code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase__ = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
... | 86 |
import unittest
from transformers import 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 ModelTesterMixin, ids_tensor
from... | 9 | 0 |
from __future__ import annotations
from collections.abc import Generator
def UpperCAmelCase__ ( ):
lowercase :dict[int, int] = {}
lowercase :List[Any] = 2
while True:
lowercase :Dict = factor_map.pop(lowerCamelCase, lowerCamelCase )
if factor:
lower... | 158 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_Uppe... | 158 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class _a :
_a : Dict = None
def UpperCAmelCase__( self : Union[str, Any] )-> int:
lowerCAmelCase__ : str =... | 131 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a ( _lowercase):
_a : Union[str, Any] = ['''image_processor''', '''tokenizer''']
_a : List[Any] = '''ChineseCLIPImageProcess... | 131 | 1 |
import warnings
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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'nvidia/segforme... | 194 |
from __future__ import annotations
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : List[Any] = str(SCREAMING_SNAKE_CASE__ )
return len(SCREAMING_SNAKE_CASE__ ) == 9 and set(SCREAMING_SNAKE_CASE__ ) == set('123456789' )
def UpperCamelCase__ ... | 194 | 1 |
snake_case_ = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 1000000,
"gigajoule": 1000000000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 3600000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 4186800.00,
"electronvolt"... | 24 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_pr... | 24 | 1 |
'''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
lowercase__ : ... | 359 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
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 ... | 287 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_av... | 8 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
snake_case_ = []
if len(SCREAMING_SNAKE_CASE__ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):
snake_case_ = nums.pop(0 )
snake_case_ ... | 8 | 1 |
'''simple docstring'''
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
fr... | 220 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_t... | 220 | 1 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MO... | 18 | from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a__ ( yaml.SafeLoader ):
def __UpperCamelCase ( self : str,_A : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_... | 18 | 1 |
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : Dict , __UpperCamelCase : str ):
"""simple docstring"""
__UpperCamelCase =(boundary[1] - boundary[0]) / steps
__UpperCamelCase =boundary[0]
__UpperCamelCase =boundary[1]
__UpperCa... | 360 | """simple docstring"""
def lowerCAmelCase (__UpperCamelCase : str ):
"""simple docstring"""
__UpperCamelCase =0
# if input_string is "aba" than new_input_string become "a|b|a"
__UpperCamelCase =''''''
__UpperCamelCase =''''''
# append eac... | 85 | 0 |
'''simple docstring'''
from collections import namedtuple
A__: List[Any] = namedtuple('''from_to''', '''from_ to''')
A__: Union[str, Any] = {
'''cubicmeter''': from_to(1, 1),
'''litre''': from_to(0.001, 1000),
'''kilolitre''': from_to(1, 1),
... | 276 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowerCAmelCase_ = logging.getLogger(__name__)
@dataclass
class ... | 279 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class UpperCAmelCase_ ( A__):
lowerCamelCase__ ... | 351 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ... | 300 | 0 |
from __future__ import annotations
from typing import Any
class UpperCAmelCase :
def __init__(self : str , snake_case__ : int ) -> None:
'''simple docstring'''
snake_case : str = num_of_nodes
snake_case :... | 59 |
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 FlaxTimestepEmbed... | 59 | 1 |
import argparse
import struct
import unittest
class lowercase :
'''simple docstring'''
def __init__(self , __a ) -> None:
"""simple docstring"""
UpperCAmelCase__ = data
# Initialize hash values
UpperCAmelCase__ = [
... | 335 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 335 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
... | 56 |
'''simple docstring'''
import re
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
snake_case_ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase, __U... | 56 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Tuple = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try... | 360 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a_ ( __lowercase : np.ndarray , __lowercase : np.ndarray , __lowercase : np.ndarray , __lowercase : int , __lowercase : int ) -> np.ndarray:
_snake_case ... | 130 | 0 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 152 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( lowerCamelCase ):
snake_case_ = ["""image_processor""", """tokenizer"""]
snake_case_ ... | 152 | 1 |
'''simple docstring'''
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> float:
return round(float(moles / volume ) * nfactor )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> float:
return round(float((mo... | 338 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
def __init__( self ) -> int:
UpperCAmelCase : str = (0, 0)
UpperCAmelCase : Union[str, Any] = None
UpperCAmelCase : Any = 0
