code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''bert-generation'''
def __init__(self : Dict , _UpperCAmelCase : Optional[int]=5_0358 , _UpperCAmelCase ... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : int = 10 , __magic_name__ : int = 1000 , __magic_name__ : bool = True ) -> int:
"""simple docstring"""
assert (
isinstance(__magic_name__ , __magic_name__ )
and isinstance(__magic_name... | 305 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 305 | 1 |
import itertools
import math
def UpperCamelCase ( __magic_name__ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even number... | 305 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.ge... | 305 | 1 |
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co... | 305 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from trans... | 305 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 305 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : Optional[int] , _UpperCAmelCase : str , _UpperCAmelCase... | 305 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A... | 305 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class A ( unittest.TestCase ):
'''simple docstring'''
... | 305 |
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : ... | 305 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 305 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 ... | 305 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Optional[Any] = {
'microsoft/trocr-base-handwritten': (
'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
... | 305 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneratio... | 305 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
A : Dict = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true ... | 305 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
... | 305 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, re... | 305 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
pri... | 305 | 1 |
from collections.abc import Callable
import numpy as np
def UpperCamelCase ( __magic_name__ : Callable , __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ) -> np.a... | 305 |
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in v... | 305 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import Poly... | 305 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 305 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( UpperCAmelCase__ ... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_tit... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : int , __magic_name__ : Any , __magic_name__ : Optional[int] ) -> str:
"""simple docstring"""
lowercase__ = [False] * len(__magic_name__ )
lowerc... | 305 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : ... | 305 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( UpperCAmelCase__ ... | 305 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> L... | 305 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class A ... | 305 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_... | 305 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : complex , __magic_name__ : str = "x" , __magic_name__ : float = 10**-10 , __magic_na... | 305 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(l... | 305 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : Optional[Any] , __magic_name__ : List[Any] , __m... | 305 |
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ ... | 305 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A : Union[str, Any] = logging.get_logger(__name__)
class A ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__(self : int ... | 305 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseS... | 305 | 1 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ... | 305 |
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) != 2 or len(a[0] ) != 2 or len(__magic_name__ ) != 2 or len(b[... | 305 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStruct... | 305 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfor... | 305 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
... | 305 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase ( __magic_name__ : Union[dict, list, tuple, tor... | 305 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_tit... | 305 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.ge... | 305 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A : str = datasets.utils.logging.g... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co... | 305 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMa... | 305 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 305 | 1 |
from __future__ import annotations
import numpy as np
def UpperCamelCase ( __magic_name__ : np.ndarray ) -> tuple[np.ndarray, np.ndarray]:
"""simple docstring"""
lowercase__ , lowercase__ = np.shape(__magic_name__ )
if rows != columns:
lowe... | 305 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A... | 305 | 1 |
from collections.abc import Generator
from math import sin
def UpperCamelCase ( __magic_name__ : bytes ) -> bytes:
"""simple docstring"""
if len(__magic_name__ ) != 32:
raise ValueError("""Input must be of length 32""" )
lowercase__ = B""... | 305 |
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : ... | 305 | 1 |
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 : Optional[Any] = logging.get_logger(__name__)
A : int = {
'g... | 305 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 ... | 305 | 1 |
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ ... | 305 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneratio... | 305 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 305 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
... | 305 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A : Optional[Any] = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnx... | 305 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
pri... | 305 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : List[Any] = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
# ... | 305 |
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in v... | 305 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 305 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 305 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_tit... | 305 | 1 |
from math import factorial
A : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
if not isinstance(__magic_name__ , __magic_name__ ):
rais... | 305 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : ... | 305 | 1 |
# flake8: noqa
# Lint as: python3
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,
