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 |
|---|---|---|---|---|
'''simple docstring'''
import re
def __lowerCamelCase ( _lowercase ) -> list:
return [char.split() for char in re.split(R"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def __lowerCamelCase ( _lowercase ) -> str:
UpperCAmelCase : Optional[int] = split_... | 672 |
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> bool:
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 672 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a : List[Any] = logging.get... | 672 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCAmelCase : Union[str, Any] = set()
# Replace all the whitespace in our sentence
UpperCAmelCase : List[str] = input_str.rep... | 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Tuple = False
class UpperCamelCase_ ( unit... | 672 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. ... | 672 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase_ ( __magic_name__ ):
@staticmethod
@abstractmethod
def _lowercase( A ) -> List[Any]:
raise NotImplementedError()
@abstractmethod
def ... | 672 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Any = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""goo... | 672 | 1 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
a : List[Any] = logging.getLogger()
@unittest.skip('Temporar... | 672 |
'''simple docstring'''
a : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Dict = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
... | 672 | 1 |
'''simple docstring'''
import numpy as np
def __lowerCamelCase ( _lowercase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 672 |
'''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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condit... | 672 | 1 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : int = logging.get_logger(__name__)
a : Union[str, Any] = {
"""facebook/encodec_24khz""": """... | 672 |
'''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
| 672 | 1 |
'''simple docstring'''
from collections import deque
def __lowerCamelCase ( _lowercase ) -> Optional[Any]:
UpperCAmelCase : Any = len(_lowercase )
UpperCAmelCase : List[Any] = deque()
UpperCAmelCase : Tuple = [False for _ in range(_lowercase )... | 672 |
'''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... | 672 | 1 |
'''simple docstring'''
from math import isqrt, loga
def __lowerCamelCase ( _lowercase ) -> list[int]:
UpperCAmelCase : Optional[int] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j... | 672 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> np.array:
UpperCAmelCase : Optional[Any] = int(np.ceil((x_end - xa) / st... | 672 | 1 |
'''simple docstring'''
class UpperCamelCase_ :
def __init__( self ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = 0
UpperCAmelCase : int = 0
UpperCAmelCase : Dict = {}
def _lowercase( self , A ) -> Tup... | 672 |
'''simple docstring'''
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
a : List[str] = logging.get_logger(__name__)... | 672 | 1 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : List[Any] = logging.get_logger(__name__)
a : str ... | 672 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 672 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sag... | 672 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 672 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a : Optional[Any] = ... | 672 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
a : str = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies... | 672 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = 1_0_0_0 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 672 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowerCamelCase ( _lowercase , _lowercase ) -> str | Literal[False]:
UpperCAmelCase : Optional[int] = list(_lowercase )
UpperCAmelCa... | 672 | 1 |
'''simple docstring'''
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class UpperCamelCase_ :
def __init__( self , A ) -> Optional[Any]:
UpperCAmelCase : Any = data
UpperCAmelCase : Dict = [0X67452301, 0Xe... | 672 |
'''simple docstring'''
a : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : str = 0
while number:
# Increased Speed Slightly by checking ev... | 672 | 1 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
impo... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, 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_modeli... | 672 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCamelCase ( _lowercase , _lowercase = True , _lowercase = math.inf , _lowercase = -math.inf , _lowercase = math.inf , _lowercase = ... | 672 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class UpperCamelCase_ :
lowercase = None
lowercase = False
lowercase = False
lowercase = False
lowercase = None
lowercase ... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 672 | 1 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCamelCase_ :
def _lowercase( self , A ) -> Optional[int]:
raise NotImplementedError()
def _l... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Tuple = False
class UpperCamelCase_ ( unit... | 672 | 1 |
'''simple docstring'''
import math
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : str = input("""Enter message: """ )
UpperCAmelCase : Union[str, Any] = int(input(F'''Enter key [2-{len(_lowercase ) - 1}]: ''' ) )
UpperCAmelCase : Dict = in... | 672 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorF... | 672 | 1 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Any = ... | 672 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
a : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __lowerCamelCase... | 672 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 672 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 672 | 1 |
'''simple docstring'''
from collections import deque
class UpperCamelCase_ :
def __init__( self , A , A , A ) -> None:
UpperCAmelCase : str = process_name # process name
UpperCAmelCase : List[Any] = arrival_time # arrival time of the process
... | 672 |
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> bool:
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 672 | 1 |
