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 |
|---|---|---|---|---|
"""simple docstring"""
def _snake_case ( UpperCamelCase : int = 4000000 ):
UpperCAmelCase : Optional[int] = [0, 1]
UpperCAmelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
UpperCAmelCase ... | 360 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 76 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list[int] , UpperCamelCase : list[int] ):
# Check if the input is valid
if not len(UpperCamelCase ) == len(UpperCamelCase ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0... | 361 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAK... | 76 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 362 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 0 |
"""simple docstring"""
A: int = {str(digit): digit**5 for digit in range(1_0)}
def _snake_case ( UpperCamelCase : int ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase ) )
def _snake_case ( ):
return sum(
number
f... | 363 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
A: Optional[Any] = logging.get_logger(__name__)
def _snake_case ( Up... | 76 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipeline... | 364 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__... | 76 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A: str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if... | 365 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 366 |
"""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_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils impo... | 367 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 76 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.util... | 368 |
"""simple docstring"""
import baseaa
def _snake_case ( UpperCamelCase : str ):
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def _snake_case ( UpperCamelCase : bytes ):
return baseaa.aaadecode(UpperCamelCase ).decode("""utf-8""" )
i... | 76 | 0 |
"""simple docstring"""
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import lo... | 369 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ):
UpperCAmelCase : Any = HfArgumentParser(UpperCamelCase )
UpperCAmelCase : Tuple = parser.parse_args_into_dataclasses()[0]
... | 370 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import loggi... | 76 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 371 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
'''simple docstring'''
UpperCAmelCase : Node | None = None
UpperCAmelCase ... | 76 | 0 |
"""simple docstring"""
import os
def _snake_case ( ):
with open(os.path.dirname(UpperCamelCase ) + """/p022_names.txt""" ) as file:
UpperCAmelCase : str = str(file.readlines()[0] )
UpperCAmelCase : Optional[int] = names.replace("""\"""" , """""" ... | 350 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 76 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
A: int = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
... | 351 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 76 | 0 |
"""simple docstring"""
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import versi... | 352 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 76 | 0 |
"""simple docstring"""
import numpy as np
from PIL import Image
def _snake_case ( UpperCamelCase : np.ndarray , UpperCamelCase : int , UpperCamelCase : int ):
UpperCAmelCase : int = np.array(UpperCamelCase )
if arr.shape[0] != arr.shape[1]:
rais... | 353 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : dict ):
UpperCAmelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase : set[int] = set()
return any(
node not in visited and depth_f... | 76 | 0 |
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the same n-dimensional space"... | 354 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 76 | 0 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 355 |
"""simple docstring"""
# 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
... | 76 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditional... | 356 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number | (1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number & ~(1 << position)
def _snake_case ... | 76 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list ):
UpperCAmelCase : Any = len(UpperCamelCase )
for i in range(1 , UpperCamelCase ):
UpperCAmelCase : Dict = collection[i]
UpperCAmelCase : Optional[int] = 0
UpperCAmelC... | 357 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 76 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling... | 358 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A: List[Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__lowerCAmelCase : Dict = 'all_... | 76 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A: List[str] = {
"configuration_roberta": ["ROBERTA_PRETRAIN... | 359 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the sa... | 76 | 0 |
"""simple docstring"""
from __future__ import annotations
A: Optional[int] = list[tuple[int, int]]
A: str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0... | 360 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 76 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 361 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAK... | 76 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return int(input_a == input_a == 0 )
def _snake_case ( ):
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(F"... | 362 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A: Optional[Any] = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_A... | 363 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
A: Optional[Any] = logging.get_logger(__name__)
def _snake_case ( Up... | 76 | 0 |
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ :
def __init__( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Dict = 0
UpperCAmelCase : Any = 0
UpperCAmelCase : Tuple = {}