... | 305 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> L... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ = []
lowercase__ = set({"""(""", """[""", """{"""} )
lowercase__ = set({""")""", """]""", """}"""} )
lowercase__ = {"""{""": """}"... | 305 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_... | 305 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
A : Optional[Any] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or m... | 305 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(l... | 305 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A : Any = logging.get_logger(__name__)
A : Any = {'vocab_file': 'vocab.json', 'merges_file': ... | 305 |
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ ... | 305 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from... | 305 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseS... | 305 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseS... | 305 |
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) != 2 or len(a[0] ) != 2 or len(__magic_name__ ) != 2 or len(b[... | 305 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
A ... | 305 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfor... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : Dict ) -> Any:
"""simple docstring"""
lowercase__ = [0 for i in range(r + 1 )]
# nc0 = 1
lowercase__ = 1
for i in range(1 , n + 1 ):
# to compute current ro... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : int ) -> int:
"""simple docstring"""
if not isinstance(__magic_name__ , __magic_name__ ):
raise TypeError("""Input value must be an 'int' type""" )
lowercase__ = 0
while number:
position += 1
num... | 305 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 305 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : str = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class A ( ... | 305 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.ge... | 305 | 1 |
from math import pi, sqrt, tan
def UpperCamelCase ( __magic_name__ : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def U... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co... | 305 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
A : List[str] =... | 305 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 305 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
A : List[str] = logging.get... | 305 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A... | 305 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here... | 305 |
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : ... | 305 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class A ( UpperCAmelCase... | 305 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 ... | 305 | 1 |
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 ..auto import CONFIG_MAPPING
A : Any = logging.get_logger(__name__)
A ... | 305 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneratio... | 305 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, rand... | 305 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 ... | 305 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
pri... | 305 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def UpperCamelCase ( __magic_name__ : str ) -> str:
"""simple docstring"""
return "".join(sorted(__magic_name__ ) )
def UpperCamelCase ( __ma... | 305 |
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in v... | 305 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 305 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 305 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgu... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_tit... | 305 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ... | 305 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : ... | 305 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from... | 305 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> L... | 305 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def UpperCamelCase ( __magic_name__ : List[str] , __magic_name__ : Tuple , __magic_name__ : Union[str, Any] , __magic_name__ : ... | 305 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_... | 305 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 305 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(l... | 305 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_tex... | 305 |
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ ... | 305 | 1 |
from __future__ import annotations
A : Any = '#'
class A :
'''simple docstring'''
def __init__(self : int ) -> None:
"""simple docstring"""
lowercase__ = {}
def lowerCamelCase__ (self : ... | 305 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseS... | 305 | 1 |
from math import isqrt
def UpperCamelCase ( __magic_name__ : int ) -> list[int]:
"""simple docstring"""
lowercase__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , ... | 305 |
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) != 2 or len(a[0] ) != 2 or len(__magic_name__ ) != 2 or len(b[... | 305 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 305 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfor... | 305 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tok... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
... | 305 | 1 |
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 (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 305 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 305 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.c... | 305 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.ge... | 305 | 1 |
from collections.abc import Callable
class A :
'''simple docstring'''
def __init__(self : List[str] , _UpperCAmelCase : Callable | None = None ) -> None:
"""simple docstring"""
lowercase__ = []
# ... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co... | 305 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils impor... | 305 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 305 | 1 |
from __future__ import annotations
A : Optional[Any] = 1.6_0_2_1e-1_9 # units = C
def UpperCamelCase ( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float , ) -> tuple[str, float]:
"""simple docstring"""
... | 305 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A... | 305 | 1 |
import argparse
from collections import defaultdict
import yaml
A : int = 'docs/source/en/_toctree.yml'
def UpperCamelCase ( __magic_name__ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowercase__ = defaultdict(__magic_name_... | 305 |
