




'''simple docstring'''







import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        LayoutLMForMaskedLM,
        LayoutLMForQuestionAnswering,
        LayoutLMForSequenceClassification,
        LayoutLMForTokenClassification,
        LayoutLMModel,
    )

class   _snake_case:






 def __init__(self					:     List[str]						,		a					:     int						,		a					:     List[str]=13						,		a					:     int=7						,		a					:     str=True						,		a					:     Optional[Any]=True						,		a					:     Any=True						,		a					:     List[Any]=True						,		a					:     int=99						,		a					:     List[str]=32						,		a					:     Dict=5						,		a					:     Any=4						,		a					:     List[Any]=37						,		a					:     List[str]="gelu"						,		a					:     Any=0.1						,		a					:     Union[str, Any]=0.1						,		a					:     str=5_12						,		a					:     Optional[int]=16						,		a					:     Optional[int]=2						,		a					:     Union[str, Any]=0.02						,		a					:     Dict=3						,		a					:     Tuple=4						,		a					:     Union[str, Any]=None						,		a					:     Optional[int]=10_00						,		)					->      Optional[Any]:






    """simple docstring"""


    A__         =    parent
    A__         =    batch_size
    A__         =    seq_length
    A__         =    is_training
    A__         =    use_input_mask
    A__         =    use_token_type_ids
    A__         =    use_labels
    A__         =    vocab_size
    A__         =    hidden_size
    A__         =    num_hidden_layers
    A__         =    num_attention_heads
    A__         =    intermediate_size
    A__         =    hidden_act
    A__         =    hidden_dropout_prob
    A__         =    attention_probs_dropout_prob
    A__         =    max_position_embeddings
    A__         =    type_vocab_size
    A__         =    type_sequence_label_size
    A__         =    initializer_range
    A__         =    num_labels
    A__         =    num_choices
    A__         =    scope
    A__         =    range_bbox






 def 	_UpperCamelCase   (self					:     Dict						)					->      Union[str, Any]:






    """simple docstring"""


    A__         =    ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size						)

    A__         =    ids_tensor([self.batch_size, self.seq_length, 4]						,		self.range_bbox						)
    # Ensure that bbox is legal
    for i in range(bbox.shape[0]						):
       for j in range(bbox.shape[1]						):
          if bbox[i, j, 3] < bbox[i, j, 1]:
             A__         =    bbox[i, j, 3]
             A__         =    bbox[i, j, 1]
             A__         =    t
          if bbox[i, j, 2] < bbox[i, j, 0]:
             A__         =    bbox[i, j, 2]
             A__         =    bbox[i, j, 0]
             A__         =    t

    A__         =    None
    if self.use_input_mask:
       A__         =    ids_tensor([self.batch_size, self.seq_length]						,		vocab_size=2						)

    A__         =    None
    if self.use_token_type_ids:
       A__         =    ids_tensor([self.batch_size, self.seq_length]						,		self.type_vocab_size						)

    A__         =    None
    A__         =    None
    A__         =    None
    if self.use_labels:
       A__         =    ids_tensor([self.batch_size]						,		self.type_sequence_label_size						)
       A__         =    ids_tensor([self.batch_size, self.seq_length]						,		self.num_labels						)
       A__         =    ids_tensor([self.batch_size]						,		self.num_choices						)

    A__         =    self.get_config()

    return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






 def 	_UpperCamelCase   (self					:     Tuple						)					->      Any:






    """simple docstring"""


    return LayoutLMConfig(
        vocab_size=self.vocab_size						,		hidden_size=self.hidden_size						,		num_hidden_layers=self.num_hidden_layers						,		num_attention_heads=self.num_attention_heads						,		intermediate_size=self.intermediate_size						,		hidden_act=self.hidden_act						,		hidden_dropout_prob=self.hidden_dropout_prob						,		attention_probs_dropout_prob=self.attention_probs_dropout_prob						,		max_position_embeddings=self.max_position_embeddings						,		type_vocab_size=self.type_vocab_size						,		initializer_range=self.initializer_range						,		)






 def 	_UpperCamelCase   (self					:     Tuple						,		a					:     List[str]						,		a					:     List[Any]						,		a					:     Dict						,		a					:     Any						,		a					:     Optional[int]						,		a					:     List[Any]						,		a					:     str						,		a					:     Any						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    LayoutLMModel(config=a						)
    model.to(a						)
    model.eval()
    A__         =    model(a						,		a						,		attention_mask=a						,		token_type_ids=a						)
    A__         =    model(a						,		a						,		token_type_ids=a						)
    A__         =    model(a						,		a						)
    self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, self.seq_length, self.hidden_size)						)
    self.parent.assertEqual(result.pooler_output.shape						,		(self.batch_size, self.hidden_size)						)






 def 	_UpperCamelCase   (self					:     Any						,		a					:     str						,		a					:     str						,		a					:     Any						,		a					:     Any						,		a					:     Any						,		a					:     Dict						,		a					:     Optional[int]						,		a					:     int						)					->      str:






    """simple docstring"""


    A__         =    LayoutLMForMaskedLM(config=a						)
    model.to(a						)
    model.eval()
    A__         =    model(a						,		a						,		attention_mask=a						,		token_type_ids=a						,		labels=a						)
    self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.vocab_size)						)






 def 	_UpperCamelCase   (self					:     Any						,		a					:     Union[str, Any]						,		a					:     Optional[int]						,		a					:     List[str]						,		a					:     Any						,		a					:     Optional[Any]						,		a					:     str						,		a					:     Tuple						,		a					:     List[str]						)					->      Any:






    """simple docstring"""


    A__         =    self.num_labels
    A__         =    LayoutLMForSequenceClassification(a						)
    model.to(a						)
    model.eval()
    A__         =    model(
        a						,		a						,		attention_mask=a						,		token_type_ids=a						,		labels=a						)
    self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)						)






 def 	_UpperCamelCase   (self					:     Dict						,		a					:     Optional[Any]						,		a					:     Optional[int]						,		a					:     List[str]						,		a					:     Any						,		a					:     Union[str, Any]						,		a					:     Any						,		a					:     Union[str, Any]						,		a					:     Dict						)					->      Any:






    """simple docstring"""


    A__         =    self.num_labels
    A__         =    LayoutLMForTokenClassification(config=a						)
    model.to(a						)
    model.eval()
    A__         =    model(a						,		a						,		attention_mask=a						,		token_type_ids=a						,		labels=a						)
    self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.num_labels)						)






 def 	_UpperCamelCase   (self					:     Dict						,		a					:     Tuple						,		a					:     Any						,		a					:     str						,		a					:     Optional[Any]						,		a					:     int						,		a					:     List[Any]						,		a					:     Optional[Any]						,		a					:     Tuple						)					->      List[Any]:






    """simple docstring"""


    A__         =    LayoutLMForQuestionAnswering(config=a						)
    model.to(a						)
    model.eval()
    A__         =    model(
        a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						,		start_positions=a						,		end_positions=a						,		)
    self.parent.assertEqual(result.start_logits.shape						,		(self.batch_size, self.seq_length)						)
    self.parent.assertEqual(result.end_logits.shape						,		(self.batch_size, self.seq_length)						)






 def 	_UpperCamelCase   (self					:     Dict						)					->      int:






    """simple docstring"""


    A__         =    self.prepare_config_and_inputs()
    (
        (
        A__
    )     , (
        A__
    )     , (
        A__
    )     , (
        A__
    )     , (
        A__
    )     , (
        A__
    )     , (
        A__
    )     , (
        A__
    )     , 
    )         =    config_and_inputs
    A__         =    {
        'input_ids': input_ids,
        'bbox': bbox,
        'token_type_ids': token_type_ids,
        'attention_mask': input_mask,
    }
    return config, inputs_dict




@require_torch
class   _snake_case( UpperCAmelCase					,   UpperCAmelCase					,   unittest.TestCase			):
 __snake_case:       List[str]									=				(
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 __snake_case:       Dict									=				(
     {
         '''document-question-answering''': LayoutLMForQuestionAnswering,
         '''feature-extraction''': LayoutLMModel,
         '''fill-mask''': LayoutLMForMaskedLM,
         '''text-classification''': LayoutLMForSequenceClassification,
         '''token-classification''': LayoutLMForTokenClassification,
         '''zero-shot''': LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __snake_case:       str									=				True






 def 	_UpperCamelCase   (self					:     List[str]						)					->      Tuple:






    """simple docstring"""


    A__         =    LayoutLMModelTester(self						)
    A__         =    ConfigTester(self						,		config_class=a						,		hidden_size=37						)






 def 	_UpperCamelCase   (self					:     int						)					->      str:






    """simple docstring"""


    self.config_tester.run_common_tests()






 def 	_UpperCamelCase   (self					:     Dict						)					->      List[Any]:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a						)






 def 	_UpperCamelCase   (self					:     int						)					->      Any:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       A__         =    type
       self.model_tester.create_and_check_model(*a						)






 def 	_UpperCamelCase   (self					:     Union[str, Any]						)					->      str:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*a						)






 def 	_UpperCamelCase   (self					:     Dict						)					->      Dict:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*a						)






 def 	_UpperCamelCase   (self					:     Any						)					->      Optional[int]:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*a						)






 def 	_UpperCamelCase   (self					:     Any						)					->      List[str]:






    """simple docstring"""


    A__         =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*a						)






 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Optional[Any]:






    """simple docstring"""


    pass







def 	_A			(   ):






   '''simple docstring'''







   A__         =    torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]			,device=UpperCAmelCase	)  # noqa: E231
   A__         =    torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]			,device=UpperCAmelCase	)  # noqa: E231
   A__         =    torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]			,device=UpperCAmelCase	)  # noqa: E231
   A__         =    torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]			,device=UpperCAmelCase	)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   A__         =    torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]			,device=UpperCAmelCase	)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class   _snake_case( unittest.TestCase			):






 @slow
 def 	_UpperCamelCase   (self					:     Tuple						)					->      Union[str, Any]:






    """simple docstring"""


    A__         =    LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'						).to(a						)

    A__     , A__     , A__     , A__     , A__         =    prepare_layoutlm_batch_inputs()

    # forward pass
    A__         =    model(input_ids=a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						)

    # test the sequence output on [0, :3, :3]
    A__         =    torch.tensor(
        [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]						,		device=a						,		)

    self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]						,		a						,		atol=1e-3						)						)

    # test the pooled output on [1, :3]
    A__         =    torch.tensor([-0.6580, -0.0214, 0.8552]						,		device=a						)

    self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]						,		a						,		atol=1e-3						)						)






 @slow
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      Dict:






    """simple docstring"""


    A__         =    LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,		num_labels=2						).to(
        a						)

    A__     , A__     , A__     , A__     , A__         =    prepare_layoutlm_batch_inputs()

    # forward pass
    A__         =    model(
        input_ids=a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						,		labels=torch.tensor([1, 1]						,		device=a						)						,		)

    # test whether we get a loss as a scalar
    A__         =    outputs.loss
    A__         =    torch.Size([]						)
    self.assertEqual(loss.shape						,		a						)

    # test the shape of the logits
    A__         =    outputs.logits
    A__         =    torch.Size((2, 2)						)
    self.assertEqual(logits.shape						,		a						)






 @slow
 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      str:






    """simple docstring"""


    A__         =    LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,		num_labels=13						).to(
        a						)

    A__     , A__     , A__     , A__     , A__         =    prepare_layoutlm_batch_inputs()

    # forward pass
    A__         =    model(
        input_ids=a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						,		labels=a						)

    # test the loss calculation to be around 2.65
    # expected_loss = torch.tensor(2.65, device=torch_device)

    # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
    # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

    # test the shape of the logits
    A__         =    outputs.logits
    A__         =    torch.Size((2, 25, 13)						)
    self.assertEqual(logits.shape						,		a						)






 @slow
 def 	_UpperCamelCase   (self					:     List[str]						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'						).to(a						)

    A__     , A__     , A__     , A__     , A__         =    prepare_layoutlm_batch_inputs()

    # forward pass
    A__         =    model(input_ids=a						,		bbox=a						,		attention_mask=a						,		token_type_ids=a						)

    # test the shape of the logits
    A__         =    torch.Size((2, 25)						)
    self.assertEqual(outputs.start_logits.shape						,		a						)
    self.assertEqual(outputs.end_logits.shape						,		a						)








'''simple docstring'''







import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


lowerCAmelCase_    	=							logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowerCAmelCase_    	=							direct_transformers_import(Path(__file__).parent)


lowerCAmelCase_    	=							{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}

class   _snake_case( UpperCAmelCase			):
 __snake_case:       str									=				['''feature_extractor''', '''tokenizer''']
 # Names need to be attr_class for attr in attributes
 __snake_case:       Optional[Any]									=				None
 __snake_case:       List[str]									=				None
 __snake_case:       Any									=				None






 def __init__(self					:     List[Any]						,		*a					:     Union[str, Any]						,		**a					:     Optional[int]						)					->      Any:






    """simple docstring"""


    for key in kwargs:
       if key not in self.attributes:
          raise TypeError(f"""Unexpected keyword argument {key}."""						)
    for arg, attribute_name in zip(a						,		self.attributes						):
       if attribute_name in kwargs:
          raise TypeError(f"""Got multiple values for argument {attribute_name}."""						)
       else:
          A__         =    arg

    if len(a						) != len(self.attributes						):
       raise ValueError(
           f"""This processor requires {len(self.attributes						)} arguments: {", ".join(self.attributes						)}. Got """
           f"""{len(a						)} arguments instead."""						)

    # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
    for attribute_name, arg in kwargs.items():
       A__         =    getattr(self						,		f"""{attribute_name}_class"""						)
       # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
       A__         =    AUTO_TO_BASE_CLASS_MAPPING.get(a						,		a						)
       if isinstance(a						,		a						):
          A__         =    tuple(getattr(a						,		a						) for n in class_name if n is not None						)
       else:
          A__         =    getattr(a						,		a						)

       if not isinstance(a						,		a						):
          raise ValueError(
              f"""Received a {type(a						).__name__} for argument {attribute_name}, but a {class_name} was expected."""						)

       setattr(self						,		a						,		a						)






 def __repr__(self					:     Optional[int]						)					->      Any:






    """simple docstring"""


    A__         =    [f"""- {name}: {repr(getattr(self						,		a						)						)}""" for name in self.attributes]
    A__         =    '\n'.join(a						)
    return f"""{self.__class__.__name__}:\n{attributes_repr}"""






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     Tuple						,		a					:     bool = False						,		**a					:     int						)					->      List[str]:






    """simple docstring"""


    os.makedirs(a						,		exist_ok=a						)

    if push_to_hub:
       A__         =    kwargs.pop('commit_message'						,		a						)
       A__         =    kwargs.pop('repo_id'						,		save_directory.split(os.path.sep						)[-1]						)
       A__         =    self._create_repo(a						,		**a						)
       A__         =    self._get_files_timestamps(a						)
    # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
    # loaded from the Hub.
    if self._auto_class is not None:
       A__         =    [getattr(self						,		a						) for attribute_name in self.attributes]
       A__         =    [(a.init_kwargs if isinstance(a						,		a						) else a) for a in attrs]
       custom_object_save(self						,		a						,		config=a						)

    for attribute_name in self.attributes:
       A__         =    getattr(self						,		a						)
       # Include the processor class in the attribute config so this processor can then be reloaded with the
       # `AutoProcessor` API.
       if hasattr(a						,		'_set_processor_class'						):
          attribute._set_processor_class(self.__class__.__name__						)
       attribute.save_pretrained(a						)

    if self._auto_class is not None:
       # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
       for attribute_name in self.attributes:
          A__         =    getattr(self						,		a						)
          if isinstance(a						,		a						):
             del attribute.init_kwargs["auto_map"]

    if push_to_hub:
       self._upload_modified_files(
           a						,		a						,		a						,		commit_message=a						,		token=kwargs.get('use_auth_token'						)						,		)






 @classmethod
 def 	_UpperCamelCase   (cls					:     int						,		a					:     Union[str, os.PathLike]						,		a					:     Optional[Union[str, os.PathLike]] = None						,		a					:     bool = False						,		a					:     bool = False						,		a					:     Optional[Union[str, bool]] = None						,		a					:     str = "main"						,		**a					:     Union[str, Any]						,		)					->      Optional[Any]:






    """simple docstring"""


    A__         =    cache_dir
    A__         =    force_download
    A__         =    local_files_only
    A__         =    revision

    A__         =    kwargs.pop('use_auth_token'						,		a						)
    if use_auth_token is not None:
       warnings.warn(
           'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'						,		a						)
       if token is not None:
          raise ValueError(
              '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'						)
       A__         =    use_auth_token

    if token is not None:
       # change to `token` in a follow-up PR
       A__         =    token

    A__         =    cls._get_arguments_from_pretrained(a						,		**a						)
    return cls(*a						)






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[int]						,		a					:     Union[str, Any]="AutoProcessor"						)					->      Any:






    """simple docstring"""


    if not isinstance(a						,		a						):
       A__         =    auto_class.__name__

    import transformers.models.auto as auto_module

    if not hasattr(a						,		a						):
       raise ValueError(f"""{auto_class} is not a valid auto class."""						)

    A__         =    auto_class






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[Any]						,		a					:     Optional[Any]						,		**a					:     Tuple						)					->      int:






    """simple docstring"""


    A__         =    []
    for attribute_name in cls.attributes:
       A__         =    getattr(cls						,		f"""{attribute_name}_class"""						)
       if isinstance(a						,		a						):
          A__         =    tuple(getattr(a						,		a						) if n is not None else None for n in class_name						)
          A__         =    kwargs.get('use_fast'						,		a						)
          if use_fast and classes[1] is not None:
             A__         =    classes[1]
          else:
             A__         =    classes[0]
       else:
          A__         =    getattr(a						,		a						)

       args.append(attribute_class.from_pretrained(a						,		**a						)						)
    return args






 @property
 def 	_UpperCamelCase   (self					:     str						)					->      List[str]:






    """simple docstring"""


    A__         =    getattr(self						,		self.attributes[0]						)
    return getattr(a						,		'model_input_names'						,		a						)




lowerCAmelCase_    	=							copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
    lowerCAmelCase_    	=							ProcessorMixin.push_to_hub.__doc__.format(
        object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
    )


