code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
import pandas as pd
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> list[int]:
"""simple docstring"""
_snake_case = [0] * no_of_processes
_snake_case = [0] * no_of_processes
# Copy the burst ti... | 278 |
__A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def snake_case_(_UpperCamelCase ) -> bytes:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
_snake_case = F"""a bytes-like object is required, no... | 278 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
l... | 278 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__A = logging.get_logger(__name__)
class lowercase_ ( __lowercase ):
def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : ... | 278 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase_ ( __lowercase ):
UpperCamelCase_ ... | 278 |
from __future__ import annotations
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = get_failure_array(_UpperCamelCase )
# 2) Step through text searching for pattern
_snake_case, _snake_case = 0, 0 ... | 278 | 1 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_snake_case = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
_snake_case ... | 278 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase_ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase_ : List[Any] = [("size", ctypes.c_int... | 278 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
fr... | 278 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
i... | 278 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew... | 278 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.t... | 278 | 1 |
def snake_case_(_UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
_snake_case = [0] * len(_UpperCamelCase )
_snake_case = []
_snake_case = []
_snake_case = 0
for values in graph.values():
for i in values:
inde... | 278 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 278 | 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 = logging.get_logger(__name__)
__A = {
'''google/mobilenet_v1_1.0_224''': '''h... | 278 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 278 | 1 |
from math import factorial
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(_UpperCamelCase ) // (factorial(_UpperCam... | 278 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 278 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''AltCLIPTextConfig''',
... | 278 |
import cmath
import math
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> complex:
"""simple docstring"""
_snake_case = math.radians(_UpperCamelCase )
_snake_case = math.radians(_UpperCamelCase )
# Con... | 278 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewTokensCriteria,... | 278 |
# 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
#
# Unless required by applica... | 278 | 1 |
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for ve... | 278 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
_snake_case = tau * frequency / samplerate
_snake_case ... | 278 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPegasusOnnxConfig'''... | 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pi... | 278 | 1 |
from importlib import import_module
from .logging import get_logger
__A = get_logger(__name__)
class lowercase_ :
def __init__( self : List[Any] , A__ : Any , A__ : str=None ) -> str:
_snake_case = attrs or []
if module i... | 278 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
_s... | 278 | 1 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> list[int]:
"""simple docstring"""
_snake_case = int(_UpperCamelCase )
# Initialize Result
_snake_case = []
# Traverse through all denomination
for denomination in reversed(_UpperCamelCase ):... | 278 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependenc... | 278 | 1 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import Conf... | 278 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is... | 278 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.t... | 278 |
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=__lowercase ):
UpperCamelCase_ : Optional[int] = ["speech"]
def __init__( self : str , *A__ : List[str] , **A__ : Tuple ) -> Optional[Any]:
requi... | 278 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase_ ( __lowercase ):
@staticmethod
@abstractmethod
def UpperCamelCase_ ( A__ : ArgumentParser ) -> Optional[int]:
raise NotImplementedError()
@abstractmet... | 278 |
from math import factorial
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(_UpperCamelCase ) // (factorial(_UpperCam... | 278 | 1 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__A = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__A = [file for file in filepaths if file != file.lower()]
if upper_f... | 278 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_snake_case = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
_snake_case ... | 278 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ta... | 278 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = ... | 278 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is... | 278 |
__A = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',... | 278 | 1 |
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
__A = logging.get_logger(__name__)
__A = ... | 278 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,... | 278 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
__A = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
__A = requests.get(url, headers={'''UserAgent''': U... | 278 |
__A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def snake_case_(_UpperCamelCase ) -> bytes:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
_snake_case = F"""a bytes-like object is required, no... | 278 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_... | 278 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__A = logging.get_logger(__name__)
class lowercase_ ( __lowercase ):
def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : ... | 278 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class lowercase_ ( __lowercase ):
UpperCamelCa... | 278 |
from __future__ import annotations
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = get_failure_array(_UpperCamelCase )
