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"""
import torch
def lowerCamelCase__ ( ) -> Tuple:
"""simple docstring"""
if torch.cuda.is_available():
_UpperCamelCase = torch.cuda.device_count()
else:
_UpperCamelCase = 0
print(F''... | 356 |
"""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/licens... | 100 | 0 |
"""simple docstring"""
import requests
_a : int = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def lowerCamelCase__ ( __snake_case ) -> None:
"""simple docstring"""
_UpperCamelCase = requests.get... | 357 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 100 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case = 4_00_00_00 ) -> int:
"""simple docstring"""
_UpperCamelCase = [0, 1]
_UpperCamelCase = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 358 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead.""... | 100 | 0 |
import re
import subprocess
import sys
_a = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
_a = (
subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()
)
_a ... | 359 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""bert-base-uncased"... | 100 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a = {
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
... | 360 |
"""simple docstring"""
import numpy
class _UpperCAmelCase:
def __init__( self , __a , __a) -> None:
'''simple docstring'''
_UpperCamelCase = input_array
# Random initial weights are assigned where first argument is the
... | 100 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""token... | 361 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock... | 100 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
... | 362 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, 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
fr... | 100 | 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
... | 363 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_a = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONF... | 100 | 0 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _UpperCAmelCase( unittest.TestCase ):
def UpperC... | 364 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 100 | 0 |
"""simple docstring"""
import math
def lowerCamelCase__ ( __snake_case, __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = len(__snake_case )
_UpperCamelCase = int(math.floor(math.sqrt(__snake_case ) ... | 365 |
"""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/licens... | 100 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_... | 366 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[int]:
"""simple docstring"""
if len(__snake_case ) <= 1 or n <= 1:
return
insert_next(__snake_case, ... | 100 | 0 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock... | 367 |
"""simple docstring"""
class _UpperCAmelCase:
def __init__( self , __a , __a , __a) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = name
_UpperCamelCase = value
_UpperCamelCase = weight
def __r... | 100 | 0 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case ) -> np.ndarray:
"""simple docstring"... | 368 |
"""simple docstring"""
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, ImagePipelineOutpu... | 100 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_A... | 369 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"""_TestComma... | 100 | 0 |
"""simple docstring"""
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 (
SegformerConfig,
SegformerForImageClassification,
... | 370 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case, __snake_case ) -> str:
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
_UpperCamelCase = s... | 100 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from tra... | 371 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.... | 100 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCamelCase__ ( __snake_case="ro", __snake_case="en", __snake_case="wmt16", __snake_case=None ) -> None:
"""simple docstring"""
try:
import datasets
except ... | 350 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowerCamelCase__ ( __snake_case ) -> None:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase ... | 100 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case = 50_00_00_00 ) -> int:
"""simple docstring"""
__UpperCamelCase = set()
__UpperCamelCase = int((limit - 24) ** (1 / 2) )
__UpperCamelCase = set(range(3, ... | 351 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mo... | 100 | 0 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.ber... | 352 |
"""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
_a = logging.get_logger(__name__)
_a ... | 100 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ... | 353 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""facebook/xlm-rober... | 100 | 0 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_... | 354 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a ... | 100 | 0 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase__ ( __snake_case, __snake_case=False ) -> List[str]:
"""simple docstring"""
_UpperCamel... | 355 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 100 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase )
class _UpperCAmelCase( lowerCamelCase ):
low... | 356 |
"""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/licens... | 100 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase( metaclass=lowerCamelCase ):
lowercase__ = ['keras_nlp']
def __init__( self , *__a , **__a) -> Tuple:
'''simple docstring'''
requires... | 357 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 100 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_ta... | 358 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead.""... | 100 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
requ... | 359 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""bert-base-uncased"... | 100 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""BridgeTower/bridgetower-base""": """https://huggingface.co/Bridge... | 360 |
"""simple docstring"""
import numpy
class _UpperCAmelCase:
def __init__( self , __a , __a) -> None:
'''simple docstring'''
_UpperCamelCase = input_array
# Random initial weights are assigned where first argument is the
... | 100 | 0 |
"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torc... | 361 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock... | 100 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case = 1_00 ) -> int:
"""simple docstring"""
_UpperCamelCase = set()
_UpperCamelCase = 0
_UpperCamelCase = n + 1 # maximum limit
for a in range(2, ... | 362 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, 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
fr... | 100 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case ) -> List[str]:
"""simple docstring"""
for i in range(0, __snake_case ):
for _ in range(0, n - i - 1 ): # printing spaces
print(''' ''', end='''''' )... | 363 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_a = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONF... | 100 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_a : Any = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise Optional... | 364 |
"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 100 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTok... | 365 |
"""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/licens... | 100 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = len(__snake_case )
for i in range(length - 1 ):
_UpperCamelCase = i
for k in range(i + 1, ... | 366 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[int]:
"""simple docstring"""
if len(__snake_case ) <= 1 or n <= 1:
return
insert_next(__snake_case, ... | 100 | 0 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm... | 367 |
"""simple docstring"""
class _UpperCAmelCase:
def __init__( self , __a , __a , __a) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = name
_UpperCamelCase = value
_UpperCamelCase = weight
def __r... | 100 | 0 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _UpperCAmelCase( lowerCamelCase ):
def UpperCAmelCase ( self) -> Dict:
'''simple docstring'''
... | 368 |
"""simple docstring"""
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, ImagePipelineOutpu... | 100 | 0 |
"""simple docstring"""