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : ... | 305 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
... | 305 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 ... | 305 | 1 |
from __future__ import annotations
import pandas as pd
def UpperCamelCase ( __magic_name__ : list[int] , __magic_name__ : list[int] , __magic_name__ : int ) -> list[int]:
"""simple docstring"""
lowercase__ = [0] * no_of_pr... | 305 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneratio... | 305 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def UpperCamelCase ( __magic_name__ : int ) -> str:
"""simple docstring"""
if "img_encoder.pos_embed" in name:
lowe... | 305 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
... | 305 | 1 |
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 OptionalDependencyNotAvailable()
except OptionalDependencyNo... | 305 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
pri... | 305 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = field(... | 305 |
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in v... | 305 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
Ju... | 305 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 305 | 1 |
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : int ) -> int:
"""simple docstring"""
lowercase__ = len(__magic_name__ )
lowercase__ = int(math.floor(math.sqrt(__magic_name__ ) ) )
lower... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
lowercase__ = 0
while index >= 0:
lowercase__ = (ord(column_tit... | 305 | 1 |
import torch
def UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
if torch.cuda.is_available():
lowercase__ = torch.cuda.device_count()
else:
lowercase__ = 0
print(f'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main... | 305 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : ... | 305 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoars... | 305 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> L... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : Optional[int] , __magic_name__ : int ) -> List[str]:
"""simple docstring"""
lowercase__ = [1]
for i in range(2 , __magic_name__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < facto... | 305 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
if not is_... | 305 | 1 |
# using dfs for finding eulerian path traversal
def UpperCamelCase ( __magic_name__ : str , __magic_name__ : int , __magic_name__ : Union[str, Any] , __magic_name__ : Tuple=None ) -> Optional[Any]:
"""simple docstring"""
... | 305 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(l... | 305 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Optional[Any] = {
'huggingface/informer-tourism-monthly': (
'https://huggingface.co/huggingface/i... | 305 |
A : Union[str, Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase ( __magic_name__ : dict[int, list[int]] , __magic_name__ : int , __magic_name__ ... | 305 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
A : Any = datasets.utils.logging.get_logger(__name__)
@dataclass
class A ( ... | 305 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseS... | 305 | 1 |
import math
A : Optional[Any] = 1_0
A : List[str] = 7
A : str = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( __magic_name__ : int = 20 ) -> str:
"""simple docstring"""
lowercase__ = math.comb(__magic_name__ ... | 305 |
from __future__ import annotations
import math
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list ) -> list:
"""simple docstring"""
if len(__magic_name__ ) != 2 or len(a[0] ) != 2 or len(__magic_name__ ) != 2 or len(b[... | 305 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def UpperCamelCase ( __magic_name__ : Dict , __magic_name__ : Union[str, Any] , __magic_n... | 305 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfor... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
... | 305 |
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
... | 305 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class A :
'''simple docstring'''
A__ = 42
A__ = None
A__ = None
A : Union[str, Any] = namedtuple('CoinsDistrib... | 305 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 305 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto imp... | 305 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.ge... | 305 | 1 |
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,
ConditionalDetrFor... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co... | 305 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = (DDPMParallelScheduler,)
def lowerCamelCase__ (self : str , ... | 305 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 305 | 1 |
import math
import qiskit
def UpperCamelCase ( __magic_name__ : int = 1 , __magic_name__ : int = 1 , __magic_name__ : int = 1 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if (
isinstance(__magic_name__ , _... | 305 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
return x + 2
class A... | 305 | 1 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( __magic_name__ : str = "AAPL" ) -> str:
"""simple docstring"""
lowercase__ = f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase__ = BeautifulSoup(requests.ge... | 305 |
class A :
'''simple docstring'''
def __init__(self : List[str] ) -> Tuple:
"""simple docstring"""
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def lowerCamelCase__ (self : ... | 305 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_... | 305 |
def UpperCamelCase ( __magic_name__ : int , __magic_name__ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def UpperCamelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 ... | 305 | 1 |
def UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
for n in range(1 , 100_0000 ):
yield n * (n + 1) // 2
def UpperCamelCase ( __magic_name__ : List[str] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ ... | 305 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneratio... | 305 | 1 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class A :
'''simple docstring'''
def __init__(self : Optional[Any] , _UpperCAmelCase ... | 305 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A ( tf.keras.layers.Layer ):
'''simple docstring'''
... | 305 | 1 |
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in v... | 305 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : Optional[int] = 1_0_0
A : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
pri... | 305 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
... | 305 |
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in v... | 305 | 1 |
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