code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase__ ( ... | 699 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer,... | 699 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Autoencode... | 699 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMSchedul... | 699 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCAmelCase__ : List[str] = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attenti... | 699 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 699 | 1 |
from math import factorial
lowerCAmelCase__ : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def UpperCamelCase__ ( A__ ) -> int:
if not isinstance(A__ , A__ ):
raise TypeError('Parameter number must be int' )
if number < 0:... | 699 | from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __snake_case :
__lowerCamelCase = field(
metadata={"""help""": ... | 699 | 1 |
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.... | 699 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModel... | 699 | 1 |
import os
def UpperCamelCase__ ( ) -> int:
snake_case__ : Optional[Any] = os.path.join(os.path.dirname(A__ ) , 'num.txt' )
with open(A__ ) as file_hand:
return str(sum(int(A__ ) for line in file_hand ) )[:10]
if __name__ == ... | 699 | from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase__ : List[Any] = datasets.utils.logging.get_logger(__name__)
class __snake_case ( folder_based_builder.FolderBasedBuild... | 699 | 1 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowerCAmelCase__ : List[str] = 20_48
lowerCAmelCase__ : int = 40_96
lowerCAmelCase__ : str = 42
lowerCAmelCase__ : Tuple = os.environ.pop('''PROCESS_TRAIN''', '''false''')
lowerCAmelCase__ : Tuple ... | 699 | import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IM... | 699 | 1 |
# 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 ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate... | 699 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : List[Any] = ... | 699 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tor... | 699 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase__ : List[str] = HfApi()
lowerCAmelCase__ : str = {}
# fmt: off
lowerCAmelCase__ : int = torch.tensor([
-0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1... | 699 | 1 |
class __snake_case :
def __init__( self ) -> List[Any]:
'''simple docstring'''
snake_case__ : Optional[int] = {}
def __a ( self ) -> None:
'''simple docstring'''
... | 699 | import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
def __init__( self , *__UpperCamelCase , **__Up... | 699 | 1 |
from __future__ import annotations
def UpperCamelCase__ ( A__ , A__ , A__ , A__ ) -> list:
snake_case__ : Dict = []
snake_case__ , snake_case__ : str = input_list[low:mid], input_list[mid : high + 1]
while lef... | 699 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCAmelCase__ : List[Any] = datasets.uti... | 699 | 1 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase__ : List[Any] = datasets.utils.logging.get_logger(__name__)
class __snake_case ( folder_based_builder.FolderBasedBuild... | 699 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Any = {'''configuration_xglm''': [''... | 699 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 699 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCAmelCase__ : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and ... | 699 | 1 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
... | 699 | from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ : Optional[int] = TypeVar('''T''')
class __snake_case ( Generic[T] ):
def __init__( self , __UpperCamelCase ) -> Any:
... | 699 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowerCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
cl... | 699 | 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
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = ... | 699 | 1 |
import numpy as np
import qiskit
def UpperCamelCase__ ( A__ = 8 , A__ = None ) -> str:
snake_case__ : Optional[int] = np.random.default_rng(seed=A__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
sn... | 699 | import numpy as np
import qiskit
def UpperCamelCase__ ( A__ = 8 , A__ = None ) -> str:
snake_case__ : Optional[int] = np.random.default_rng(seed=A__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
sn... | 699 | 1 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.com... | 699 | def UpperCamelCase__ ( A__ , A__ , A__ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case__ : Dict = _modexpt(A__ , exponent // 2 , A__ ) % modulo_value
return (x * x) % modulo_value
... | 699 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowerCAmelCase__ : ... | 699 | # tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
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 c... | 699 | 1 |
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 __snake_case ( _lowerCamelCase ,_lowerCamelCase ):
... | 699 | def UpperCamelCase__ ( A__ ) -> list[int]:
if length <= 0 or not isinstance(A__ , A__ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(A__ )]
if __name__ == "__main__":
print(hexagonal_numbers(lengt... | 699 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 699 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 699 | 1 |
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 .tokenization_camembe... | 699 | from collections import namedtuple
lowerCAmelCase__ : Union[str, Any] = namedtuple('''from_to''', '''from_ to''')