UpperCAmelC... | 338 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def A_ ( _lowerCAmelCase ) -> Optional[int]:
if "cls_token" in name:
UpperCamelCase : Union[str, Any] = name.replace("cls_toke... | 52 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 0 |
'''simple docstring'''
def UpperCamelCase ( a = 1000 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 98 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 1 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = ... | 86 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
lowerCamelCase__ = """us-east-1""" # defaults region
@dataclass
class A__ :
A_ : str
A_ : Union[str, Any] = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
A_ : ... | 86 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ):
A__ = len(UpperCAmelCase_ )
A__ = sum(UpperCAmelCase_ )
A__ = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in rang... | 69 |
"""simple docstring"""
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,
EulerAncestralDiscreteScheduler,
LMSD... | 69 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = ["""image_processor... | 96 | """simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrog... | 256 | 0 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaX... | 7 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
snake_case_ : Dict = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr, ... | 7 | 1 |
'''simple docstring'''
from __future__ import annotations
def a_ ( __snake_case : list[list[int]] ) -> bool:
"""simple docstring"""
lowerCamelCase_ =len(__snake_case )
# We need to create solution object to save path.
lowerCamelCa... | 75 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
__UpperCAmelCase : Optional[int] ... | 111 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
_lowerCamelCase : Dict = {
"susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
... | 249 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
UpperCamelCase = len(A__ ) // 2
# choose the middle 3 elements
UpperCamelCase = lst[m - 1 : m + 2]
... | 249 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor,... | 278 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenize... | 326 | 0 |
from typing import Any
class _lowercase :
"""simple docstring"""
def __init__(self , lowerCamelCase_ ):
"""simple docstring"""
a = data
a = None
def __repr__(self ):
"""simple docstring"""
return F'''Node({self.data})'''
clas... | 350 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 71 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Any =DownBlockaD... | 302 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCamelCase__ = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albert-large-v1""": """https://huggin... | 302 | 1 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked bef... | 307 |
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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase : Union[str, Any] =logging.get_logger(__name__)
__lowerCAmelCase : Dict ={
'google/bit-50': 'https://h... | 9 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "bert-generation"
def __init__( self , __A=5_0358 , __A=1024 , __A=24 , __A=16 ... | 84 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLA... | 351 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
a =logging.get_logger(__name__)
def SCREAMING_SNAK... | 113 | 0 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from ... | 103 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(import... | 321 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ ( metaclass=lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""torch""", """torchsde"""]
def __init__( ... | 289 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mod... | 289 | 1 |
from __future__ import annotations
__UpperCAmelCase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
... | 119 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...... | 119 | 1 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common ... | 140 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
class A__ ( __snake_case ):
_UpperCAmelCase :List[Any] = 'timm_backbone'
def __init__( self , A_=None , ... | 140 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class __magic_name__ ( _UpperCamelCase ):
... | 89 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/lice... | 89 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 8 |
'''simple docstring'''
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 UpperCAmelCase ( lowerCamelCase_ :Dict , ... | 8 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
... | 180 | import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase :Tuple = JukeboxTokenizer
lowerCamelCase :str = {
'''artist... | 180 | 1 |
class _snake_case :
def __init__( self: Union[str, Any] , __lowerCamelCase: str = "" , __lowerCamelCase: bool = False ) -> None:
# Mapping from the first character of the prefix of the node
__UpperCAmelCase : dict[str, RadixNode] = ... | 342 | import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _UpperCamelCase ( snake_case__ ) -> Tupl... | 342 | 1 |
"""simple docstring"""
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 ..... | 86 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class A__ ( _lowerCamelCase):
A_ : Any = ['image_processor', 'tokenizer']
A_ : Optional[Any] = 'AutoImageProcessor'
A_ : ... | 86 | 1 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