# 2) Step through text searching for pattern
_snake_case, _snake_case = 0, 0 ... | 278 | 1 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Dict:
"""simple docstring"""
_snake_case = OmegaConf.load(_UpperCamelCase ... | 278 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase_ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase_ : List[Any] = [("size", ctypes.c_int... | 278 | 1 |
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 CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 278 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
i... | 278 | 1 |
def snake_case_(_UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = [int(_UpperCamelCase ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(_UpperCamelCase ) == 4 and all(0 <= int(_UpperCamelCase ) <= 254 for octet in octets ... | 278 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.t... | 278 | 1 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def snake_case_(_UpperCamelCase , _UpperCamelCase=() , _UpperCamelCase=None , _UpperCamelCase="no" , _UpperCamelCase="29500" ... | 278 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 278 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 278 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 278 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
'''ClapTextConfig''',
],
... | 278 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 278 | 1 |
import numpy as np
from transformers import Pipeline
def snake_case_(_UpperCamelCase ) -> str:
"""simple docstring"""
_snake_case = np.max(_UpperCamelCase , axis=-1 , keepdims=_UpperCamelCase )
_snake_case = np.exp(outputs - maxes )
return shift... | 278 |
import cmath
import math
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> complex:
"""simple docstring"""
_snake_case = math.radians(_UpperCamelCase )
_snake_case = math.radians(_UpperCamelCase )
# Con... | 278 | 1 |
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, PatchingSpec
from ...utils... | 278 |
# 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
#
# Unless required by applica... | 278 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 278 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
_snake_case = tau * frequency / samplerate
_snake_case ... | 278 | 1 |
from __future__ import annotations
def snake_case_(_UpperCamelCase ) -> float:
"""simple docstring"""
if not nums:
raise ValueError('''List is empty''' )
return sum(_UpperCamelCase ) / len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.test... | 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pi... | 278 | 1 |
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 lowercase_ ( __lowercase , __lowercase ):
@register_to_config
def __init__( self ... | 278 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
_s... | 278 | 1 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowercase_ ( enum.Enum ):
UpperCamelCa... | 278 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependenc... | 278 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def snake_case_(_UpperCamelCase ) -> ... | 278 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is... | 278 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from... | 278 |
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=__lowercase ):
UpperCamelCase_ : Optional[int] = ["speech"]
def __init__( self : str , *A__ : List[str] , **A__ : Tuple ) -> Optional[Any]:
requi... | 278 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = ... | 278 |
from math import factorial
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(_UpperCamelCase ) // (factorial(_UpperCam... | 278 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENE... | 278 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_snake_case = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
_snake_case ... | 278 | 1 |
import math
def snake_case_(_UpperCamelCase ) -> bool:
"""simple docstring"""
return math.sqrt(_UpperCamelCase ) * math.sqrt(_UpperCamelCase ) == num
def snake_case_(_UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = 0... | 278 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = ... | 278 | 1 |
from ...processing_utils import ProcessorMixin
class lowercase_ ( __lowercase ):
UpperCamelCase_ : Union[str, Any] = ["image_processor", "feature_extractor"]
UpperCamelCase_ : Optional[int] = "TvltImageProcessor"
UpperCamelCase_ : Optional[int] ... | 278 |
__A = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',... | 278 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils ... | 278 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,... | 278 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 278 |
__A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def snake_case_(_UpperCamelCase ) -> bytes:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
_snake_case = F"""a bytes-like object is required, no... | 278 | 1 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
while a != 0:
_snake_case, _snake_case = b % a, a
return b
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if ... | 278 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__A = logging.get_logger(__name__)
class lowercase_ ( __lowercase ):
def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : ... | 278 | 1 |
__A = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',... | 278 |
from __future__ import annotations
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = get_failure_array(_UpperCamelCase )
# 2) Step through text searching for pattern
_snake_case, _snake_case = 0, 0 ... | 278 | 1 |
import os
def snake_case_() -> Optional[int]:
"""simple docstring"""
_snake_case = os.path.dirname(os.path.realpath(_UpperCamelCase ) )
_snake_case = os.path.join(_UpperCamelCase , '''triangle.txt''' )
with open(_UpperCamelCase ) as f:
_sn... | 278 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase_ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase_ : List[Any] = [("size", ctypes.c_int... | 278 | 1 |
from jiwer import compute_measures
import datasets
__A = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected spe... | 278 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
i... | 278 | 1 |
