code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import numpy as np
_snake_case = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class ... | 611 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_e... | 611 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case = {
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tokenization_transfo_xl""": ["""Tra... | 611 |
_snake_case = [
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def _A ( __magic_name_... | 611 | 1 |
from __future__ import annotations
def _A ( __magic_name__ , __magic_name__ ):
if b == 0:
return (1, 0)
((lowercase__) , (lowercase__)) = extended_euclid(__magic_name__ , a % b )
lowercase__ = a // b
return (y, x - k * y)
def _A... | 611 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 611 | 1 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAme... | 611 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[int] , *_lowercase :Tuple , ... | 611 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available()... | 611 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM... | 611 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer"""],
}
try:
if not is_torc... | 611 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """... | 611 | 1 |
def _A ( __magic_name__ = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 611 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 611 | 1 |
# Algorithm for the pigeonhole sorting
def _A ( __magic_name__ ):
lowercase__ = min(__magic_name__ ) # min() finds the minimum value
lowercase__ = max(__magic_name__ ) # max() finds the maximum value
lowercase__ = max_val - min_val + 1 # size is diff... | 611 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 611 | 1 |
def _A ( __magic_name__ , __magic_name__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove the leading "0b"
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove t... | 611 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ , __magic_name__ , ... | 611 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
_snake_case = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.Image.Resampling.BILINEAR,... | 611 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import B... | 611 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers... | 611 |
from __future__ import annotations
def _A ( __magic_name__ ):
lowercase__ = 2
lowercase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__magic_name__ )
if n > 1:
factors.append(__magic_name__ )
... | 611 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"""configuration_xlm_roberta""": [
... | 611 |
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,
require_... | 611 | 1 |
from sklearn.metrics import mean_squared_error
import datasets
_snake_case = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofe... | 611 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase ( lowercase_ ):
__lowerC... | 611 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_snake_case = """src/transformers"""
# This is to make sure the trans... | 611 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""... | 611 | 1 |
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
_snake_case = logging.getLogger(__name__)
@dataclass
class ... | 611 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case = logging.get_logger("""transformers.models.speecht5""")
def _A ( __magic_name__ , __magic_name__ , __magic_n... | 611 | 1 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def _A ( ... | 611 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1024 ):
lowercase__ , lowercase__ = [], []
lowercas... | 611 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json... | 611 |
# Algorithm for the pigeonhole sorting
def _A ( __magic_name__ ):
lowercase__ = min(__magic_name__ ) # min() finds the minimum value
lowercase__ = max(__magic_name__ ) # max() finds the maximum value
lowercase__ = max_val - min_val + 1 # size is diff... | 611 | 1 |
from __future__ import annotations
from collections import namedtuple
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = namedtuple("result" , "name value" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("Onl... | 611 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...ima... | 611 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _A ( ):
lowercase__ = ArgumentParser("Diffusers CLI tool" , usage="diffusers-cli <command> [<args>]" )
lowercase__ = parser.add_subparsers(help="diffusers-cli command helpers" )
# R... | 611 |
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
from jax import jit
from transforme... | 611 | 1 |
def _A ( __magic_name__ ):
lowercase__ = []
for data in source_data:
for i, el in enumerate(__magic_name__ ):
if len(__magic_name__ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(__magic_name__ ) )
return data_lists
def _A ( ... | 611 |
_snake_case = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_snake_case = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = True
lowercase__ = []
for ne... | 611 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils imp... | 611 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""YituTech/conv-bert-base""": """https://huggingface.... | 611 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """... | 611 |
def _A ( __magic_name__ , __magic_name__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove the leading "0b"
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove t... | 611 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""YituTech/conv-bert-base""": """https://huggingface.... | 611 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_e... | 611 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_snake_case = logging.getLogger()
@unittest.skip('Temporarily disable the doc tests.' )
@... | 611 |
_snake_case = [