fr... | 363 | """simple docstring"""
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_m... | 149 | 0 |
def __lowerCamelCase ( lowerCamelCase__ : list[list[int]] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : set ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase = len(lowerCamelCase__ ), len(grid[0] )
i... | 252 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : List[str] = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig... | 252 | 1 |
"""simple docstring"""
def _a ( lowerCamelCase: List[Any] ) -> str:
'''simple docstring'''
stooge(_snake_case , 0 , len(_snake_case ) - 1 )
return arr
def _a ( lowerCamelCase: List[Any] , lowerCa... | 369 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=_lowerCamelCase ):
lowerCAmelCase__ = ["""speech"""]
def __init__(self :Optional[int] , *_UpperCamelCase :int , **_UpperCamelCase :List[str] )-> List[str]:... | 250 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : Tuple = """bert-generation"""
def __init__( self , lowerCAmelCase=5_03_58 , lowerCAmelCase=10_24 ... | 150 | """simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conve... | 150 | 1 |
"""simple docstring"""
from __future__ import annotations
def _UpperCAmelCase ( __lowerCamelCase : list[int] , __lowerCamelCase : int ) -> list[int]:
_snake_case = 0
_snake_case = len(__lowerCamelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 40 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
__a = 42
__a = 42
class lowerCAmelCase__ :
... | 40 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCond... | 21 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
lowerCamelCase__ : Optional[int] = [
'encoder.version',
'decoder.version',
... | 50 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional ... | 235 |
from math import ceil
def lowerCamelCase__ ( _lowercase = 1001 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
UpperCAmelCase_ : List[Any] = 2 * i +... | 235 | 1 |
"""simple docstring"""
import os
import string
import sys
__lowercase = 1 << 8
__lowercase = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""... | 40 |
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCAmelCase , int(b / 2 ) ) * actual_power(UpperCAmelCase , int(b / 2 ) )
else:
r... | 142 | 0 |
"""simple docstring"""
from math import sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = 0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE... | 366 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {'''vocab_file''': '''vocab.json'''}
__Upp... | 38 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : Union[str, Any] ) -> list[list[int]]:
"""simple docstring"""
A__ : int =[]
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__... | 134 |
from math import isqrt
def _lowerCamelCase( lowercase__ ) -> bool:
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(lowercase__ ) + 1 ) )
def _lowerCamelCase( lowercase__ = 1_0**6 ) -> int:
'''simple docstring'''
__... | 295 | 0 |
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a :Union[str, Any] = get_tests_dir("fixtu... | 56 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
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_common import... | 56 | 1 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__lowerCamelCase : Any = [
'''kernels/rwkv/wkv_cuda.cu''',
'''kernels/rwkv/wkv_op.cpp''',
'''kernels/deformable_detr/ms_deform_attn.h''',
'''kernels/deformable_detr/cuda/ms_defo... | 219 |
'''simple docstring'''
from __future__ import annotations
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[True] * limit
__lowercase =False
__lowercase =False
__lowercase =True
for i in range(3 , int(... | 166 | 0 |
"""simple docstring"""
from math import factorial, pi
def UpperCAmelCase__ ( lowerCAmelCase__ :float , lowerCAmelCase__ :int = 3_0 ) -> float:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , (int, float) ):
raise ValueError("""maclaur... | 361 | """simple docstring"""
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowerCAmelCa... | 32 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase_ ... | 104 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class lowerCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__(self , *_lowerCamelCase , **_lowerCamelCase ):
"""simple docstring"""
super().__init__(*_... | 171 | 0 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that ther... | 78 | from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''google/efficient... | 78 | 1 |
'''simple docstring'''
import qiskit
def snake_case_ (_a : int , _a : int ):
UpperCAmelCase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
UpperCAmelCase = qiskit.QuantumCircuit(_a ... | 34 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructu... | 317 | 0 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'vocab_file': 'vocab.json',
'merg... | 238 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
... | 238 | 1 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase__ = "scheduler_config.json"
class lowercase_ ( lowercase ):
... | 0 | """simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import fr... | 290 | 0 |
import numpy as np
def __lowerCAmelCase ( a__ ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 350 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDepend... | 33 | 0 |
'''simple docstring'''
lowerCamelCase = range(2, 20 + 1)
lowerCamelCase = [10**k for k in range(ks[-1] + 1)]
lowerCamelCase = {}