def SCREAMING_SNAKE_CASE ( ... | 364 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__... | 76 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A: Any = {
"configuration_roformer": ["ROFORMER_PRETRAINED_C... | 365 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configura... | 366 |
"""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_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 | 0 |
"""simple docstring"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A: ... | 367 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 76 | 0 |
"""simple docstring"""
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 368 |
"""simple docstring"""
import baseaa
def _snake_case ( UpperCamelCase : str ):
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def _snake_case ( UpperCamelCase : bytes ):
return baseaa.aaadecode(UpperCamelCase ).decode("""utf-8""" )
i... | 76 | 0 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
... | 369 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 0 |
"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__... | 370 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import loggi... | 76 | 0 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
A = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def _snake_case ( ):
UpperCAmelCase : int = ... | 371 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
'''simple docstring'''
UpperCAmelCase : Node | None = None
UpperCAmelCase ... | 76 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 350 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 76 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : dict ):
UpperCAmelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase : set[int] = set()
return any(
node not in visited a... | 351 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 76 | 0 |
"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case ( UpperCamelCase : bytes , UpperCamelCase : int ):
UpperCAmelCase : int = F"{sampling_rate}"
UpperCAmelCase :... | 352 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 76 | 0 |
"""simple docstring"""
from math import factorial
A: dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def _snake_case ( UpperCamelCase : int ):
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError("""Parameter number m... | 353 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : dict ):
UpperCAmelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase : set[int] = set()
return any(
node not in visited and depth_f... | 76 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType... | 354 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 76 | 0 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelC... | 355 |
"""simple docstring"""
# 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
... | 76 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int = 100 ):
UpperCAmelCase : Optional[Any] = set()
UpperCAmelCase : Union[str, Any] = 0
UpperCAmelCase : List[Any] = n + 1 # maximum limit
for a in range(2 , UpperCamelCase ):
... | 356 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number | (1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number & ~(1 << position)
def _snake_case ... | 76 | 0 |
"""simple docstring"""
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
A: Dict = logging.get_logger(__name__)
A: Optional[in... | 357 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 76 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _snake_case ( UpperCamelCase : Optional[... | 358 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A: List[Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__lowerCAmelCase : Dict = 'all_... | 76 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int ):
UpperCAmelCase : Tuple = int(UpperCamelCase )
if n_element < 1:
UpperCAmelCase : Tuple = ValueError("""a should be a positive number""" )
raise my_error
UpperCAmelCase : Tuple ... | 359 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the sa... | 76 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : List[Any] = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE ( self , ... | 360 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 76 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffusion... | 361 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAK... | 76 | 0 |
"""simple docstring"""
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A: Dict = abspath(join(dirname(dirname(__f... | 362 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 ... | 363 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
A: Optional[Any] = logging.get_logger(__name__)
def _snake_case ( Up... | 76 | 0 |
"""simple docstring"""
from functools import lru_cache
def _snake_case ( UpperCamelCase : int ):
UpperCAmelCase : Tuple = 2
UpperCAmelCase : Dict = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(UpperCamelCase )
if n > 1:
... | 364 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__... | 76 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return int((input_a, input_a).count(1 ) != 0 )
def _snake_case ( ):
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1... | 365 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 366 |
"""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_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 | 0 |
"""simple docstring"""
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 367 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 76 | 0 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _lowerCAmelCase ( ):
raise RuntimeError("""CUDA out of memory.""" )
class SCREAMING... | 368 |
"""simple docstring"""
import baseaa
def _snake_case ( UpperCamelCase : str ):
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def _snake_case ( UpperCamelCase : bytes ):
return baseaa.aaadecode(UpperCamelCase ).decode("""utf-8""" )
i... | 76 | 0 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Any = ... | 369 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 0 |
