code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from math import factorial
lowerCamelCase__ = {str(digit): factorial(digit) for digit in range(10)}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError('Parameter number must be int' )
... | 361 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[list[int]]:
lowerCAmelCase__ : list[list[int]] = []
create_all_state(1 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , [] , SCREA... | 307 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 ) -> List[Any]:
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_shave_p... | 362 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
... | 307 | 0 |
from __future__ import annotations
from math import gcd
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 2 , SCREAMING_SNAKE_CASE_ = 1 , SCREAMING_SNAKE_CASE_ = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the a... | 363 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,... | 307 | 0 |
def lowerCAmelCase__ ( ) -> int:
return [
a * b * (1_000 - a - b)
for a in range(1 , 999 )
for b in range(SCREAMING_SNAKE_CASE_ , 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solution() =... | 364 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> str:
stooge(SCREAMING_SNAKE_CASE_ , 0 , len(SCREAMING_SNAKE_CASE_ ) - 1 )
return arr
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
... | 307 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A__ ( __magic_name__ ):
lower... | 365 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PR... | 307 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class A__ :
def __init__( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : list[Any] = []
lowerCAmelCase__ : int = ... | 366 |
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 i... | 307 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase__ = logging.get_logger(__name__)
class A__ ( __magic_name__ , __mag... | 367 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowerCamelCase__ = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7"""):
... | 307 | 0 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A__ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase =... | 368 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import U... | 307 | 0 |
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 (
ProphetNetForConditionalGeneration as Prophet... | 369 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__magic_name__ ):
lowercase = ['torch', 'transformers', 'onnx']
def __init__( self : Any , *a : Any , **a : Any ):
'''simple doc... | 307 | 0 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import r... | 370 |
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 ConfigTeste... | 307 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import Tens... | 371 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispeech-larg... | 307 | 0 |
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element i... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_availa... | 308 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 308 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
r... | 308 | 1 |
def UpperCamelCase_( _snake_case : Union[str, Any] ):
"""simple docstring"""
if collection == []:
return []
# get some information about the collection
__a =len(_snake_case )
__a =max(_snake_case )
__a =min(_snake_case ... | 308 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 308 | 1 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common im... | 308 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencep... | 308 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Dict = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],... | 308 |
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
... | 308 | 1 |
import torch
from transformers import AutoModel
class __magic_name__ ( torch.nn.Module ):
def __init__( self , __snake_case="sayef/fsner-bert-base-uncased" ) -> Optional[int]:
'''simple docstring'''
super(__snake_case , self ).__init__()
... | 308 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
... | 308 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmb... | 308 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassific... | 308 | 1 |
from math import pi, sqrt, tan
def UpperCamelCase_( _snake_case : float ):
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def UpperC... | 308 |
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] ... | 308 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
"ut/deta": "https://huggingface.co/ut/deta/resolve... | 308 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C"... | 308 | 1 |
def UpperCamelCase_( _snake_case : Dict , _snake_case : Dict , _snake_case : int , _snake_case : int ):
"""simple docstring"""
__a =[False] * len(_snake_case )
__a =[]
queue.append(_snake_case )
__a =Tr... | 308 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/... | 308 | 1 |
def UpperCamelCase_( _snake_case : float , _snake_case : list[float] ):
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be emp... | 308 |
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element i... | 308 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)... | 308 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__sn... | 308 | 1 |
_lowerCAmelCase : str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_lowerCAmelCase : Any = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCamelCase_( _snake_case : dict[int, list[int]] , _snake_case : int , _snake_case ... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 308 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | 308 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : ... | 308 | 1 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza,... | 308 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFa... | 308 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModel... | 308 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , u... | 308 | 1 |
import os
import numpy
import onnx
def UpperCamelCase_( _snake_case : Any , _snake_case : Optional[Any] ):
"""simple docstring"""
__a =a.name
__a =b.name
__a =''
__a =''
__a =a == b
__a ... | 308 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ... | 308 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , u... | 308 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CAS... | 308 | 1 |
from __future__ import annotations
_lowerCAmelCase : Dict = 8.988E9 # units = N * m^s * C^-2
def UpperCamelCase_( _snake_case : float , _snake_case : float , _snake_case : float , _snake_case : float ):
"""simple docstrin... | 308 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sag... | 308 | 1 |
from PIL import Image
def UpperCamelCase_( _snake_case : Image , _snake_case : float ):
"""simple docstring"""
def brightness(_snake_case : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