lowerCAmelCase__ : Tuple = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.0_01, 10_00),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.0_04_54... | 699 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IM... | 699 | 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
lowerCAmelCase__ : Tuple = logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, An... | 699 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class __snake_case :
def __init__( self , __UpperCamelCase = None ) -> None:
'''simple docstring'''
... | 699 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer,... | 699 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase__ : Optional[int] = {... | 699 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMSchedul... | 699 | 1 |
from __future__ import annotations
from random import random
class __snake_case :
def __init__( self , __UpperCamelCase = None ) -> Any:
'''simple docstring'''
snake_case__ : Union[str, Any] = value
... | 699 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 699 | 1 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
lowerCAmelCase__ : Optional[int] = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
... | 699 | from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __snake_case :
__lowerCamelCase = field(
metadata={"""help""": ... | 699 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 699 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModel... | 699 | 1 |
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_available():
imp... | 699 | from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase__ : List[Any] = datasets.utils.logging.get_logger(__name__)
class __snake_case ( folder_based_builder.FolderBasedBuild... | 699 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docst... | 699 | import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IM... | 699 | 1 |
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
lowerCAmelCase__ : Tuple = logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, An... | 699 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : List[Any] = ... | 699 | 1 |
def UpperCamelCase__ ( A__ , A__ ) -> str:
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase__ : List[str] = HfApi()
lowerCAmelCase__ : str = {}
# fmt: off
lowerCAmelCase__ : int = torch.tensor([
-0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1... | 699 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : List[Any] = get_tests_dir('''fixt... | 699 | import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
def __init__( self , *__UpperCamelCase , **__Up... | 699 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( _lowerCamelCase ,unittest.TestCase ... | 699 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCAmelCase__ : List[Any] = datasets.uti... | 699 | 1 |
class __snake_case :
def __init__( self ) -> List[str]:
'''simple docstring'''
snake_case__ : str = 0
snake_case__ : int = 0
snake_case__ : int = {}
d... | 699 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Any = {'''configuration_xglm''': [''... | 699 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : int = logging.get_logger(__name__)
lowerCAmelCase__ : Optional[int] = {
'''vocab_file''': '''vocab.json''',
... | 699 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCAmelCase__ : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and ... | 699 | 1 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 699 | from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ : Optional[int] = TypeVar('''T''')
class __snake_case ( Generic[T] ):
def __init__( self , __UpperCamelCase ) -> Any:
... | 699 | 1 |
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_xpu,
requi... | 699 | 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
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = ... | 699 | 1 |
def UpperCamelCase__ ( A__ = 50 ) -> int:
snake_case__ : List[str] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_le... | 699 | import numpy as np
import qiskit
def UpperCamelCase__ ( A__ = 8 , A__ = None ) -> str:
snake_case__ : Optional[int] = np.random.default_rng(seed=A__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
sn... | 699 | 1 |
from __future__ import annotations
def UpperCamelCase__ ( A__ , A__ ) -> set[str]:
snake_case__ , snake_case__ : str = set(A__ ), [start]
while stack:
snake_case__ : str = stack.pop()
explored.add(A__ ... | 699 | def UpperCamelCase__ ( A__ , A__ , A__ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case__ : Dict = _modexpt(A__ , exponent // 2 , A__ ) % modulo_value
return (x * x) % modulo_value
... | 699 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 699 | # tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
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 c... | 699 | 1 |
import random
from typing import Any
def UpperCamelCase__ ( A__ ) -> list[Any]:
for _ in range(len(A__ ) ):
snake_case__ : List[Any] = random.randint(0 , len(A__ ) - 1 )
snake_case__ : Union[str, Any] = rand... | 699 | def UpperCamelCase__ ( A__ ) -> list[int]:
if length <= 0 or not isinstance(A__ , A__ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(A__ )]
if __name__ == "__main__":
print(hexagonal_numbers(lengt... | 699 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowerCAmelCase__ : int = logging.get_logger(__name__)
lowerCAmelCase__ : Any = {
'''post_extract_proj''': '... | 699 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 699 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand... | 699 | from collections import namedtuple