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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A = ... | 278 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.t... | 278 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pi... | 278 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 278 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1_337 , num_examples=42 , dataset_name='''my_dataset''' ... | 278 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 278 | 1 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowercase ):
UpperCamelCase_ : Dict = (DDIMParallelScheduler,)
UpperCamelCase_ : List[Any] = (("eta", 0.0), ("num_inference_steps... | 278 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 278 | 1 |
def snake_case_(_UpperCamelCase ) -> int:
"""simple docstring"""
_snake_case = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def snake_case_(_UpperCamelCase = 100 ) -> int:
"""simple docstring"""
_snake_case ... | 278 |
import cmath
import math
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> complex:
"""simple docstring"""
_snake_case = math.radians(_UpperCamelCase )
_snake_case = math.radians(_UpperCamelCase )
# Con... | 278 | 1 |
from __future__ import annotations
__A = []
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
for i in range(len(_UpperCamelCase ) ):
if board[row][i] == 1:
return False
for i in range(len(_UpperC... | 278 |
# 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
#
# Unless required by applica... | 278 | 1 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import ... | 278 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
_snake_case = tau * frequency / samplerate
_snake_case ... | 278 | 1 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common impo... | 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pi... | 278 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM im... | 278 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
_s... | 278 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class lowercase_ ( __lowercase ):
UpperCamelCase_ : Optional[int] = CustomTokenizer
pass
| 278 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependenc... | 278 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 278 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is... | 278 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__A = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__A = ... | 278 |
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=__lowercase ):
UpperCamelCase_ : Optional[int] = ["speech"]
def __init__( self : str , *A__ : List[str] , **A__ : Tuple ) -> Optional[Any]:
requi... | 278 | 1 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTo... | 278 |
from math import factorial
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(_UpperCamelCase ) // (factorial(_UpperCam... | 278 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, lo... | 278 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_snake_case = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
_snake_case ... | 278 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/... | 278 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = ... | 278 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class lowercase_ ( __lowercase ):
UpperCamelCase_ : Tuple = ... | 278 |
__A = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',... | 278 | 1 |
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_with_bytefallback.model''')
@... | 278 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,... | 278 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase_ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase_ : List[Any] = [("size", ctypes.c_int... | 278 |
__A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def snake_case_(_UpperCamelCase ) -> bytes:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
_snake_case = F"""a bytes-like object is required, no... | 278 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 278 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__A = logging.get_logger(__name__)
class lowercase_ ( __lowercase ):
def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : ... | 278 | 1 |
import heapq
def snake_case_(_UpperCamelCase ) -> set[int]:
"""simple docstring"""
_snake_case = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works wi... | 278 |
from __future__ import annotations
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = get_failure_array(_UpperCamelCase )
# 2) Step through text searching for pattern
_snake_case, _snake_case = 0, 0 ... | 278 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
... | 278 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase_ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase_ : List[Any] = [("size", ctypes.c_int... | 278 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 278 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
i... | 278 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def snake_case_() -> Any:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIM... | 278 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.t... | 278 | 1 |
from __future__ import annotations
__A = [True] * 1_00_00_01
__A = 2
while i * i <= 1_00_00_00:
if seive[i]:
for j in range(i * i, 1_00_00_01, i):
__A = False
i += 1
def snake_case_(_UpperCamelCase ) -> bool:
"""simple docstring"""
return seiv... | 278 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 278 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipel... | 278 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 278 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteSch... | 278 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 278 | 1 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''nielsr/canine-s''': 20_48,
}
# Unicode defines 1,114,112 total “codepoints”
__A = 1_11_41_12
... | 278 |
import cmath
import math
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> complex:
"""simple docstring"""
_snake_case = math.radians(_UpperCamelCase )
_snake_case = math.radians(_UpperCamelCase )
# Con... | 278 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowercase_ ( unittest.TestCase ):
def UpperCamelCase_ ( self : Optional[Any] ) -> List[str]:
debug_launcher(test_script.m... | 278 |
# 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
#
# Unless required by applica... | 278 | 1 |
def snake_case_(_UpperCamelCase = 1_000_000 ) -> int:
"""simple docstring"""
_snake_case = 1
