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 |
|---|---|---|---|---|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__snake_case = logging.get_logger(__name__)
__snake_case = ... | 358 | from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''google/efficient... | 78 | 0 |
from collections import deque
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
UpperCAmelCase : Tuple =len(__lowerCAmelCase )
UpperCAmelCase : str =deque()
UpperCAmelCase : List[str] =[False for _ in ... | 359 | 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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_... | 78 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase )-> None:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =generate_pascal_triangle(__lowerCAmelCase )
for row_idx in range(__lowerCAmelCase ):
# Print left spaces
for _ in range(num_rows - row... | 360 | import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 78 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
__snake_case = 1 # (0 is vertical, 1 is horizontal)
def lowerCAmelCase_ ( )-> None:
'''sim... | 361 | import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.t... | 78 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> tuple[float, list[float]]:
'''simple docstring'''
UpperCAmelCase : int =list(range(len(__lowerCAmelCase ) ) )
UpperCAmelCase ... | 362 | 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... | 78 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> list[list[int]]:
'''simple docstring'''
UpperCAmelCase : list[list[int]] =[]
create_all_state(1 , __lowerCAmelCase , __lowerCAmelCase , ... | 363 | import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diff... | 78 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_log... | 364 | import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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
__snake_case ... | 78 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0 , __lowerCAmelCase = 0 )-> int:
UpperCAmelCase : Tuple =right or len(__lowerCAmelCase ) - 1
if left > right:
return -1
elif list_data[left] == key:
... | 365 | from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Dict = ["""torch"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
... | 78 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__snake_case = {
'''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''... | 366 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],... | 78 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):... | 367 | import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __snake_case ( lowerCamelCase__ ):
@require_torch
def UpperCAmelCase__ ( self ) -> List[str]:
'... | 78 | 0 |
import numpy
# List of input, output pairs
__snake_case = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__snake_case = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
__snake_case = [2, 4, 1, 5]
__snake_case = le... | 368 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://h... | 78 | 0 |
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 I... | 369 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__lowerCamelCase : Union[str, Any]... | 78 | 0 |
from manim import *
class __snake_case ( lowerCamelCase__ ):
def UpperCAmelCase__ ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple =Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase : Any =Rectangle... | 370 | from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase_ ( __lowerCAmelCase = "isbn/0140328726" )-> dict:
'''simple docstring'''
UpperCAmelCase : Tuple =olid.strip().strip('''/''' ) # Remove leadi... | 78 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion... | 371 | from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...ut... | 78 | 0 |
from __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 350 | from __future__ import annotations
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> list[list[int]]:
'''simple docstring'''
UpperCAmelCase : list[list[int]] =[]
create_all_state(1 , __lowerCAmelCase , __lowerCAmelCase , ... | 78 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> str:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__lowerCAmelCase , int(b / 2 ) ) * actual_power(__lowerCAmelCase , int(b / 2 ) )
... | 351 | import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_ut... | 78 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCAmelCase : Union[str, Any] =len(bin(__lowerCAmelCase )[3:] )
UpperCAmelCase : Any ... | 352 | import sys
def lowerCAmelCase_ ( __lowerCAmelCase )-> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =len(__lowerCAmelCase )
UpperCAmelCase : List[str] =[[0 for x in range(__lowerCAmelCase )] for x in range(__lowerCAmelCase )]
... | 78 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTRL... | 353 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.json'''}
__snake_case = {
'''vocab_file''': {
... | 78 | 0 |
import math
class __snake_case :
def UpperCAmelCase__ ( self , snake_case__ , snake_case__ ) -> int:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] =0.0
UpperCAmelCase : Tuple =0.0
for i in range... | 354 | import colorsys
from PIL import Image # type: ignore
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> float:
'''simple docstring'''
UpperCAmelCase : Any =x
UpperCAmelCase : List[str] =y
for ... | 78 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class ... | 355 | class __snake_case :
def __init__( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =n
UpperCAmelCase : Any =[None] * self.n
UpperCAmelCase : Tuple =0 # index of the first e... | 78 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diff... | 356 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise OptionalDepende... | 78 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> bool:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__lowerCAmelCase ) )
def lowerCAmel... | 357 | from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
'''... | 78 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = '''▁'''
__snake_case ... | 358 | from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''google/efficient... | 78 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__snake_case = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
raise OptionalDependency... | 359 | 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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_... | 78 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase )-> list:
'''simple docstring'''