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_availabl... | 369 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"""_TestComma... | 100 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a = logging.get_logger(__name__)
_a = {
"""facebook/convnex... | 370 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case, __snake_case ) -> str:
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
_UpperCamelCase = s... | 100 | 0 |
"""simple docstring"""
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_a = TypeVar("""T""")
class _UpperCAmelCase( Generic[T] ):
def __init__( self , __a = True) -> None:
'''simple... | 371 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.... | 100 | 0 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number ... | 101 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax impo... | 101 | 1 |
'''simple docstring'''
# 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]' w... | 101 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class A__ :
def __init__( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
A_ = str(id_ )
A_ = None
A_ = None
A... | 101 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 10_00 ) -> int:
A_ = -1
A_ = 0
for a in range(1, n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
A_ = (n * n - 2 * a * n) // (2 * n -... | 101 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if number != int(UpperCAmelCase__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of... | 101 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_util... | 101 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_blenderbot''': [
... | 101 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_... | 101 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__m... | 101 | 1 |
'''simple docstring'''
import qiskit
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> qiskit.result.counts.Counts:
A_ = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
A_ = qiskit.QuantumCircuit(Up... | 101 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
f... | 101 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> int:
return int(input_a == input_a == 0 )
def UpperCAmelCase__ ( ) -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )... | 101 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 101 | 1 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
__lowerCamelCase = parse(importlib.metadata.version('''torch'''))
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCas... | 101 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requi... | 101 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> float:
A_ = sorted(numsa + numsa )
A_ , A_ = divmod(len(UpperCAmelCase__ ), 2 )
if mod == 1:
return all_numbers[d... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 1_00 ) -> int:
A_ = n * (n + 1) * (2 * n + 1) / 6
A_ = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 101 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from tran... | 101 |
'''simple docstring'''
import requests
__lowerCamelCase = '''''' # <-- Put your OpenWeatherMap appid here!
__lowerCamelCase = '''https://api.openweathermap.org/data/2.5/'''
def UpperCAmelCase__ ( UpperCAmelCase__ = "Chicago", UpperCAmelCase__ = APPID ) -> dict:
... | 101 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_... | 101 |
'''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,
)
__lowerCamelCase... | 101 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCH... | 101 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 101 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> int:
return number | (1 << position)
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> int:
return number & ~(1 << position)
def UpperCAmelCase__ ( UpperC... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> bool:
A_ = len(UpperCAmelCase__ )
A_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any el... | 101 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 101 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 101 | 1 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational imp... | 101 |
'''simple docstring'''
import sys
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'... | 101 | 1 |
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class A__ ( _snake_case ):
lowercase = "M-CLIP"
def __init__( self , UpperCamelCase__=1024 , UpperCamelCase__=768 , **UpperCamelCase__ ) -> str... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> float:
if edge <= 0 or not isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
... | 101 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import gcd
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ = 2, UpperCAmelCase__ = 1, UpperCAmelCase__ = 3, ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
... | 101 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_... | 101 | 1 |
'''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
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase... | 101 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if not postfix_notation:
return 0
A_ = {"""+""", """-""", """*""", """/"""}
A_ = []
for token in postfix_notati... | 101 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> bool:
A_ = len(UpperCAmelCase__ )
A_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any el... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 10_00 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3, n + 1 ) )
if __name__ == "__main__":
print(solution())
| 101 | 1 |
'''simple docstring'''
import re
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> str:
if len(re.findall("""[ATCG]""", UpperCAmelCase__ ) ) != len(UpperCAmelCase__ ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""", "... | 101 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, ) -> list[float]:
A_ , A_ = coeffici... | 101 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.tes... | 101 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImage... | 101 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_snake_case )
class A__ ( _snake_case ):
lowercase = field(default="audio-... | 101 |
'''simple docstring'''
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
... | 101 | 1 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_av... | 101 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax impo... | 101 | 1 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__lowerCamelCase = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator... | 101 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class A__ :
def __init__( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
A_ = str(id_ )
A_ = None
A_ = None
A... | 101 | 1 |
'''simple docstring'''
__lowerCamelCase = [
'''DownloadConfig''',
'''DownloadManager''',
'''DownloadMode''',
'''StreamingDownloadManager''',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager ... | 101 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if number != int(UpperCAmelCase__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of... | 101 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> str:
return " ".join(
"""""".join(word[::-1] ) if len(UpperCAmelCase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
prin... | 101 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_blenderbot''': [
... | 101 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if not postfix_notation:
return 0
A_ = {"""+""", """-""", """*""", """/"""}
A_ = []
for token in postfix_notati... | 101 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__m... | 101 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
i... | 101 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
f... | 101 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.sta... | 101 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 101 | 1 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''facebook/encodec_24khz''': '''https://h... | 101 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requi... | 101 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCamelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepende... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 1_00 ) -> int:
A_ = n * (n + 1) * (2 * n + 1) / 6
A_ = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 101 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vi... | 101 |
'''simple docstring'''