lowerCAmelCase__ : Union[str, Any] = namedtuple('''from_to''', '''from_ to''')
lowerCAmelCase__ : Tuple = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.0_01, 10_00),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.0_04_54... | 699 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = r'''
Args:
input_ids (`to... | 699 | 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
lowerCAmelCase__ : Tuple = logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, An... | 699 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
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 c... | 699 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer,... | 699 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __snake_case ( _lowerCamelCase ,_low... | 699 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMSchedul... | 699 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __snake_case ( unittest.TestCase ):
__lowerCamelCase = JukeboxTokenizer
__lowerCamelCase = {
"""artist""": """Zac Brown Band""",
... | 699 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 699 | 1 |
import operator as op
lowerCAmelCase__ : Optional[int] = '''scaler.pt'''
lowerCAmelCase__ : List[str] = '''pytorch_model'''
lowerCAmelCase__ : Tuple = '''random_states'''
lowerCAmelCase__ : List[str] = '''optimizer'''
lowerCAmelCase__ : int = '''scheduler'''
lowerC... | 699 | from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __snake_case :
__lowerCamelCase = field(
metadata={"""help""": ... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : int = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONF... | 699 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModel... | 699 | 1 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def UpperCamelCase__ ( A__ , A__ , A__ = 10**-10 ) -> float:
snake_case__ : List[Any] = a
while True:
snake_case__ ... | 699 | from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase__ : List[Any] = datasets.utils.logging.get_logger(__name__)
class __snake_case ( folder_based_builder.FolderBasedBuild... | 699 | 1 |
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_common import ids_tensor
if is_torch_av... | 699 | import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IM... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta ... | 699 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : List[Any] = ... | 699 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.ut... | 699 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase__ : List[str] = HfApi()
lowerCAmelCase__ : str = {}
# fmt: off
lowerCAmelCase__ : int = torch.tensor([
-0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1... | 699 | 1 |
from collections.abc import Generator
from math import sin
def UpperCamelCase__ ( A__ ) -> bytes:
if len(A__ ) != 32:
raise ValueError('Input must be of length 32' )
snake_case__ : int = b''
for i in [3, 2, 1, 0]:
little_endian +=... | 699 | import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
def __init__( self , *__UpperCamelCase , **__Up... | 699 | 1 |
import sys
def UpperCamelCase__ ( A__ ) -> List[Any]:
snake_case__ : Optional[Any] = len(A__ )
snake_case__ : List[str] = [[0 for x in range(A__ )] for x in range(A__ )]
snake_case__ : List[str] = [[0 for ... | 699 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCAmelCase__ : List[Any] = datasets.uti... | 699 | 1 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
lowerCAmelCase__ : int = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
def __init__( self , *__UpperCamelCase , **__Upp... | 699 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Any = {'''configuration_xglm''': [''... | 699 | 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_mobilebert import MobileBertTokenizer
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lo... | 699 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCAmelCase__ : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and ... | 699 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ : Optional[int] = TypeVar('''T''')
class __snake_case ( Generic[T] ):
def __init__( self , __UpperCamelCase ) -> Any:
... | 699 | from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ : Optional[int] = TypeVar('''T''')
class __snake_case ( Generic[T] ):
def __init__( self , __UpperCamelCase ) -> Any:
... | 699 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase__ : Optional[Any] = False
class __snake_case ( unittest.TestCase ):
... | 699 | 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
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = ... | 699 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCAmelCase__ : Tuple = logging.get_logger(__name__)
def UpperCamelCase__ ( A__ ) -> Dict:
snake_case__ : ... | 699 | import numpy as np
import qiskit
def UpperCamelCase__ ( A__ = 8 , A__ = None ) -> str:
snake_case__ : Optional[int] = np.random.default_rng(seed=A__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
sn... | 699 | 1 |
def UpperCamelCase__ ( A__ , A__ ) -> str:
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
snake_case__ : List[str] = str(bin(A__ ) )[2:] # remove the leading "0b"
snake_case__ : List[Any] ... | 699 | def UpperCamelCase__ ( A__ , A__ , A__ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case__ : Dict = _modexpt(A__ , exponent // 2 , A__ ) % modulo_value
return (x * x) % modulo_value
... | 699 | 1 |