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple doc... | 166 |
'''simple docstring'''
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, BlipaProc... | 166 | 1 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformer... | 226 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConf... | 226 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : int = {
'''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}
try:
if not is_torch_available():
r... | 338 | import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( UpperCamelCase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = (DDPMScheduler,)
def SCREAMING_SNAKE_CASE_ ( self , **__SCREA... | 338 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"configuration_data2vec_text": [
... | 350 |
'''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCamelCase_ = {
... | 111 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ = {
"""configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
... | 317 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a__ = {
"""configuration_wav2vec2""": ["""WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Wav2Vec2Config"""],
... | 317 | 1 |
def UpperCamelCase__( UpperCamelCase__ : Tuple )->bool:
if num < 0:
return False
A__ = num
A__ = 0
while num > 0:
A__ = rev_num * 10 + (num % 10)
num //= 10
return num_copy ... | 368 |
def UpperCamelCase__( UpperCamelCase__ : int = 1_00 )->int:
A__ = (n * (n + 1) // 2) ** 2
A__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F"{solution() = }")
| 39 | 0 |
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_seed
from accelerate import ... | 14 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_UpperCAmelCase : Optional[Any]... | 50 | 0 |
from ..utils import DummyObject, requires_backends
class a ( metaclass=_A ):
'''simple docstring'''
lowerCAmelCase : List[str] = ['flax', 'transformers']
def __init__( self : Tuple , *__snake_case : str , **__s... | 177 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'facebook/data2vec-text... | 177 | 1 |
import re
import subprocess
import sys
a_ : Tuple = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
a_ : Tuple = (
subprocess.check_output(f"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode('utf-8').split()
)
a_ : ... | 137 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Tuple = logging.get_logger(__name__)
lowercase__ : Any = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface... | 224 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Any =logging.get_logger(__name__)
A__ : Dict ={
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/confi... | 354 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
... | 220 | 0 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 ) ->Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ :Union[str, Any] = length or len(_a )
lowerCAmelCase__ :List[str] = False
for i in range(length - ... | 293 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not ... | 131 | 0 |
from math import factorial
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Tuple ) -> Tuple:
__lowerCAmelCase = real
if isinstance(lowerCAmelCase_ , lowerCAm... | 359 |
from functools import lru_cache
@lru_cache
def a_ ( lowerCAmelCase_ : int ):
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 207 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__lowerCamelCase : str = 100
__lowerCamelCase : Any = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__lowerCamelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if pri... | 52 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nes... | 9 | 0 |
'''simple docstring'''
def lowerCamelCase__ ( __lowerCamelCase : int = 1_0_0_0 ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =-1
_UpperCAmelCase : Optional[Any] =0
for a in range(1 , n // 3 ):
# Sol... | 363 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fl... | 242 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils imp... | 216 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import ... | 216 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> Dict:
"""simple docstring"""
UpperCamelCase_ = 0
if start < end:
Upper... | 60 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> list:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return lst
UpperCamelCase_ = 1
while i < len(SCREAMING_SNAKE_CASE_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 60 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A = logging.get_logger(__name__)
# TODO: upload to AWS
A = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retrib... | 160 |
def __lowercase ( _UpperCamelCase = 4000000 ) ->int:
"""simple docstring"""
lowercase : int = []
lowercase , lowercase : str = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCamelCase )
... | 337 | 0 |
"""simple docstring"""
__A = 6_5_5_2_1
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> int:
lowercase__: Optional[Any] = 1
lowercase__: Dict = 0
for plain_chr in plain_text:
lowercase__: Optional[int] = (a + ord(__UpperCAmelCase )) % M... | 364 | """simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A = get_tests_dir("fixtu... | 2 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_ten... | 1 | from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 180 | 0 |
def UpperCamelCase ( _A : str )-> str:
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(_A ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(rever... | 351 |
