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 |
|---|---|---|---|---|
'''simple docstring'''
def a__ ( a__ = 10**9 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while p... | 267 |
from __future__ import annotations
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = len(snake_case__ ) - 1
while i < j:
... | 306 | 0 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _lowerCamelCase ( _UpperCAmelCase ):
@require_torch
def snake_case_ ... | 153 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling... | 306 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : str ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
... | 109 |
def __lowerCamelCase ( snake_case__ ) -> list:
"""simple docstring"""
def merge(snake_case__ ,snake_case__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[... | 306 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import Lea... | 66 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
... | 306 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = s.rsplit(snake_case__, snake_case__ )
return new.join(snake_case... | 299 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( snake_case__ ,snake_case__ ... | 306 | 0 |
import heapq
import sys
import numpy as np
UpperCAmelCase__ = tuple[int, int]
class lowercase_ :
'''simple docstring'''
def __init__( self : Optional[Any] ) ->str:
"""simple docstring"""
a ... | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 306 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@r... | 220 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mas... | 306 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
... | 275 |
import random
def __lowerCamelCase ( snake_case__ ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = num - 1
_SCREAMING_SNAKE_CASE = 0
while s % 2 == 0:
_SCREAMING_SNAKE_CASE = s // 2
... | 306 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_A : Lis... | 41 |
def __lowerCamelCase ( snake_case__ ) -> int:
"""simple docstring"""
if not isinstance(snake_case__ ,snake_case__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
_SCREAMING_SNAKE_CASE =... | 306 | 0 |
def SCREAMING_SNAKE_CASE__ ( __a ):
assert isinstance(snake_case__ , snake_case__ ), f"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
snake_case_ : Optional[int] = f"""The input value of [n={number}] ha... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''... | 306 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 12 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 306 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self : Any , __SCREAMING_SNAKE_CASE ... | 267 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(snake_case__ )
_SCREAMING_SNAKE_CASE = [[0] * n for i in range(snake_case__ )]
for i... | 306 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def a__ ( _SCREAMING_SNAKE_CASE ):
"""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... | 153 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wa... | 306 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class SCREAMING_SNAKE_CASE__ :
def __init__( self ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Optional[int] = {}
def SCREAMING_SNAKE_C... | 109 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __UpperCAmelCase :
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor... | 306 | 0 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
__a = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def A_ ( ):
'''simple docstring'''
snake_case_ :str = os.path.dirname(os.path.realpath(snake_case__ ) )
snake_case_ :Tuple... | 66 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 306 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, ... | 299 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __UpperCAmelCase :
def __init__( self: List[str] , UpperCAmelCase_: Dict ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE ... | 306 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transforme... | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pi... | 306 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForCondi... | 220 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 306 | 0 |
def _lowercase ( lowercase__ ):
__lowerCAmelCase : Optional[int] = generate_pascal_triangle(snake_case__ )
for row_idx in range(snake_case__ ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''... | 275 |
# 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... | 306 | 0 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_A : str =logging.getLogger(__name__)
@dataclass
class _lowercase ( ... | 41 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __lowerCamelCase ( ) -> tuple[list[int], int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [randint(-10_00 ... | 306 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
cl... | 327 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
i... | 306 | 0 |
def lowerCamelCase__ ( A__ : List[Any] ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return lst
__lowerCamelCase = 1
while i < len(snake_case__ ):
if lst[i - 1] <= lst[i]:
i += 1
... | 12 |
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 (
ProphetNetForConditionalG... | 306 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase : Union[str, Any] = {
'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CON... | 267 |
from __future__ import annotations
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = len(snake_case__ ) - 1
while i < j:
... | 306 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class _lowerCamelCase :
UpperCAmelCase_ = 42 # [batch_size x 3]
UpperCAmelCase_ = 42 # [batch_size x 3]
UpperCAmelCase_ = 42 # [bat... | 153 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling... | 306 | 0 |
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_availabl... | 109 |
def __lowerCamelCase ( snake_case__ ) -> list:
"""simple docstring"""
def merge(snake_case__ ,snake_case__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[... | 306 | 0 |