"""simple docstring"""
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # 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 six # ... | 370 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import loggi... | 76 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenize... | 371 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
'''simple docstring'''
UpperCAmelCase : Node | None = None
UpperCAmelCase ... | 76 | 0 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
A: Union[str, Any] = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( folder_based_builder.Fol... | 350 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 76 | 0 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A: List[Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__lowerCAmelCase : Dic... | 351 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 76 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCamelCase : tuple[int, int] , UpperCamelCase : int ):
UpperCAmelCase : Optional[int] = position
UpperCAmelCase : str = [
(y + 1, x + 2),
(y - 1, x + 2),
(y ... | 352 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pi... | 76 | 0 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib ... | 353 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : dict ):
UpperCAmelCase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase : set[int] = set()
return any(
node not in visited and depth_f... | 76 | 0 |
import collections
import os
import re
from pathlib import Path
A: Optional[int] = "src/transformers"
# Matches is_xxx_available()
A: int = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
A: int = re.compile(R"^_import_st... | 354 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Optional[int] ):
UpperCAmelCase : List[Any] = getattr(UpperCamelCase , """handle_key""" ,... | 76 | 0 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( UpperCamelCase : Dict , ... | 355 |
"""simple docstring"""
# 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
... | 76 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A: Any = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__( self , *_SCREAMING_SNAKE_CASE , **_S... | 356 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number | (1 << position)
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int ):
return number & ~(1 << position)
def _snake_case ... | 76 | 0 |
"""simple docstring"""
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_image_inputs
if is_torch_avai... | 357 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 76 | 0 |
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A: Dict = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Aro... | 358 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A: List[Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__lowerCAmelCase : Dict = 'all_... | 76 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCamelCase : str , UpperCamelCase : str ):
UpperCAmelCase : Union[str, Any] = get_failure_array(UpperCamelCase )
# 2) Step through text searching for pattern
UpperCAmelCase ... | 359 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the sa... | 76 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: Tuple = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
... | 360 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( UpperCamelCase : list[list[float]] ):
UpperCAmelCase : int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 76 | 0 |
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A: Tuple... | 361 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAK... | 76 | 0 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
A: Optional[Any] = version.... | 362 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCamelCase : str ):
UpperCAmelCase : Dict = """"""
try:
with open(UpperCamelCase , """rb""" ) as binary_file:
UpperCAmelCase : str = binary_file.read()
for dat in data:
UpperC... | 76 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _snake_case ( UpperCamelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are n... | 363 |
"""simple docstring"""
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
A: Optional[Any] = logging.get_logger(__name__)
def _snake_case ( Up... | 76 | 0 |
"""simple docstring"""
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstr... | 364 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
def __init__... | 76 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 365 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _snake_case ( UpperCamelCase : np.ndarray ):
return input_array.reshape((input_array.size, 1) )
def _snake_cas... | 76 | 0 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils imp... | 366 |
"""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_convbert import ConvBertTokenizer
A: int = logging.get_logger(__name__)
... | 76 | 0 |
"""simple docstring"""
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
A: List[Any] ... | 367 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 76 | 0 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
A: int = HUGGINGFACE_HUB_CACHE
A: Optional[Any] = "config.json"
A: Union[str, Any] = "diffusion_pytorch_model.bin"
A: List[str] = "... | 368 |
"""simple docstring"""
import baseaa
def _snake_case ( UpperCamelCase : str ):
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def _snake_case ( UpperCamelCase : bytes ):
return baseaa.aaadecode(UpperCamelCase ).decode("""utf-8""" )
i... | 76 | 0 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
... | 369 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _snake_case ( UpperCamelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(UpperCamelCase ):
UpperCAmelCase : List[Any] = [d for d in dir_names if d != """scripts""" and d[0] not in... | 76 | 0 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 370 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import loggi... | 76 | 0 |
"""simple docstring"""