import datasets
from .evaluate import evaluate
UpperCAmelCase_ : List[Any] = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},... | 198 | 0 |
'''simple docstring'''
__lowercase = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Sat... | 272 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowercase = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_to... | 272 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logg... | 155 |
"""simple docstring"""
import math
import sys
def __lowercase ( _a ):
if number != int(_a ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a negative number''' )
if number == ... | 155 | 1 |
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__ ( snake_case__ ):
_a : List[Any] =... | 92 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE :List[Any] = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", ... | 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE :int = {'configuration_encoder_decoder': ['EncoderDecoderConfig']}
try:
if not is_torch_avai... | 124 | 0 |
'''simple docstring'''
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : int ) -> Tuple:
'''simple docstring'''
A: Optional[Any] = s... | 319 |
from pathlib import Path
import fire
def UpperCamelCase( __UpperCamelCase : str ,__UpperCamelCase : str ,__UpperCamelCase : int ):
lowerCAmelCase_ : List[str] = Path(__UpperCamelCase )
lowerCAmelCase_ : Union[str, Any] = Path(__UpperCamelCase )
d... | 103 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, 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.numpy as jnp
... | 60 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 60 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.... | 340 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a_ = (3, 9, -11, 0, 7, 5, 1, -1)
a_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class lowercase__ :
a_ =42
a_ =42
class lowercase__ :
def __init__( ... | 340 | 1 |
from math import factorial
_UpperCAmelCase = {str(d): factorial(d) for d in range(10)}
def UpperCamelCase ( __lowercase : int ):
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(__lowercase ) )
def UpperCamelCase ( ):
... | 192 | _UpperCAmelCase = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def UpperCamelCase ( ):
'''simple docstring'''
A_ : Tuple = input('Enter message: ' )
A_ : int = input('Enter key [alphanumeric]: ' )
A_ : Optional[Any] = input('Encr... | 192 | 1 |
def _a ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : list[int] ):
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
print("The following activities are selected:" )
# The first activity is always selected
__lowerCAmelC... | 92 |
def __lowercase ( a__ ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
lowerCAmelCase__ : Optional[Any] =int(input(''... | 257 | 0 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__lowercase = 299_792_458
# Symbols
__lowercase , __lowercase , __lowercase , __lowercase = symbols('''ct x y z''')
def lowerCAmelCase (__UpperCamelCas... | 369 | """simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCAmelCase (__UpperCamelCase : Tuple ):
... | 85 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_A : Union[str, Any] =False
class _lowe... | 41 |
from PIL import Image
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image , _lowerCamelCase : int) -> Image:
'''simple docstring'''
__UpperCamelCase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamel... | 232 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERendere... | 246 |
'''simple docstring'''
import math
def lowercase__( __UpperCamelCase: float ,__UpperCamelCase: float ):
"""simple docstring"""
return math.pow(__UpperCamelCase ,2 ) - a
def lowercase__( __UpperCamelCase: float ):
"""s... | 246 | 1 |
"""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... | 249 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_ac... | 249 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS... | 355 |
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... | 292 | 0 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class _snake_case ( UpperCamelCase_ ):
snake_... | 135 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 199 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__A : List[str] = logging.getLogger(__name__)
def __UpperCamelCase ( ) ->int:
"""simple docstring"""
lowerCamelCase_ ... | 371 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
def __init__( self , _SCRE... | 49 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional ... | 124 |
import re
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> str:
if len(re.findall("""[ATCG]""" ,lowercase ) ) != len(lowercase ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" ,"""TAGC""" ) )
if __name__ ==... | 124 | 1 |
"""simple docstring"""
import numpy as np
import datasets
SCREAMING_SNAKE_CASE_ = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of th... | 371 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCamelCase__ ( lowerCAmelCase... | 193 | 0 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, ... | 345 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def _lowerCAmelCase ( ) -> None:
"""simple docstring"""
assert or_gate(0 , 0 ... | 230 | 0 |
"""simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import ... | 351 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch... | 298 | 0 |
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