... | 308 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : ... | 308 | 1 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ... | 308 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 308 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 308 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = ... | 308 | 1 |
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
assert column_title.isupper()
__a =0
__a =len(_snake_case ) - 1
__a =0
while index >= 0:
__a =(ord(column_title[index] ) - 64) * pow(26 , _snake_cas... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_availa... | 308 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCAmelCase : Any = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# S... | 308 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
r... | 308 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils imp... | 308 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 308 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_lowerCAmelCase : Union[str, Any] = logging... | 308 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencep... | 308 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
fro... | 308 |
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
... | 308 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'CL... | 308 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
... | 308 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __magic_name__ :
pass
| 308 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassific... | 308 | 1 |
def UpperCamelCase_( _snake_case : int , _snake_case : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
__a =str(bin(_snake_case ) )[2:] # remove the leading "0b"
... | 308 |
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] ... | 308 | 1 |
import string
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
for key in range(len(string.ascii_uppercase ) ):
__a =''
for symbol in message:
if symbol in string.ascii_uppercase:
__a =s... | 308 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C"... | 308 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
fr... | 308 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/... | 308 | 1 |
from __future__ import annotations
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int , _snake_case : int ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa]) or (... | 308 |
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element i... | 308 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
Autoen... | 308 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__sn... | 308 | 1 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 308 | 1 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def UpperCamelCase_( _snake_case : Dict , _snake_case : Tupl... | 308 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : ... | 308 | 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 __magic_name__ ( tf.keras.layers.Layer ):
def _... | 308 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFa... | 308 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_lowerCAmelCase : Tuple = namedtuple("covid_data", "cases deaths recovered")
def UpperCamelCase_( _snake_case : str = "https://www.worldometers.info/coronavirus/" ):
... | 308 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , u... | 308 | 1 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def UpperCamelCase_( _snake_case : Optional[int] , _snake_case : Dict , _snake_ca... | 308 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ... | 308 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : int = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"google... | 308 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CAS... | 308 | 1 |
from __future__ import annotations
_lowerCAmelCase : str = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class __magic_name__ :
def __init__( sel... | 308 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sag... | 308 | 1 |
def UpperCamelCase_( _snake_case : int = 50 ):
"""simple docstring"""
__a =[1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 308 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : ... | 308 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import... | 308 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 308 | 1 |
def UpperCamelCase_( _snake_case : int = 10**9 ):
"""simple docstring"""
__a =1
__a =2
__a =0
__a =0
__a =0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value ... | 308 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = ... | 308 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : str = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_availa... | 308 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs... | 308 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
r... | 308 | 1 |
from __future__ import annotations
from statistics import mean
def UpperCamelCase_( _snake_case : list[int] , _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
__a =[0] * no_of_processes
__a =[0] * ... | 308 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 308 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : ... | 308 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencep... | 308 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __magic_name__ ( lowerCAmelCase_ ):
@staticmethod
@abstractmethod
def __magic_name__ ( __snake_case ) -> List[Any]:
'''simple docstring'''
raise NotImpl... | 308 |
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
... | 308 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import... | 308 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
... | 308 | 1 |
from functools import lru_cache
@lru_cache
def UpperCamelCase_( _snake_case : int ):
"""simple docstring"""
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
i... | 308 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassific... | 308 | 1 |
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = ['flax']
def __init__( self , *__snake_case , **__snake_case ) -> Any:
'''simple docstring'''
... | 308 |
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] ... | 308 | 1 |
from __future__ import annotations
def UpperCamelCase_( _snake_case : int = 4 ):
"""simple docstring"""
__a =abs(_snake_case ) or 4
return [[1 + x + y * row_size for x in range(_snake_case )] for y in range(_snake_case )]
d... | 308 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C"... | 308 | 1 |
def UpperCamelCase_( _snake_case : int = 1000000 ):
"""simple docstring"""
__a =limit + 1
__a =[0] * limit
for first_term in range(1 , _snake_case ):
for n in range(_snake_case , _snake_case , _snake_case ):
__a ... | 308 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/... | 308 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C"... | 308 |
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element i... | 308 | 1 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __magic_name__ ( tf.keras.optimizers.schedules.Learnin... | 308 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__sn... | 308 | 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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
t... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 308 | 1 |