"""simple docstring"""
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__a = {
"facebook/maskformer-swin-base-ade": (
"h... | 66 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
... | 306 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available()... | 299 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( snake_case__ ,snake_case__ ... | 306 | 0 |
def _a ( a :Union[str, Any] , a :List[Any] , a :Tuple ) -> float:
a = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def _a ( ) -> str:
print(sum_of_series(1 , ... | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 306 | 0 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLe... | 220 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mas... | 306 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational i... | 275 |
import random
def __lowerCamelCase ( snake_case__ ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = num - 1
_SCREAMING_SNAKE_CASE = 0
while s % 2 == 0:
_SCREAMING_SNAKE_CASE = s // 2
... | 306 | 0 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterM... | 41 |
def __lowerCamelCase ( snake_case__ ) -> int:
"""simple docstring"""
if not isinstance(snake_case__ ,snake_case__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
_SCREAMING_SNAKE_CASE =... | 306 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class SCREAMING_SNAKE_CASE_ :
__magic_name__: int
__magic_name__: Node | None = None
__magic_n... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''... | 306 | 0 |
def lowerCamelCase__ ( A__ : Tuple , A__ : Union[str, Any] ):
'''simple docstring'''
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError("""String lengths must match!""" )
__lowerCamelCase = 0
for chara, cha... | 12 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 306 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 267 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(snake_case__ )
_SCREAMING_SNAKE_CASE = [[0] * n for i in range(snake_case__ )]
for i... | 306 | 0 |
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_... | 153 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wa... | 306 | 0 |
"""simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_m... | 109 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __UpperCAmelCase :
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor... | 306 | 0 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def A_ ( _lowercase = 3 ):
'''simple docstring'''
if isinstance(snake_case__, snake_case__ ):
raise TypeError("""number ... | 66 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 306 | 0 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def A__ ( ... | 299 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __UpperCAmelCase :
def __init__( self: List[str] , UpperCAmelCase_: Dict ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE ... | 306 | 0 |
from scipy.stats import spearmanr
import datasets
UpperCAmelCase__ = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive c... | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pi... | 306 | 0 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigT... | 220 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 306 | 0 |
def _lowercase ( lowercase__ ):
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
__lowerCAmelCase : Dict = [True] * (num + 1)
__lowerCAmelCase : List[Any] = 2
while p * p <= num:
if primes[p]... | 275 |
# 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... | 306 | 0 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
... | 41 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __lowerCamelCase ( ) -> tuple[list[int], int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [randint(-10_00 ... | 306 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, typ... | 327 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
i... | 306 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar('T')
def lowerCamelCase__ ( A__ : Tuple ):
'''simple docstring'''
return (position - 1) // 2
def lowerCamelCase__ ( ... | 12 |
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 (
ProphetNetForConditionalG... | 306 | 0 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fl... | 267 |
from __future__ import annotations
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = len(snake_case__ ) - 1
while i < j:
... | 306 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICEN... | 153 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling... | 306 | 0 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 109 |
def __lowerCamelCase ( snake_case__ ) -> list:
"""simple docstring"""
def merge(snake_case__ ,snake_case__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[... | 306 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import ... | 66 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
... | 306 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase__ :
"""simple docstring"""
UpperCAmelCase_ =None
def _UpperCamelCase ( self ) -> Union[str, Any]:
... | 299 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( snake_case__ ,snake_case__ ... | 306 | 0 |
from __future__ import annotations
import math
def _a ( a :Dict ) -> list[int]:
if num <= 0:
a = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(snake_case__ )
a = [True] * (num + 1)
a = []
a = ... | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 306 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( __snake_case : str , __snake_case : Union[str, Any] ):
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
assert or_gate... | 220 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mas... | 306 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floa... | 275 |
import random
def __lowerCamelCase ( snake_case__ ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = num - 1
_SCREAMING_SNAKE_CASE = 0
while s % 2 == 0:
_SCREAMING_SNAKE_CASE = s // 2
... | 306 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> int:
