_logger(__name__)
__A : List[str] = OrderedDict(
... | 16 | 1 |
def __a ( A__ : int = 2000000 ):
SCREAMING_SNAKE_CASE = [0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
... | 16 |
def __a ( A__ : float , A__ : float ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(A__ ) * abs(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 16 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __a ( A__ : str = "laptop" ):
SCREAMING_SNAKE_CASE = F"https://www.amazon.in/laptop/s?k={product}"
SCREAMING_SNAKE_CASE = {
"Us... | 16 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__A : Dict = logging.get_logger(__nam... | 16 | 1 |
from __future__ import annotations
from math import pow, sqrt
def __a ( A__ : float , A__ : float , A__ : float ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance ... | 16 |
from collections.abc import Callable
import numpy as np
def __a ( A__ : Callable , A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = int(np.ceil((x_end - xa) / step_size ) )
SCREAMING_SNAKE_CASE ... | 16 | 1 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__A : Tuple = 2_9_9_7_9_2_4_5_8
# Symbols
__A , __A , __A , __A : Dict = symbols('ct x y z')
def __a ( A__ : float ):
if velocity > ... | 16 |
def __a ( A__ : int ):
if not isinstance(A__ , A__ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("Input must be positive" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ... | 16 | 1 |
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