'''simple docstring'''
a : List[str] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a : int = {
0: """Sunday""",
1: """Monday""",
2: """Tuesday""",
3: """Wednesday""",
4: """Thursday""",
... | 672 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCAmelCase : Union[str, Any] = set()
# Replace all the whitespace in our sentence
UpperCAmelCase : List[str] = input_str.rep... | 672 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, tor... | 672 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. ... | 672 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoi... | 672 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Any = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""goo... | 672 | 1 |
'''simple docstring'''
import pprint
import requests
a : Optional[Any] = """https://zenquotes.io/api"""
def __lowerCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __lowerCamelCase ( ) -> list:
... | 672 |
'''simple docstring'''
a : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Dict = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
... | 672 | 1 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInpu... | 672 |
'''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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condit... | 672 | 1 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def __lowerCamelCase ( ... | 672 |
'''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
| 672 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
a : int = logging.get_logger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
def __init__( self , *A , **A ) -> None:
... | 672 |
'''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... | 672 | 1 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class UpperCamelCase_ ( __magic_name__ ):
lowercase ... | 672 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> np.array:
UpperCAmelCase : Optional[Any] = int(np.ceil((x_end - xa) / st... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class UpperCamelCase_ ( unittest.... | 672 |
'''simple docstring'''
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
a : List[str] = logging.get_logger(__name__)... | 672 | 1 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCamelCase_ ( pl.LightningModule ):
def __init__( self , A ) -> Dict:
super()... | 672 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 672 | 1 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusio... | 672 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 672 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.m... | 672 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
a : str = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies... | 672 | 1 |
'''simple docstring'''
a : int = tuple[float, float, float]
a : Optional[int] = tuple[float, float, float]
def __lowerCamelCase ( _lowercase , _lowercase ) -> Vectorad:
UpperCAmelCase : Any = end_pointa[0] - end_pointa[0]
... | 672 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowerCamelCase ( _lowercase , _lowercase ) -> str | Literal[False]:
UpperCAmelCase : Optional[int] = list(_lowercase )
UpperCAmelCa... | 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase_ ( ... | 672 |
'''simple docstring'''
a : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : str = 0
while number:
# Increased Speed Slightly by checking ev... | 672 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transf... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
... | 672 | 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_c... | 672 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCamelCase ( _lowercase , _lowercase = True , _lowercase = math.inf , _lowercase = -math.inf , _lowercase = math.inf , _lowercase = ... | 672 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Ima... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 672 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Any = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""goo... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Tuple = False
class UpperCamelCase_ ( unit... | 672 | 1 |
'''simple docstring'''
from itertools import product
def __lowerCamelCase ( _lowercase , _lowercase ) -> list[int]:
UpperCAmelCase : Any = sides_number
UpperCAmelCase : List[Any] = max_face_number * dice_number
UpperCAmelCase : int = [... | 672 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorF... | 672 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCamelCase ( _lowercase , _lowercase = True , _lowercase = math.inf , _lowercase = -math.inf , _lowercase = math.inf , _lowercase = ... | 672 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
a : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __lowerCamelCase... | 672 | 1 |
'''simple docstring'''
import cmath
import math
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase ) -> complex:
UpperCAmelCase : List[Any] = math.radians(_lowercase )
UpperCAmelCase : Union[str, Any] = math.radia... | 672 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 672 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_confi... | 672 |
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> bool:
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 672 | 1 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,... | 672 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCAmelCase : Union[str, Any] = set()
# Replace all the whitespace in our sentence
UpperCAmelCase : List[str] = input_str.rep... | 672 | 1 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMo... | 672 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. ... | 672 | 1 |
'''simple docstring'''
from math import pow
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_... | 672 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Any = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""goo... | 672 | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCamelCase_ ( __magic_name__ ):
def _lowercase( self , A ) -> Optional[int]:
with open(A , encoding="""utf-8""" ) as in... | 672 |
'''simple docstring'''
a : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Dict = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __lowerCamelCase ( _lowercase ) -> list[list[float]]:
UpperCAmelCase : Dict = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# ... | 672 |
'''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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condit... | 672 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
a : Union[str, Any] = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Underst... | 672 |
'''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
| 672 | 1 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCamelCase ( ) -> str:
raise RuntimeError("""CUDA out of memory.""" )
cl... | 672 |
'''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... | 672 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
... | 672 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> np.array:
UpperCAmelCase : Optional[Any] = int(np.ceil((x_end - xa) / st... | 672 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
... | 672 |
'''simple docstring'''
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
a : List[str] = logging.get_logger(__name__)... | 672 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
a : str = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies... | 672 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 672 | 1 |
'''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
| 672 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a : int = get_tests_di... | 672 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
a : str = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies... | 672 | 1 |
'''simple docstring'''
import numpy as np
def __lowerCamelCase ( _lowercase ) -> np.array:
return 1 / (1 + np.exp(-vector ))
def __lowerCamelCase ( _lowercase ) -> np.array:
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
impor... | 672 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowerCamelCase ( _lowercase , _lowercase ) -> str | Literal[False]:
UpperCAmelCase : Optional[int] = list(_lowercase )
UpperCAmelCa... | 672 | 1 |
'''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."""
)
| 672 |
'''simple docstring'''
a : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : str = 0
while number:
# Increased Speed Slightly by checking ev... | 672 | 1 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
parser.add_argument("""--... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
... | 672 | 1 |
'''simple docstring'''
import qiskit
def __lowerCamelCase ( _lowercase , _lowercase ) -> qiskit.result.counts.Counts:
UpperCAmelCase : int = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
UpperCAmelCase... | 672 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCamelCase ( _lowercase , _lowercase = True , _lowercase = math.inf , _lowercase = -math.inf , _lowercase = math.inf , _lowercase = ... | 672 | 1 |
'''simple docstring'''
a : Optional[Any] = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.gi... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 672 | 1 |
'''simple docstring'''
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
a : List[str] = logging.get_logger(__name__)... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Tuple = False
class UpperCamelCase_ ( unit... | 672 | 1 |
'''simple docstring'''
import string
def __lowerCamelCase ( _lowercase ) -> str:
UpperCAmelCase : Any = """"""
for i in sequence:
UpperCAmelCase : str = ord(_lowercase )
if 6_5 <= extract <= 9_0:
output += chr(... | 672 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorF... | 672 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.u... | 672 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
a : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __lowerCamelCase... | 672 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def __lowerCamelCase ( _lowercase ) -> Any:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikiped... | 672 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 672 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 672 |
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> bool:
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 672 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase ) -> bool:
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def __lowerCamelCase ( _lowercase ) -> bool:
UpperCAmelCase : List[str] = credit_card... | 672 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCAmelCase : Union[str, Any] = set()
# Replace all the whitespace in our sentence
UpperCAmelCase : List[str] = input_str.rep... | 672 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common impo... | 672 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. ... | 672 | 1 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
a : Tuple = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__ ):
lowercase = 'masked_bert'
def __init__( self , A=30522 , A=768 , A=12 ... | 672 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Any = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""goo... | 672 | 1 |
'''simple docstring'''
a : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Dict = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
... | 672 |
'''simple docstring'''
a : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Dict = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
... | 672 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.wa... | 672 |
'''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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condit... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowerCamelCase ( _lowercase , _lowercase ) -> str | Literal[False]:
UpperCAmelCase : Optional[int] = list(_lowercase )
UpperCAmelCa... | 672 |
'''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
| 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class UpperCamelCase_ ( unittest.TestCase ):
def _lowercase( self ) -> int:
UpperCAmelCas... | 672 |
'''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... | 672 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 672 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> np.array:
UpperCAmelCase : Optional[Any] = int(np.ceil((x_end - xa) / st... | 672 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from tr... | 672 |