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def _A ( __magic_name_... | 611 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT models at https://huggingface.co... | 611 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 611 | 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_format,
)
... | 611 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[int] , *_lowercase :Tuple , ... | 611 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""google/bit-50""": """https://huggingface.co/go... | 611 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM... | 611 | 1 |
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_snake_case = logging... | 611 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """... | 611 | 1 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
... | 611 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 611 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
_snake_case = logging.getLogger(__name__)
_snake_case = 50 # max width of layer n... | 611 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 611 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = (DDPMParallelScheduler,)
def UpperCAmelCase ( self :str , **_lowercase :Optional... | 611 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ , __magic_name__ , ... | 611 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
_snake_case = """src/transformers"""
_snake_case = """docs/source/e... | 611 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import B... | 611 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfig""",
],
}
t... | 611 |
from __future__ import annotations
def _A ( __magic_name__ ):
lowercase__ = 2
lowercase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__magic_name__ )
if n > 1:
factors.append(__magic_name__ )
... | 611 | 1 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_snake_case = namedtuple(
"""_TestCommandArgs""",
[
"""dataset""... | 611 |
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,
require_... | 611 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_snake_case = _LazyModu... | 611 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase ( lowercase_ ):
__lowerC... | 611 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blend... | 611 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""... | 611 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@... | 611 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case = logging.get_logger("""transformers.models.speecht5""")
def _A ( __magic_name__ , __magic_name__ , __magic_n... | 611 | 1 |
def _A ( __magic_name__ = 1000 ):
lowercase__ = 2**power
lowercase__ = 0
while n:
lowercase__ , lowercase__ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 611 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1024 ):
lowercase__ , lowercase__ = [], []
lowercas... | 611 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...ima... | 611 |
# Algorithm for the pigeonhole sorting
def _A ( __magic_name__ ):
lowercase__ = min(__magic_name__ ) # min() finds the minimum value
lowercase__ = max(__magic_name__ ) # max() finds the maximum value
lowercase__ = max_val - min_val + 1 # size is diff... | 611 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""shi-labs/nat-mini-in1k-224""": """https://hugg... | 611 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...ima... | 611 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_snake_case = 4
_snake_case = 3
class lowerCAmelCase ( lowercase_ ):
... | 611 |
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
from jax import jit
from transforme... | 611 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import ... | 611 |
_snake_case = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_snake_case = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = True
lowercase__ = []
for ne... | 611 | 1 |
from maths.prime_check import is_prime
def _A ( __magic_name__ ):
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__magic_name__ )
if is_prime(__magic_name__ ) and is_pr... | 611 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""YituTech/conv-bert-base""": """https://huggingface.... | 611 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common... | 611 |
def _A ( __magic_name__ , __magic_name__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove the leading "0b"
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove t... | 611 | 1 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
_snake_case = logging.getLogger(__name__)
if __name__ == "__main__":
_snake_c... | 611 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_e... | 611 | 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 lowerCAmelCase ( lowercase_ , unittest... | 611 |
_snake_case = [
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def _A ( __magic_name_... | 611 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 611 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 611 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 611 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[int] , *_lowercase :Tuple , ... | 611 | 1 |
def _A ( __magic_name__ , __magic_name__ ):
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.tes... | 611 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM... | 611 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=_lowerCamelCase ):
__lowerCamelCase = ['transformers', 'torch', 'note_seq']
def __init__( self :str , *_lowercase :str , **_lowercase :Union[str, Any] ... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """... | 611 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ ):
if isinstance(__A , np.ndarray ):
return list(tensor.shape )
lowercase__ = ... | 701 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 611 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=snake_case__ ):
__lowerCamelCase = ["""torch""", """transformers""", """onnx"""]
def __init__( self :Union[str, Any] , *_lowercase :str , **_lowercase :... | 702 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 611 | 0 |
class lowerCAmelCase :