if not numbers:
return 0
if not isinstance(snake_case__ , (list, tuple) ) or not all(
isinstance(snake_case__ , snake_case_... | 41 |
def __lowerCamelCase ( snake_case__ ) -> int:
"""simple docstring"""
if not isinstance(snake_case__ ,snake_case__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
_SCREAMING_SNAKE_CASE =... | 306 | 0 |
from manim import *
class SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase ):
def UpperCAmelCase_ ( self : str ) -> List[Any]:
"""simple docstring"""
snake_case_ : List[Any] = Rectangle(height=0.5 , width=0.5 )
... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''... | 306 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( A__ : Dict , A__ : Any , A__ : List[str] ):
'''simple docstring'''
if len(snake_case__ ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if... | 12 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 306 | 0 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
Uppe... | 267 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(snake_case__ )
_SCREAMING_SNAKE_CASE = [[0] * n for i in range(snake_case__ )]
for i... | 306 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextCo... | 153 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wa... | 306 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A: Optional[int] = logging.get_logger(__name__)
A: Tuple = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.jso... | 109 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __UpperCAmelCase :
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor... | 306 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
# See all Donut models at https://hu... | 66 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 306 | 0 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate... | 299 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class __UpperCAmelCase :
def __init__( self: List[str] , UpperCAmelCase_: Dict ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE ... | 306 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase__ = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
UpperCA... | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pi... | 306 | 0 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class a :
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
if dst_width < 0 or dst_height < 0:
raise ValueError(... | 220 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 306 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCamelCase = TypeVar("T")
class __lowercase (Generic[T] ):
def __init__( self , A_ ) ->List[str]:
'''simple docstring'''
__lowerCAmelCase... | 275 |
# 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... | 306 | 0 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_ut... | 41 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __lowerCamelCase ( ) -> tuple[list[int], int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [randint(-10_00 ... | 306 | 0 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_... | 327 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
i... | 306 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = '... | 12 |
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 (
ProphetNetForConditionalG... | 306 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase__ ( nn.Module ):
"""simple docstring"""
lowerCAmelCase__ = 42
lowerCAmelCase__ = jnp.floataa
def UpperCAmelCase__ ( self : Optional[int] ... | 267 |
from __future__ import annotations
def __lowerCamelCase ( snake_case__ ,snake_case__ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = len(snake_case__ ) - 1
while i < j:
... | 306 | 0 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig... | 153 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling... | 306 | 0 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A: Optional[int] ... | 109 |
def __lowerCamelCase ( snake_case__ ) -> list:
"""simple docstring"""
def merge(snake_case__ ,snake_case__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[... | 306 | 0 |
"""simple docstring"""
from __future__ import annotations
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Dict = 0
snake_case_ :int = len(snake_case__ ) - 1
while i < j:
if nums[i] + nums[j] == target:
ret... | 66 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
... | 306 | 0 |
def A__ ( __lowerCamelCase = 1_00 ):
SCREAMING_SNAKE_CASE_ = n * (n + 1) * (2 * n + 1) / 6
SCREAMING_SNAKE_CASE_ = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 299 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCamelCase ( snake_case__ ,snake_case__ ... | 306 | 0 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is... | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 306 | 0 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 220 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mas... | 306 | 0 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 275 |
import random
def __lowerCamelCase ( snake_case__ ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = num - 1
_SCREAMING_SNAKE_CASE = 0
while s % 2 == 0:
_SCREAMING_SNAKE_CASE = s // 2
... | 306 | 0 |
'''simple docstring'''
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...token... | 41 |
def __lowerCamelCase ( snake_case__ ) -> int:
"""simple docstring"""
if not isinstance(snake_case__ ,snake_case__ ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
_SCREAMING_SNAKE_CASE =... | 306 | 0 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 327 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''... | 306 | 0 |
import string
def lowerCamelCase__ ( A__ : Optional[int] ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
__lowerCamelCase = """"""
for symbol in message:
if symbol in string.ascii_... | 12 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 306 | 0 |
'''simple docstring'''
def a__ ( a__ = 2_00 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