import requests
__lowerCamelCase = '''''' # <-- Put your OpenWeatherMap appid here!
__lowerCamelCase = '''https://api.openweathermap.org/data/2.5/'''
def UpperCAmelCase__ ( UpperCAmelCase__ = "Chicago", UpperCAmelCase__ = APPID ) -> dict:
... | 101 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase = logging.get_logger(__name__)
class A__ ( _snake_case ):
def __init__( self , *UpperCamelCase__ , **UpperCamelCase__ ) ... | 101 |
'''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,
)
__lowerCamelCase... | 101 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {}
try:
if not is_sentencepiece_available():
... | 101 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 101 | 1 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if number != int(UpperCAmelCase__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> bool:
A_ = len(UpperCAmelCase__ )
A_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any el... | 101 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_... | 101 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 101 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if not isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" ... | 101 |
'''simple docstring'''
import sys
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'... | 101 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
fro... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> float:
if edge <= 0 or not isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
... | 101 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_... | 101 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_... | 101 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''huggingface/time-series-transformer-tourism-monthly''': (
... | 101 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if not postfix_notation:
return 0
A_ = {"""+""", """-""", """*""", """/"""}
A_ = []
for token in postfix_notati... | 101 | 1 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> int:
while second != 0:
A_ = first & second
first ^= second
A_ = c << 1
return first
if __name__ == "__main__":
import doctest
... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 10_00 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3, n + 1 ) )
if __name__ == "__main__":
print(solution())
| 101 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxC... | 101 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, ) -> list[float]:
A_ , A_ = coeffici... | 101 | 1 |
'''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():
fr... | 101 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImage... | 101 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_availab... | 101 |
'''simple docstring'''
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
... | 101 | 1 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class A__ :
def __init__( self ) -> List[Any]:
'''simple docstring'''
A_ = """"""
A_ = """"""
A_ = []
A_ ... | 101 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax impo... | 101 | 1 |
'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_featu... | 101 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class A__ :
def __init__( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
A_ = str(id_ )
A_ = None
A_ = None
A... | 101 | 1 |
'''simple docstring'''
import sys
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'... | 101 |
'''simple docstring'''
import math
import sys
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if number != int(UpperCAmelCase__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of... | 101 | 1 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def UpperCAmelCase__ ( UpperCAmelCase__ = 3 ) -> qiskit.result.counts.Counts:
if isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
... | 101 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_blenderbot''': [
... | 101 | 1 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_i... | 101 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__m... | 101 | 1 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 101 |
'''simple docstring'''
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
f... | 101 | 1 |
'''simple docstring'''
import math
import unittest
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
assert isinstance(UpperCAmelCase__, UpperCAmelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 a... | 101 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 101 | 1 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v... | 101 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, requi... | 101 | 1 |
'''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... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 1_00 ) -> int:
A_ = n * (n + 1) * (2 * n + 1) / 6
A_ = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 101 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase__ ( UpperCAmelCase__ = "https://www.worldometers.info/coronavirus" ) -> dict:
A_ = BeautifulSoup(requests.get(UpperCAmelCase__ ).text, """html.parser""" )
A_ = soup.findAll("""h1... | 101 |
'''simple docstring'''
import requests
__lowerCamelCase = '''''' # <-- Put your OpenWeatherMap appid here!
__lowerCamelCase = '''https://api.openweathermap.org/data/2.5/'''
def UpperCAmelCase__ ( UpperCAmelCase__ = "Chicago", UpperCAmelCase__ = APPID ) -> dict:
... | 101 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCamelCase =... | 101 |
'''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,
)
__lowerCamelCase... | 101 | 1 |
'''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 ... | 101 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 101 | 1 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCAmelCase__ ( UpperCAmelCase__="ro", UpperCAmelCase__="en", UpperCAmelCase__="wmt16", UpperCAmelCase__=None ) -> None:
try:
import datasets
except (ModuleNotFoundErro... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> bool:
A_ = len(UpperCAmelCase__ )
A_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any el... | 101 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .uti... | 101 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 101 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class A__ ( _snake_c... | 101 |
'''simple docstring'''
import sys
__lowerCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'... | 101 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( _snake_case , unittest.TestCase ... | 101 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> float:
if edge <= 0 or not isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
... | 101 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_... | 101 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_... | 101 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class A__ :
def __init__( self , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
A_ = str(id_ )
A_ = None
A_ = None
A... | 101 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if not postfix_notation:
return 0
A_ = {"""+""", """-""", """*""", """/"""}
A_ = []
for token in postfix_notati... | 101 | 1 |
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