if len(__lowerCAmelCase ) <= 1:
return [tuple(__lowerCAmelCase )]
UpperCAmelCase : Optional[int] =[]
def generate(__lowerCAmelCase , __lowerCAmelCase ):
... | 360 | import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 78 | 0 |
__snake_case = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def lowerCAmelCase_ ( )-> None:
'''simple docstring'''
UpperCAmelCase : Optional[int] =input('''Enter message: ''' )
UpperCAmelCase : int =input('''Enter key [alphanumeric]: ''' )
... | 361 | import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.t... | 78 | 0 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils imp... | 362 | 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... | 78 | 0 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( __lowerCAmelCase = "https://www.worldometers.info/coronavirus" )-> dict:
'''simple docstring'''
UpperCAmelCase : List[str] =BeautifulSoup(requests.get(__lowerCAmelCase ).text , '''html.pars... | 363 | import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diff... | 78 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__snake_case = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__snake_case = [ord(letter) for letter in string.ascii_lowercase]
__snake_case = ... | 364 | import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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
__snake_case ... | 78 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
s... | 365 | from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Dict = ["""torch"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
... | 78 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}
class __snake_case ( low... | 366 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],... | 78 | 0 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def lowerCAmelCase_ ( __lower... | 367 | import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __snake_case ( lowerCamelCase__ ):
@require_torch
def UpperCAmelCase__ ( self ) -> List[str]:
'... | 78 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ... | 368 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://h... | 78 | 0 |
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 AutoToke... | 369 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__lowerCamelCase : Union[str, Any]... | 78 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __snake_case ( lowerCamelCase__ )... | 370 | from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase_ ( __lowerCAmelCase = "isbn/0140328726" )-> dict:
'''simple docstring'''
UpperCAmelCase : Tuple =olid.strip().strip('''/''' ) # Remove leadi... | 78 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase = 10_00 )-> int:
'''simple docstring'''
UpperCAmelCase : Optional[int] =1, 1
UpperCAmelCase : Union[str, Any] =2
while True:
UpperCAmelCase : Any =0
... | 371 | from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...ut... | 78 | 0 |
import argparse
import json
import subprocess
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =[]
UpperCAmelCase : Union[str, Any] =(
f'''... | 350 | from __future__ import annotations
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> list[list[int]]:
'''simple docstring'''
UpperCAmelCase : list[list[int]] =[]
create_all_state(1 , __lowerCAmelCase , __lowerCAmelCase , ... | 78 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = '''▁'''
__snake_case = {'''vocab_file''': '''spiece.model'''}
__snake_case ... | 351 | import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_ut... | 78 | 0 |
from PIL import Image
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> Image:
'''simple docstring'''
UpperCAmelCase : Optional[int] =(2_59 * (level + 2_55)) / (2_55 * (2_59 - level))
def contrast(__lowerCAmelCase ) -> int:
re... | 352 | import sys
def lowerCAmelCase_ ( __lowerCAmelCase )-> Any:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =len(__lowerCAmelCase )
UpperCAmelCase : List[str] =[[0 for x in range(__lowerCAmelCase )] for x in range(__lowerCAmelCase )]
... | 78 | 0 |
from pathlib import Path
import numpy as np
from PIL import Image
def lowerCAmelCase_ ( __lowerCAmelCase )-> np.ndarray:
'''simple docstring'''
UpperCAmelCase : List[Any] =rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
... | 353 | import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {'''vocab_file''': '''vocab.json'''}
__snake_case = {
'''vocab_file''': {
... | 78 | 0 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCAmelCase_ ( __lowerCAmelCase = 8 )-> str:
'''simple docstring'''
UpperCAmelCase : str =ascii_letters + digits + punctuati... | 354 | import colorsys
from PIL import Image # type: ignore
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> float:
'''simple docstring'''
UpperCAmelCase : Any =x
UpperCAmelCase : List[str] =y
for ... | 78 | 0 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Dict = ["""torch"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
... | 355 | class __snake_case :
def __init__( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =n
UpperCAmelCase : Any =[None] * self.n
UpperCAmelCase : Tuple =0 # index of the first e... | 78 | 0 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 356 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise OptionalDepende... | 78 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],... | 357 | from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case = [
'''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
'''... | 78 | 0 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...... | 358 | from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''google/efficient... | 78 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : List[str] = (DDIMParallelScheduler,)
__lowerCamelCase : Dict = (("""eta""", 0.0), ("""num_infe... | 359 | 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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_... | 78 | 0 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoF... | 360 | import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 78 | 0 |
from collections import deque
class __snake_case :
def __init__( self , snake_case__ , snake_case__ , snake_case__ ) -> None:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =process_name # process name
UpperCAmelCas... | 361 | import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.t... | 78 | 0 |
class __snake_case :
def __init__( self , snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Tuple =n
UpperCAmelCase : Any =[None] * self.n