__SCREAMING_SNAKE_CASE = [0] * (pence + 1)
__SCREAMING_SNAKE_CASE = 1 # base case: 1 way to make 0 pence
for co... | 267 |
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(snake_case__ )
_SCREAMING_SNAKE_CASE = [[0] * n for i in range(snake_case__ )]
for i... | 306 | 0 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ):
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
UpperCamelCase = F"Expected string as input, found {type(snake_case__ )}"
raise ValueError(snake... | 153 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wa... | 306 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def a_ ( _A ) -> i... | 307 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""Jukeb... | 307 | 1 |
import os
import numpy
import onnx
def a_ ( _A , _A ) -> List[Any]:
"""simple docstring"""
snake_case__ = a.name
snake_case__ = b.name
snake_case__ = ''
snake_case__ = ''
... | 307 |
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_d... | 307 | 1 |
from math import factorial
__UpperCamelCase : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def a_ ( _A ) -> int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise TypeError('Parameter number... | 307 |
import random
from typing import Any
def a_ ( _A ) -> list[Any]:
"""simple docstring"""
for _ in range(len(_A ) ):
snake_case__ = random.randint(0 , len(_A ) - 1 )
snake_case__ = random.randin... | 307 | 1 |
def a_ ( ) -> int:
"""simple docstring"""
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(_A , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__"... | 307 |
class __SCREAMING_SNAKE_CASE( a_ ):
pass
class __SCREAMING_SNAKE_CASE( a_ ):
pass
class __SCREAMING_SNAKE_CASE:
def __init__( self: List[str] ) -> Union[str, Any]:
snake_case__ = [
[],
... | 307 | 1 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert imp... | 307 |
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 __SCREAMING_SNAKE_CASE( a_ ... | 307 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 307 |
def a_ ( _A = 1000 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , _A ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 307 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : int = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""... | 307 |
import os
def a_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case__ = os.path.join(os.path.dirname(_A ) , 'num.txt' )
with open(_A ) as file_hand:
return str(sum(int(_A ) for line in file_hand ) )[:10... | 307 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 307 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __SCREAMING_SNAKE_CASE( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
_Uppe... | 307 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
__UpperC... | 307 |
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.war... | 307 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.te... | 307 |
import os
import string
import sys
__UpperCamelCase : List[Any] = 1 << 8
__UpperCamelCase : Union[str, Any] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_... | 307 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech... | 307 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama... | 307 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__UpperCamelCase : Any = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < ... | 307 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__UpperCamelCase : int = 299792458
# Symbols
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Optional[int] = symbols... | 307 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__UpperCamelCase : List[Any] = logging.g... | 307 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : Any = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__UpperCamelCase : List... | 307 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __SCREAMING_SNAKE_CASE( a_... | 307 |
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 TextInput
fr... | 307 | 1 |
def a_ ( _A , _A ) -> Any:
"""simple docstring"""
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
snake_case__ = (boundary[1] - boundary[0]) / steps
snake_case__ = boundary[0]
snake_case... | 307 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import... | 307 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def a_ ( _A ) -> bool:
"""simple docstring"""
snake_case__ = int(number**0.5 )
return number == sq * sq
def a_ ( _A , _A ... | 307 |
from math import isclose, sqrt
def a_ ( _A , _A , _A ) -> tuple[float, float, float]:
"""simple docstring"""
snake_case__ = point_y / 4 / point_x
snake_case__ = 2 * normal_gradient / (1 + normal_gradient * normal_... | 307 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : Any =... | 307 |
# 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
clas... | 307 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : str = {
"""huggingface/informer-tourism-monthly""": (
... | 307 |
import doctest
from collections import deque
import numpy as np
class __SCREAMING_SNAKE_CASE:
def __init__( self: Dict ) -> None:
snake_case__ = [2, 1, 2, -1]
snake_case__ = [1, 2, 3, 4]
def lowerCAmelCa... | 307 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_util... | 307 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def a_ ... | 307 | 1 |
def a_ ( _A ) -> list:
"""simple docstring"""
if len(_A ) < 2:
return collection
def circle_sort_util(_A , _A , _A ) -> bool:
snake_case__ = False
if low == high:
return swap... | 307 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCamelCase : Tuple = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""Patc... | 307 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
__UpperCamelCase : Union[str, Any] = {
"""speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/... | 307 |
def a_ ( _A , _A ) -> int:
"""simple docstring"""
return 1 if input_a == input_a else 0
def a_ ( ) -> None:
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) =... | 307 | 1 |
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