'''simple docstring'''
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
a : List[str] = logging.get_logger(__name__)... | 672 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoF... | 672 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 672 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import l... | 672 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 672 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging... | 672 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
a : str = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies... | 672 | 1 |
'''simple docstring'''
from collections import defaultdict
def __lowerCamelCase ( _lowercase , _lowercase ) -> bool:
UpperCAmelCase : Dict = first_str.lower().strip()
UpperCAmelCase : Union[str, Any] = second_str.lower().strip()
# Remove white... | 672 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowerCamelCase ( _lowercase , _lowercase ) -> str | Literal[False]:
UpperCAmelCase : Optional[int] = list(_lowercase )
UpperCAmelCa... | 672 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_f... | 672 |
'''simple docstring'''
a : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : str = 0
while number:
# Increased Speed Slightly by checking ev... | 672 | 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... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
... | 672 | 1 |
'''simple docstring'''
import qiskit
def __lowerCamelCase ( _lowercase , _lowercase ) -> qiskit.result.counts.Counts:
UpperCAmelCase : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
UpperCAmelCase... | 672 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCamelCase ( _lowercase , _lowercase = True , _lowercase = math.inf , _lowercase = -math.inf , _lowercase = math.inf , _lowercase = ... | 672 | 1 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> float:
UpperCAmelCase : List[Any] = x
UpperCAmelCase : List[str] = y
for step in range(_lower... | 672 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 672 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_te... | 672 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Tuple = False
class UpperCamelCase_ ( unit... | 672 | 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_... | 672 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorF... | 672 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a : Dict = 5_0_0_0_0_0
a , a : Optional[Any] = os.path.split(__file__)
a : Optional[Any] ... | 672 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
a : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __lowerCamelCase... | 672 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> float:
return base * power(_lowercase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("""Raise base to the power of exponent using recursion...""")
a : ... | 672 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 672 | 1 |
'''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 imp... | 700 |
'''simple docstring'''
import math
def __lowerCamelCase ( _lowercase ) -> bool:
assert isinstance(_lowercase , _lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 672 | 0 |
'''simple docstring'''
import numpy
# List of input, output pairs
a : Optional[int] = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
a : Optional[int] = (((5_1_5, 2_2, 1_3... | 701 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCAmelCase : Union[str, Any] = set()
# Replace all the whitespace in our sentence
UpperCAmelCase : List[str] = input_str.rep... | 672 | 0 |
'''simple docstring'''
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils ... | 702 |
'''simple docstring'''
from sklearn.metrics import mean_squared_error
import datasets
a : Union[str, Any] = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. ... | 672 | 0 |
from typing import Any
def __lowerCamelCase ( _lowercase ) -> list[Any]:
if not input_list:
return []
UpperCAmelCase : Any = [input_list.count(_lowercase ) for value in input_list]
UpperCAmelCase : int = max(_lowercase ) # Gets the maximum ... | 703 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Any = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""goo... | 672 | 0 |
import os
from collections.abc import Iterator
def __lowerCamelCase ( _lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowercase ):
UpperCAmelCase : Optional[int] = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
... | 704 |
'''simple docstring'''
a : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Dict = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
... | 672 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : Dict ... | 705 |
'''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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Condit... | 672 | 0 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
# Initialise PyTorch... | 706 |
'''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
| 672 | 0 |
'''simple docstring'''
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
a : Dict = logging.get_logger(__name__)
cla... | 707 |
'''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... | 672 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-rob... | 708 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> np.array:
UpperCAmelCase : Optional[Any] = int(np.ceil((x_end - xa) / st... | 672 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
... | 709 |
'''simple docstring'''
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
a : List[str] = logging.get_logger(__name__)... | 672 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
while a != 0:
UpperCAmelCase : Optional[Any] = b % a, a
return b
def __lowerCamelCase ( _lowercase , _lowercase ) -> int:
if gcd(... | 710 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tok... | 672 | 0 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase = 6_0_0_8_5_1_4_7_5_1_4_3 ) -> Optional[int]:
try:
UpperCAmelCase : int = int(_lowerCAmelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castabl... | 711 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 672 | 0 |
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