UpperCAmelCase : Tuple =0 # index of the first e... | 362 | 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... | 78 | 0 |
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> np.array:
'''simple docstring'''
UpperCAmelCase : Optional[int] ... | 363 | import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diff... | 78 | 0 |
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 PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = ... | 364 | import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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
__snake_case ... | 78 | 0 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...t... | 365 | from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=lowerCamelCase__ ):
__lowerCamelCase : Dict = ["""torch"""]
def __init__( self , *snake_case__ , **snake_case__ ) -> Union[str, Any]:
'''simple docstring'''
... | 78 | 0 |
"""simple docstring"""
import argparse
import json
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
fr... | 366 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],... | 78 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __snake_case ( lowerCamelCase__ , ... | 367 | import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __snake_case ( lowerCamelCase__ ):
@require_torch
def UpperCAmelCase__ ( self ) -> List[str]:
'... | 78 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCAmelCase_ ( __lowerCAmelCase )-> str:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError('''Undefined for non-integers''' )
e... | 368 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://h... | 78 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , )-> tuple:
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('''You cannot supply... | 369 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__lowerCamelCase : Union[str, Any]... | 78 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtracti... | 370 | from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase_ ( __lowerCAmelCase = "isbn/0140328726" )-> dict:
'''simple docstring'''
UpperCAmelCase : Tuple =olid.strip().strip('''/''' ) # Remove leadi... | 78 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase = " " )-> list:
'''simple docstring'''
UpperCAmelCase : List[str] =[]
UpperCAmelCase : List[str] =0
for index, char in enumerate(__lowerCAmelCase ):
... | 371 | from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...ut... | 78 | 0 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_conf... | 79 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : str , _lowercase : bool = False ) ->str:
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
a : List[Any] = F"""Expected string as input, found {ty... | 79 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noq... | 79 | 1 |
"""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 : List[str] = get_tes... | 79 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_f... | 79 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
... | 79 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.util... | 79 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...sched... | 79 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : List[str] = logging.get_logger(__name__)
a ... | 79 | 1 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def _SCREAMING_SNAKE_CASE ( _lowercase : list[int] , _lowercase : list[int] , _lowercase : int ) ->list[int]:
'''simple docstring'''
a : ... | 79 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowercase : list[list[int]] ) ->int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessi... | 79 | 1 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
fro... | 79 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Any = {'''configuration_fnet''': ['''FNET... | 79 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... | 79 | 1 |
"""simple docstring"""
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 __UpperCamel... | 79 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _SCREAMING_SNAKE_CASE ( ) ->Optional[Any]:
'''simple docstring'''
a : int = HfArgumentParser(_lowercase )
a : ... | 79 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtrac... | 79 |
"""simple docstring"""
from datetime import datetime
import requests
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->bytes:
'''simple docstring'''
a : Optional[Any] = "https://downloadgram.net/wp-json/wppress/video-downloader... | 79 | 1 |
"""simple docstring"""
import warnings
from .generation import TFGenerationMixin
class __UpperCamelCase ( a__ ):
# warning at import time
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated an... | 79 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a : List[str] = {
'''configuration_perceiver'''... | 79 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
a : str = 3
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->int:
'''simple docstring'''
... | 79 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a__ )
class __UpperCamelCase ( a__ ):
# `task` is not a ClassVar since we w... | 79 | 1 |
"""simple docstring"""
import os
import re
import warnings
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():
fr... | 79 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 50 ) ->int:
'''simple docstring'''
a : Any = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for ... | 79 | 1 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import Ten... | 79 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
a : Optional[int] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/m... | 79 | 1 |
"""simple docstring"""
a : Optional[int] = '''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
a : List[Any] = [{'''typ... | 79 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float , _lowercase : float ) ->dict[str, float]:
'''simple docstring'''
... | 79 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, r... | 79 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Dict = logging.get_logger(__name__)
a : List[Any] = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/mai... | 79 | 1 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _SCREAMING_SNAKE_CASE ( _lowercase : Any ) ->Dict:
'''simple docstring'''
a ... | 79 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
a : Tuple = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "... | 79 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( ... | 79 |
"""simple docstring"""
a : Optional[int] = 8.31_4462 # Unit - J mol-1 K-1
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float , _lowercase : float ) ->float:
'''simple docstring'''
if ... | 79 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
a : Tuple = logging.get_logger(__name__)
class __UpperCamelCase ( a__ ):
def __init__( sel... | 79 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_... | 79 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : float ) ->float:
'''simple docstring'''
if edge <= 0 or not isinstance(_lowercase , _lowercase ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2)... | 79 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _SCREAMING_SNAKE_... | 79 | 1 |
"""simple docstring"""
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __UpperCamelCase ... | 79 |
"""simple docstring"""
import math
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 100 ) ->int:
'''simple docstring'''
a : Dict = sum(i * i for i in range(1 , n + 1 ) )
a : Tuple = int(math.pow(sum(range(1 , n + 1... | 79 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers impo... | 79 |
"""simple docstring"""
from itertools import product
def _SCREAMING_SNAKE_CASE ( _lowercase : int , _lowercase : int ) ->list[int]:
'''simple docstring'''
a : Dict = sides_number
a : List[str] = max_face_num... | 79 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a : List[str] = logging.get_logger(__name__)
class __UpperCamelCase ( a__ ):
... | 79 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 | 1 |
"""simple docstring"""
from typing import List
import numpy as np
def _SCREAMING_SNAKE_CASE ( _lowercase : dict ) ->int:
'''simple docstring'''
a : str = {key: len(_lowercase ) for key, value in gen_kwargs.items() if isinstanc... | 79 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noq... | 79 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testin... | 79 |
"""simple docstring"""
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_f... | 79 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : list ) ->int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
... | 79 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.util... | 79 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : int = {
'''configuration_funnel''': ['''FUNNEL_... | 79 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : List[str] = logging.get_logger(__name__)
a ... | 79 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a : Any = None
try:
import msvcrt
except ImportError:
a : Any = None
try:
import fcntl
except ImportError:
... | 79 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowercase : list[list[int]] ) ->int:
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessi... | 79 | 1 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowercase : list[int] , _lowercase : int ) ->list[list[int]]:
'''simple docstring'''
a : list[list[int]] = []
a : list[int] ... | 79 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Any = {'''configuration_fnet''': ['''FNET... | 79 | 1 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __Uppe... | 79 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... | 79 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( a__ ):
lowerCamelCase : Any =(IPNDMScheduler,)
lowerCamelCase : int =(("... | 79 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _SCREAMING_SNAKE_CASE ( ) ->Optional[Any]:
'''simple docstring'''
a : int = HfArgumentParser(_lowercase )
a : ... | 79 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
a : Optional[Any] ... | 79 |
"""simple docstring"""
from datetime import datetime
import requests
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->bytes:
'''simple docstring'''
a : Optional[Any] = "https://downloadgram.net/wp-json/wppress/video-downloader... | 79 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def _SCREAMING_SNAKE_CASE ( _lowercase : list ) ->int:
'''simple docstring'''
if not postfix_notation:
return 0
a : Dict = {"+", "-", "*",... | 79 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a : List[str] = {
'''configuration_perceiver'''... | 79 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def _SCREAMING_SNAKE_CASE ( _lowercase : str , _lowercase : float | Decimal , _lowercase : float = 10**... | 79 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a__ )
class __UpperCamelCase ( a__ ):
# `task` is not a ClassVar since we w... | 79 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effe... | 79 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 50 ) ->int:
'''simple docstring'''
a : Any = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for ... | 79 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 3 , _lowercase : int = 7 , _lowercase : int = 100_0000 ) ->int:
'''simple docstring'''
a : Dict = 0
a : int = 1
for current_denominator i... | 79 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
a : Optional[int] = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/m... | 79 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( _lowercase : int = 100 ) ->int:
'''simple docstring'''
a : Optional[int] = n * (n + 1) * (2 * n + 1) / 6
a : Optional[Any] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - s... | 79 |
"""simple docstring"""
from __future__ import annotations
from math import pow, sqrt
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float , _lowercase : float ) ->dict[str, float]:
'''simple docstring'''
... | 79 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a : List[Any] = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Hu... | 79 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Dict = logging.get_logger(__name__)
a : List[Any] = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/mai... | 79 | 1 |
"""simple docstring"""
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _SCREAMING_SNAKE_CASE ( _lowercase : List[Any] ) ->Any:
'''simple docstring'''
... | 79 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
a : Tuple = '''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "... | 79 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcess... | 79 |
"""simple docstring"""
a : Optional[int] = 8.31_4462 # Unit - J mol-1 K-1
def _SCREAMING_SNAKE_CASE ( _lowercase : float , _lowercase : float , _lowercase : float ) ->float:
'''simple docstring'''
if ... | 79 | 1 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early o... | 79 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_... | 79 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Tuple ... | 79 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _SCREAMING_SNAKE_... | 79 | 1 |
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