_snake_case = 1
_snake_case = {1: 1}
for inputa in range(2 , _UpperCamelCase ):
_snake_case = 0
_snake_case = inputa
... | 278 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
_snake_case = tau * frequency / samplerate
_snake_case ... | 278 | 1 |
def snake_case_(_UpperCamelCase ) -> int:
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def snake_case_(_UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = 0
_snake_case = ... | 278 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pi... | 278 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__A = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_... | 278 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Optional[Any]:
"""simple docstring"""
_s... | 278 | 1 |
import numpy as np
def snake_case_(_UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def snake_case_(_UpperCamelCase ) -> np.ndarray:
"""simple docstring"""
return vector * sigmoid(_UpperCamelCase )
i... | 278 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependenc... | 278 | 1 |
from __future__ import annotations
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = get_failure_array(_UpperCamelCase )
# 2) Step through text searching for pattern
_snake_case, _snake_case = 0, 0 ... | 278 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is... | 278 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__A = 4
__A = 3
class lowercase_ ( __lowercase ):
pass
def snake_case_(_... | 278 |
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=__lowercase ):
UpperCamelCase_ : Optional[int] = ["speech"]
def __init__( self : str , *A__ : List[str] , **A__ : Tuple ) -> Optional[Any]:
requi... | 278 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__A = logging.get_logger(__name__)
class lowercase_ ( __lowercase ):
def __init__( self : List[Any] , *A__ : str , **A__ : Optional[int] ) -> No... | 278 |
from math import factorial
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
return factorial(_UpperCamelCase ) // (factorial(_UpperCam... | 278 | 1 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple docstring"""
return abs(_UpperCamelCase ) if a == 0 else greatest_common_divisor(b % a , _UpperCamelCase )
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> int:
"""simple ... | 278 |
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_snake_case = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
_snake_case ... | 278 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,... | 278 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = ... | 278 | 1 |
# 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
#
# Unless required by applica... | 278 |
__A = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',... | 278 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__A = logging.get_logger(__name__)
class lowercase_ ( __lowercase ):
def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : ... | 278 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,... | 278 | 1 |
def snake_case_(_UpperCamelCase = 600_851_475_143 ) -> int:
"""simple docstring"""
try:
_snake_case = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError(... | 278 |
__A = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def snake_case_(_UpperCamelCase ) -> bytes:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
_snake_case = F"""a bytes-like object is required, no... | 278 | 1 |
def snake_case_(_UpperCamelCase ) -> bool:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(_UpperCamelCase ) == 0:
raise ValueError('''Input list mus... | 278 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__A = logging.get_logger(__name__)
class lowercase_ ( __lowercase ):
def __init__( self : Optional[Any] , *A__ : List[Any] , **A__ : ... | 278 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
... | 278 |
from __future__ import annotations
def snake_case_(_UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
_snake_case = get_failure_array(_UpperCamelCase )
# 2) Step through text searching for pattern
_snake_case, _snake_case = 0, 0 ... | 278 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsMo... | 278 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowercase_ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase_ : List[Any] = [("size", ctypes.c_int... | 278 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A = '''src/transformers'''
# This is to make sure the transformers mo... | 278 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
i... | 278 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
_snake_case = tau * frequency / samplerate
_snake_case ... | 278 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__A = logging.get_logger(__name__)
__A = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.t... | 278 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
@proper... | 278 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 278 | 1 |
def snake_case_(_UpperCamelCase ) -> str:
"""simple docstring"""
_snake_case = 1
_snake_case = 2
while i * i <= n:
_snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > ... | 278 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 278 | 1 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@re... | 278 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 278 | 1 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers i... | 278 |
import cmath
import math
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> complex:
"""simple docstring"""
_snake_case = math.radians(_UpperCamelCase )
_snake_case = math.radians(_UpperCamelCase )
# Con... | 278 | 1 |
def snake_case_(_UpperCamelCase = 600_851_475_143 ) -> int:
"""simple docstring"""
try:
_snake_case = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise ValueError(... | 278 |
# 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
#
# Unless required by applica... | 278 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 , _UpperCamelCase = 1 , _UpperCamelCase = 1.0E4 , _UpperCamelCase = False , _UpperCamelCase = 1.0 , ) -> jnp.ndarray:
"""simple docstring"""
... | 278 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def snake_case_(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
_snake_case = tau * frequency / samplerate
_snake_case ... | 278 | 1 |
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