def __init__( self :Any , _lowercase :Tuple , _lowercase :str , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = None
lowercase__ = None
lowercase... | 703 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ , __magic_name__ , ... | 611 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_snake_case = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', '''weig... | 704 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import B... | 611 | 0 |
'''simple docstring'''
from typing import List
import numpy as np
def _A ( __magic_name__ ):
lowercase__ = {key: len(_A ) for key, value in gen_kwargs.items() if isinstance(_A , _A )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
... | 705 |
from __future__ import annotations
def _A ( __magic_name__ ):
lowercase__ = 2
lowercase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__magic_name__ )
if n > 1:
factors.append(__magic_name__ )
... | 611 | 0 |
_snake_case = 8.31_44_62 # Unit - J mol-1 K-1
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volum... | 706 |
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,
require_... | 611 | 0 |
import re
import subprocess
import sys
_snake_case = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
_snake_case = (
subprocess.check_output(F"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()
)
_snake_case ... | 707 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase ( lowercase_ ):
__lowerC... | 611 | 0 |
def _A ( __magic_name__ ):
lowercase__ = [0] * len(__A )
lowercase__ = []
lowercase__ = [1] * len(__A )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__A ) ):
if indegree[i] ==... | 708 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""... | 611 | 0 |
'''simple docstring'''
def _A ( __magic_name__ ):
lowercase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _A ( __magic_name__ ):
lowercase__ = 0
while number > 0:
lowercase__ = number % 10
sum_... | 709 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case = logging.get_logger("""transformers.models.speecht5""")
def _A ( __magic_name__ , __magic_name__ , __magic_n... | 611 | 0 |
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,
BartForSequenceClassification,
Da... | 710 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1024 ):
lowercase__ , lowercase__ = [], []
lowercas... | 611 | 0 |
from __future__ import annotations
from fractions import Fraction
def _A ( __magic_name__ , __magic_name__ ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def _A ( __magic_name__ ):
lowercase__ = []
lowerca... | 711 |
# Algorithm for the pigeonhole sorting
def _A ( __magic_name__ ):
lowercase__ = min(__magic_name__ ) # min() finds the minimum value
lowercase__ = max(__magic_name__ ) # max() finds the maximum value
lowercase__ = max_val - min_val + 1 # size is diff... | 611 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( UpperCamelCase_ ):
__lowerCamelCase = (EulerDiscreteScheduler,)
__lowerCamelCase = 10
def ... | 712 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...ima... | 611 | 0 |
_snake_case = {
"""km/h""": 1.0,
"""m/s""": 3.6,
"""mph""": 1.60_93_44,
"""knot""": 1.8_52,
}
_snake_case = {
"""km/h""": 1.0,
"""m/s""": 0.2_77_77_77_78,
"""mph""": 0.6_21_37_11_92,
"""knot""": 0.5_39_95_68_03,
}
def _A ( __magic_n... | 713 |
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
from jax import jit
from transforme... | 611 | 0 |
'''simple docstring'''
def _A ( __magic_name__ ):
if not head:
return True
# split the list to two parts
lowercase__ , lowercase__ = head.next, head
while fast and fast.next:
lowercase__ = fast.next.next
lowercase__ = slow... | 714 |
_snake_case = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_snake_case = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = True
lowercase__ = []
for ne... | 611 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
_snake_case = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _A ( ):
lowercase__ = os.path.dirname(os.path.realpath(__A ) )
lowercase__ = os.path.join(__A , "words.txt" )
lowe... | 715 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""YituTech/conv-bert-base""": """https://huggingface.... | 611 | 0 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 716 |
def _A ( __magic_name__ , __magic_name__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove the leading "0b"
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove t... | 611 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCAmelCase :
def __init__( self :Tuple , _lowercase :Any=2 , _lowercase :str=3 , _lowercase :Un... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_e... | 611 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
# See all M-CTC-T models at https://h... | 718 |
_snake_case = [
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def _A ( __magic_name_... | 611 | 0 |
import math
def _A ( __magic_name__ ):
lowercase__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_A )
def _A ( __magic_name__ = 1 / 1_2345 ):
lowercase__ = 0
lowercase__ = 0
lowercase__ = ... | 719 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 611 | 0 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urll... | 720 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[int] , *_lowercase :Tuple , ... | 611 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase = (KDPMaDiscreteScheduler,)
__lowerCamelCase = 10
def... | 721 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM... | 611 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """... | 611 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"""configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
"""tokenization_biogpt""": ["""BioGptToke... | 701 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 611 | 0 |
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def _A ( ):