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(100, 0.25) = }''')
print(f'''{price_plus_tax(125.50, 0.05) = }''')
... | 308 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : ... | 308 | 1 |
def UpperCamelCase_( _snake_case : list ):
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0... | 308 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFa... | 308 | 1 |
import math
def UpperCamelCase_( _snake_case : float , _snake_case : float ):
"""simple docstring"""
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initi... | 308 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , u... | 308 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def UpperCamelCase_( _snake_case : Iterable[str] , _snake_case : int ):
"""simple docstring"""
__a =iter(_snake_case )
while True:
__a =tupl... | 308 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ... | 308 | 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_f... | 308 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CAS... | 308 | 1 |
def UpperCamelCase_( _snake_case : list[list[int]] , _snake_case : int , _snake_case : int , _snake_case : list[int] ):
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next... | 308 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sag... | 308 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(Fal... | 308 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : ... | 308 | 1 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_lowerCAmelCase : List[str] = version.par... | 308 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 308 | 1 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
... | 308 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = ... | 308 | 1 |
class __magic_name__ :
def __init__( self , __snake_case ) -> Optional[Any]:
'''simple docstring'''
__a =n
__a =[None] * self.n
__a =0 # index of the first element
__a =0
__a =0
def __len__( sel... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_availa... | 308 | 1 |
from __future__ import annotations
import requests
def UpperCamelCase_( _snake_case : str ):
"""simple docstring"""
__a =F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(_snake_case ).json()
... | 308 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
r... | 308 | 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
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : ... | 308 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 308 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCAmelCase : int = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLI... | 308 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencep... | 308 | 1 |
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencep... | 308 |
def UpperCamelCase_( _snake_case : str , _snake_case : int ):
"""simple docstring"""
return [sentence[i : i + ngram_size] for i in range(len(_snake_case ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
... | 308 | 1 |
def UpperCamelCase_( _snake_case : int = 10 , _snake_case : int = 1000 , _snake_case : bool = True ):
"""simple docstring"""
assert (
isinstance(_snake_case , _snake_case )
and isinstance(_snake_case , _snake_case )
... | 308 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
... | 308 | 1 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 308 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassific... | 308 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def UpperCamelCase_( ):
"""simple docstring"""
with offline(OfflineSimul... | 308 |
from __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : List[str] = [8, 5, 9, 7]
_lowerCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCase : List[Any] ... | 308 | 1 |
_lowerCAmelCase : int = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def UpperCamelCase_( _snake_case ... | 308 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_lowerCAmelCase : Tuple = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"L": 4.03,
"C"... | 308 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowerCAmelCase_ ) ,... | 308 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/... | 308 | 1 |
from __future__ import annotations
import bisect
def UpperCamelCase_( _snake_case : list[int] , _snake_case : int , _snake_case : int = 0 , _snake_case : int = -1 ):
"""simple docstring"""
if hi < 0:
__a =len(... | 308 |
import os
def UpperCamelCase_( _snake_case : str = "input.txt" ):
"""simple docstring"""
with open(os.path.join(os.path.dirname(_snake_case ) , _snake_case ) ) as input_file:
__a =[
[int(_snake_case ) for element i... | 308 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING... | 308 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Any = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__sn... | 308 | 1 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 308 | 1 |
from __future__ import annotations
from random import choice
def UpperCamelCase_( _snake_case : Any ):
"""simple docstring"""
return choice(_snake_case )
def UpperCamelCase_( _snake_case : list[int] , _snake_case ... | 308 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : ... | 308 | 1 |
def UpperCamelCase_( _snake_case : int = 1000 ):
"""simple docstring"""
__a =2**power
__a =0
while n:
__a , __a =r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
... | 308 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFa... | 308 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassific... | 308 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , u... | 308 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 308 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class __magic_name__ ... | 308 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import ... | 308 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __magic_name__ ( nn.Module ):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CAS... | 308 | 1 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCAmelCase : Optional[Any] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC"... | 308 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sag... | 308 | 1 |
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils impo... | 308 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : ... | 308 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __magic_name__ ( pl.LightningModule ):
def __init__( self , __snake_case ) -> List[Any]:
... | 308 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 308 | 1 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def UpperCamelCase_( _snake_case : np.ndarray ):
"""simple docstring"""
return input_array.reshape((input_arr... | 308 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = ... | 308 | 1 |
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
_lowerCAmelCase : Optional[Any] = numpy.array([0, 0])
_lowerCAmelCase : Dict = numpy.array([0.5, 0.8660254])
_lowerCAmelCase : Any = ... | 308 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_availa... | 308 | 1 |
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