from cva import destroyAllWindows, imread, imshow, waitKey
def UpperCamelCase__ ( A__ ) -> str:
# getting number of pixels in the image
snake_case__ , snake_case__ : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative... | 699 | # tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
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 c... | 699 | 1 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : List[Any] = ... | 699 | def UpperCamelCase__ ( A__ ) -> list[int]:
if length <= 0 or not isinstance(A__ , A__ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(A__ )]
if __name__ == "__main__":
print(hexagonal_numbers(lengt... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Tuple = {
'''configuration_distilbert''': [
'''DISTILBE... | 699 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 699 | 1 |
import numpy
class __snake_case :
def __init__( self , __UpperCamelCase , __UpperCamelCase ) -> None:
'''simple docstring'''
snake_case__ : Dict = input_array
# Random initial weights are assigne... | 699 | from collections import namedtuple
lowerCAmelCase__ : Union[str, Any] = namedtuple('''from_to''', '''from_ to''')
lowerCAmelCase__ : Tuple = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.0_01, 10_00),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.0_04_54... | 699 | 1 |
from __future__ import annotations
def UpperCamelCase__ ( A__ , A__ ) -> bool:
snake_case__ : Optional[Any] = get_failure_array(A__ )
# 2) Step through text searching for pattern
snake_case__ , snake_case__ : Any = ... | 699 | 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
lowerCAmelCase__ : Tuple = logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, An... | 699 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def UpperCamelCase__ ( A__=None ,... | 699 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer,... | 699 | 1 |
from torch import nn
class __snake_case ( nn.Module ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ) -> List[Any]:
'''simple docstring'''
super().__init__()
snake_case__ : Optional[int] ... | 699 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMSchedul... | 699 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ... | 699 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 699 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers... | 699 | from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __snake_case :
__lowerCamelCase = field(
metadata={"""help""": ... | 699 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase__ : Optional[Any] = '''
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes... | 699 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModel... | 699 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tok... | 699 | from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase__ : List[Any] = datasets.utils.logging.get_logger(__name__)
class __snake_case ( folder_based_builder.FolderBasedBuild... | 699 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowerCAmelCase__ : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
... | 699 | import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IM... | 699 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def UpperCamelCase__ ( A__ , A__ , A__ , A__ = 100 , ) -> float:
snake_case__ : Optional[int] = x_start
snake_case__ : Any = fnc(A__ ... | 699 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : List[Any] = ... | 699 | 1 |
def UpperCamelCase__ ( A__ , A__ ) -> str:
if not (isinstance(A__ , A__ ) and isinstance(A__ , A__ )):
raise ValueError('longest_common_substring() takes two strings for inputs' )
snake_case__ : Optional[int] = len(A__ ... | 699 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase__ : List[str] = HfApi()
lowerCAmelCase__ : str = {}
# fmt: off
lowerCAmelCase__ : int = torch.tensor([
-0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1... | 699 | 1 |
from timeit import timeit
def UpperCamelCase__ ( A__ ) -> int:
if number < 0:
raise ValueError('the value of input must not be negative' )
snake_case__ : Optional[Any] = 0
while number:
number &= number - 1
result += 1
retu... | 699 | import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
def __init__( self , *__UpperCamelCase , **__Up... | 699 | 1 |
def UpperCamelCase__ ( A__ = 1000 ) -> int:
return sum(e for e in range(3 , A__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 699 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCAmelCase__ : List[Any] = datasets.uti... | 699 | 1 |
def UpperCamelCase__ ( A__ ) -> int:
if not numbers:
return 0
if not isinstance(A__ , (list, tuple) ) or not all(
isinstance(A__ , A__ ) for number in numbers ):
raise ValueError('numbers must be an iterable of integers' )
... | 699 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Any = {'''configuration_xglm''': [''... | 699 | 1 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=_lowerCamelCase ):
__lowerCamelCase = ["""sentencepiece"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ) -> Tuple:
'''simple docstri... | 699 | from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCAmelCase__ : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and ... | 699 | 1 |
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_configuration_common import C... | 699 | from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase__ : Optional[int] = TypeVar('''T''')
class __snake_case ( Generic[T] ):
def __init__( self , __UpperCamelCase ) -> Any:
... | 699 | 1 |
from math import isqrt