print(sum_of_series(1 , 1 , 10... | 702 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 611 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {""... | 703 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ , __magic_name__ , ... | 611 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get... | 704 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import B... | 611 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _A ( __mag... | 705 |
from __future__ import annotations
def _A ( __magic_name__ ):
lowercase__ = 2
lowercase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__magic_name__ )
if n > 1:
factors.append(__magic_name__ )
... | 611 | 0 |
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_infos.json"],
["... | 706 |
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,
require_... | 611 | 0 |
from PIL import Image
def _A ( __magic_name__ , __magic_name__ ):
def brightness(__magic_name__ ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.point(__sna... | 707 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCAmelCase ( lowercase_ ):
__lowerC... | 611 | 0 |
import os
from math import logaa
def _A ( __magic_name__ = "base_exp.txt" ):
lowercase__ = 0
lowercase__ = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(UpperCAmelCase__ ) , UpperCAmelCase__ ) ) ):
lowercase__ = ... | 708 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""... | 611 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = 100 , ):
lowercase__ = x_start
lowercase__ = ... | 709 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case = logging.get_logger("""transformers.models.speecht5""")
def _A ( __magic_name__ , __magic_name__ , __magic_n... | 611 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class lowerCAmelCase ( UpperCAmelCase__ ):
__lowerCamelCase = field(default=... | 710 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=1024 ):
lowercase__ , lowercase__ = [], []
lowercas... | 611 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT... | 711 |
# Algorithm for the pigeonhole sorting
def _A ( __magic_name__ ):
lowercase__ = min(__magic_name__ ) # min() finds the minimum value
lowercase__ = max(__magic_name__ ) # max() finds the maximum value
lowercase__ = max_val - min_val + 1 # size is diff... | 611 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
'tokenizati... | 712 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...ima... | 611 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import lo... | 713 |
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
from jax import jit
from transforme... | 611 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_snake_case = ["""small""", """medium""", """large"""]
_snake_case = """lm_head.decoder.weight"""
_snake_case = """lm_head.weight"""
def _A ( __magic_name__ ... | 714 |
_snake_case = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_snake_case = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = True
lowercase__ = []
for ne... | 611 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase__ ):
__lowerCamelCase = """timm_backbone"""
def __init__( self :Any , _lowercase :... | 715 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""YituTech/conv-bert-base""": """https://huggingface.... | 611 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 716 |
def _A ( __magic_name__ , __magic_name__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove the leading "0b"
lowercase__ = str(bin(__magic_name__ ) )[2:] # remove t... | 611 | 0 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class lowerCAmelCase ( UpperCamelCase__ ):
# to overwrite at feature extractactor spe... | 717 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_e... | 611 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
e... | 718 |
_snake_case = [
(1000, """M"""),
(900, """CM"""),
(500, """D"""),
(400, """CD"""),
(100, """C"""),
(90, """XC"""),
(50, """L"""),
(40, """XL"""),
(10, """X"""),
(9, """IX"""),
(5, """V"""),
(4, """IV"""),
(1, """I"""),
]
def _A ( __magic_name_... | 611 | 0 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case ... | 719 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 611 | 0 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironmen... | 720 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[int] , *_lowercase :Tuple , ... | 611 | 0 |
from __future__ import annotations
from typing import TypedDict
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
__lowerCamelCase = 42
def _A ( __magic_name__ ) -> Optional[int]:
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
... | 721 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM... | 611 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""uclanlp/visualbert-vqa-pre""": """... | 611 | 0 |
class lowerCAmelCase : # Public class to implement a graph
def __init__( self :int , _lowercase :int , _lowercase :int , _lowercase :list[list[bool]] ):
'''simple docstring'''
lowercase__ = row
l... | 701 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 611 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER,... | 702 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 611 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class lowerCAmelCase ( a__ ... | 703 |
import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ , __magic_name__ , ... | 611 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_snake_case = logging.get_logger(__name__)
... | 704 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import B... | 611 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_snake_case = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pa... | 705 |
from __future__ import annotations
def _A ( __magic_name__ ):
lowercase__ = 2
lowercase__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(__magic_name__ )
if n > 1:
factors.append(__magic_name__ )
... | 611 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
Vil... | 706 |
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,
require_... | 611 | 0 |
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