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_... | 371 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , _SCREAMING_SNAKE_CASE = 6 ) -> None:
'''simple docstring'''
UpperCAmelCase : Node | None = None
UpperCAmelCase ... | 76 | 0 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase =generate_large_matrix()
UpperCAmelCase =(
[[4, 3, 2, -1], [3, ... | 77 | 1 |
"""simple docstring"""
import math
class lowerCamelCase__ :
'''simple docstring'''
def UpperCamelCase__ ( self ,lowerCamelCase_ ,lowerCamelCase_ ) -> int:
A = 0.0
A = 0.0
for i in range(l... | 77 |
"""simple docstring"""
import pytest
UpperCAmelCase ="__dummy_dataset1__"
UpperCAmelCase ="\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \... | 77 | 1 |
"""simple docstring"""
UpperCAmelCase =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( _a : Dict , _a : Optional[int] , _a : List[str] ... | 77 |
"""simple docstring"""
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Any:
A = 0
A = 0
A = {}
def UpperCamelCase__ ( self ,lowerCamelCase_ ) ... | 77 | 1 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = (DDPMScheduler,)
def UpperCamelCase__... | 77 |
"""simple docstring"""
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a ) + 1
A = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
... | 77 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = (IPNDMScheduler,)
_lowerC... | 77 |
"""simple docstring"""
from math import factorial
def _A ( _a : int = 1_0_0 ):
"""simple docstring"""
return sum(map(_a , str(factorial(_a ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the N... | 77 | 1 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class lowerCamelCase__ ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def UpperCamelCase__ ( lowerCamelCase_ ) -> str:
A = ... | 77 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 77 | 1 |
"""simple docstring"""
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
... | 77 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _A ( _a : Callable , _a : float , _a : float , _a : float , _a : float ):
"""simple docstring"""
A = int... | 77 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 77 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from .... | 77 | 1 |
"""simple docstring"""
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase__ ( SCREAMING_SNAKE_C... | 77 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL im... | 77 | 1 |
"""simple docstring"""
from math import sqrt
def _A ( _a : int ):
"""simple docstring"""
assert isinstance(_a , _a ) and (
number >= 0
), "'number' must been an int and positive"
A = True
... | 77 |
"""simple docstring"""
import torch
from transformers import AutoModel
class lowerCamelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_="sayef/fsner-bert-base-uncased" ) -> Tuple:
super(lowerCamelCase_ ... | 77 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
fro... | 77 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 | 1 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : list[int] ): # This function is recursive
"""simple docstring"""
A = len(_a )
# If the array contains only one element, we return it (it's the s... | 77 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms... | 77 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.gene... | 77 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"distilbert-base-unca... | 77 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
UpperCAmelCase =logging.get_logger(__name__)
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( s... | 77 |
"""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 lowerCamelCase__ ( SC... | 77 | 1 |
"""simple docstring"""
def _A ( _a : int = 1_0_0_0 ):
"""simple docstring"""
A = 2**power
A = str(_a )
A = list(_a )
A = 0
for i in list_num:... | 77 |
"""simple docstring"""
def _A ( _a : Optional[int] ):
"""simple docstring"""
A = []
A = set({"""(""", """[""", """{"""} )
A = set({""")""", """]""", """}"""} )
A ... | 77 | 1 |
"""simple docstring"""
from math import loga
def _A ( _a : int ):
"""simple docstring"""
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(_a , _a ):
raise Ty... | 77 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase ="\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {... | 77 | 1 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pip... | 77 |
"""simple docstring"""
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
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ... | 77 | 1 |
"""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... | 77 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _A ( _a : Callable[[int | float], int | float] , _a : int | float , _a : int | float , _a : int = 1_0_0 , ):
"""simple docs... | 77 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCAmelCase =50_000
UpperCAmelCase =5_000
UpperCAmelCase , UpperCAmelCase =os.path.split(__file__)
UpperCAmelCase =os.path.join(RESULTS... | 77 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def _A ( _a : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
return quad(_a , 0 ... | 77 | 1 |
"""simple docstring"""
def _A ( _a : int ):
"""simple docstring"""
A = [[0 for _ in range(_a )] for _ in range(m + 1 )]
for i in range(m + 1 ):
A = 1
for n in range(m + 1 ... | 77 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
... | 77 | 1 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
UpperCAmelCase ="src/transformers"
# Matches is_xxx_available()
UpperCAmelCase =re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase =re.compile(R... | 77 |
"""simple docstring"""
def _A ( _a : int ):
"""simple docstring"""
A = abs(_a )
A = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _A ( _a... | 77 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name... | 77 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase =argparse.ArgumentParser()
parser.add_argument("--dump_path", de... | 77 | 1 |
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