def UpperCamelCase__ ( A__ ) -> list[int]:
snake_case__ : Optional[Any] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , A__ , A__ ... | 699 | 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
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : int = ... | 699 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
lowerCAmelCase__ : Tuple = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
lowerCAmelCase__ : Li... | 699 | import numpy as np
import qiskit
def UpperCamelCase__ ( A__ = 8 , A__ = None ) -> str:
snake_case__ : Optional[int] = np.random.default_rng(seed=A__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
sn... | 699 | 1 |
def UpperCamelCase__ ( A__ , A__ , A__ ) -> int:
def count_of_possible_combinations(A__ ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in... | 699 | def UpperCamelCase__ ( A__ , A__ , A__ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
snake_case__ : Dict = _modexpt(A__ , exponent // 2 , A__ ) % modulo_value
return (x * x) % modulo_value
... | 699 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_n... | 699 | # tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
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 c... | 699 | 1 |
def UpperCamelCase__ ( A__ ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
snake_case__ : Tuple = gray_code_sequenc... | 699 | def UpperCamelCase__ ( A__ ) -> list[int]:
if length <= 0 or not isinstance(A__ , A__ ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(A__ )]
if __name__ == "__main__":
print(hexagonal_numbers(lengt... | 699 | 1 |
def UpperCamelCase__ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(A__ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'''{solution() = }''')
... | 699 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmen... | 699 | 1 |
from collections.abc import Sequence
from queue import Queue
class __snake_case :
def __init__( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None ) -> List[Any]:
'''simple docstri... | 699 | from collections import namedtuple
lowerCAmelCase__ : Union[str, Any] = namedtuple('''from_to''', '''from_ to''')
lowerCAmelCase__ : Tuple = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.0_01, 10_00),
'''kilolitre''': from_to(1, 1),
'''gallon''': from_to(0.0_04_54... | 699 | 1 |
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 ImageProcessingSavingTestMixin, prepare_image_inputs
... | 699 | 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
lowerCAmelCase__ : Tuple = logging.get_logger(__name__)
lowerCAmelCase__ : Union[str, An... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : List[str] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 699 | import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer,... | 699 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __snake_case :
__lowerCamelCase = field(
metadata={"""help""": ... | 699 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMSchedul... | 699 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 699 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 699 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : str = logging.get_logger(__name__)
lowerCAmelCase__ : List[str] = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt model... | 699 | from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __snake_case :
__lowerCamelCase = field(
metadata={"""help""": ... | 699 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_ma... | 699 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModel... | 699 | 1 |
def UpperCamelCase__ ( A__ = 1000 ) -> int:
snake_case__ , snake_case__ : str = 1, 1
snake_case__ : Optional[int] = 2
while True:
snake_case__ : Union[str, Any] = 0
snake_case__ : Tu... | 699 | from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase__ : List[Any] = datasets.utils.logging.get_logger(__name__)
class __snake_case ( folder_based_builder.FolderBasedBuild... | 699 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCAmelCase__ : Dict = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and ... | 699 | import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IM... | 699 | 1 |
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,
map_nes... | 699 | import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ : List[Any] = ... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : Dict = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
try:
if not is_torch_available():
... | 699 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowerCAmelCase__ : List[str] = HfApi()
lowerCAmelCase__ : str = {}
# fmt: off
lowerCAmelCase__ : int = torch.tensor([
-0.75_15, -1.68_83, 0.24_20, 0.03_00, 0.63_47, 1.34_33, -1... | 699 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __snake_case ( _low... | 699 | import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
def __init__( self , *__UpperCamelCase , **__Up... | 699 | 1 |
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=_lowerCamelCase ):
__lowerCamelCase = ["""torch""", """scipy"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ) -> Union[str, Any]:
''... | 699 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCAmelCase__ : List[Any] = datasets.uti... | 699 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ : List[str] = {
'''configuration_speech_to_text''': ['''SPEEC... | 699 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Any = {'''configuration_xglm''': [''... | 699 | 1 |
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