




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		_A   (    a__ ):
  _UpperCamelCase  :     Union[str, Any]					=     ['''image_processor''', '''tokenizer''']
  _UpperCamelCase  :     Union[str, Any]					=     '''LayoutLMv2ImageProcessor'''
  _UpperCamelCase  :     Dict					=     ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






  def __init__(    self						:      int ,		_A						:      Tuple=None ,		_A						:      List[str]=None ,		**_A						:      Optional[int] )						->       Any:






       """simple docstring"""







       if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.''' ,		_UpperCAmelCase ,		)
            lowercase   :  str        =					kwargs.pop('''feature_extractor''' )

       lowercase   :  Dict        =					image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.''' )
       if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.''' )

       super().__init__(_UpperCAmelCase ,		_UpperCAmelCase )






  def __call__(    self						:      int ,		_A						:      List[str] ,		_A						:      str = None ,		_A						:      int = None ,		_A						:      str = None ,		_A						:      Any = None ,		_A						:      Union[str, Any] = True ,		_A						:      Optional[Any] = False ,		_A						:      int = None ,		_A						:      Optional[int] = None ,		_A						:      int = 0 ,		_A						:      List[str] = None ,		_A						:      Tuple = None ,		_A						:      List[Any] = None ,		_A						:      List[Any] = False ,		_A						:      str = False ,		_A						:      Optional[int] = False ,		_A						:      Tuple = False ,		_A						:      int = True ,		_A						:      Any = None ,		**_A						:      Dict ,		)						->       Optional[int]:






       """simple docstring"""







       if self.image_processor.apply_ocr and (boxes is not None):
            raise ValueError(
                '''You cannot provide bounding boxes '''
                '''if you initialized the image processor with apply_ocr set to True.''' )

       if self.image_processor.apply_ocr and (word_labels is not None):
            raise ValueError(
                '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''' )

       if return_overflowing_tokens is True and return_offsets_mapping is False:
            raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.''' )

       # first, apply the image processor
       lowercase   :  Dict        =					self.image_processor(images=_UpperCAmelCase ,		return_tensors=_UpperCAmelCase )

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
            if isinstance(_UpperCAmelCase ,		_UpperCAmelCase ):
                 lowercase   :  Optional[int]        =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
            lowercase   :  Dict        =					features['words']

       lowercase   :  Any        =					self.tokenizer(
           text=text if text is not None else features['''words'''] ,		text_pair=text_pair if text_pair is not None else None ,		boxes=boxes if boxes is not None else features['''boxes'''] ,		word_labels=_UpperCAmelCase ,		add_special_tokens=_UpperCAmelCase ,		padding=_UpperCAmelCase ,		truncation=_UpperCAmelCase ,		max_length=_UpperCAmelCase ,		stride=_UpperCAmelCase ,		pad_to_multiple_of=_UpperCAmelCase ,		return_token_type_ids=_UpperCAmelCase ,		return_attention_mask=_UpperCAmelCase ,		return_overflowing_tokens=_UpperCAmelCase ,		return_special_tokens_mask=_UpperCAmelCase ,		return_offsets_mapping=_UpperCAmelCase ,		return_length=_UpperCAmelCase ,		verbose=_UpperCAmelCase ,		return_tensors=_UpperCAmelCase ,		**_UpperCAmelCase ,		)

       # add pixel values
       lowercase   :  Dict        =					features.pop('''pixel_values''' )
       if return_overflowing_tokens is True:
            lowercase   :  str        =					self.get_overflowing_images(_UpperCAmelCase ,		encoded_inputs['''overflow_to_sample_mapping'''] )
       lowercase   :  str        =					images

       return encoded_inputs






  def    __a     (    self						:      Any ,		_A						:      Union[str, Any] ,		_A						:      List[str] )						->       Tuple:






       """simple docstring"""







       lowercase   :  Tuple        =					[]
       for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx] )

       if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
            raise ValueError(
                '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                f""" {len(_UpperCAmelCase )} and {len(_UpperCAmelCase )}""" )

       return images_with_overflow






  def    __a     (    self						:      Tuple ,		*_A						:      int ,		**_A						:      str )						->       Optional[int]:






       """simple docstring"""







       return self.tokenizer.batch_decode(*_UpperCAmelCase ,		**_UpperCAmelCase )






  def    __a     (    self						:      Any ,		*_A						:      Optional[int] ,		**_A						:      List[str] )						->       Any:






       """simple docstring"""







       return self.tokenizer.decode(*_UpperCAmelCase ,		**_UpperCAmelCase )






  @property
  def    __a     (    self						:      Any )						->       List[Any]:






       """simple docstring"""







       return ["input_ids", "bbox", "attention_mask", "image"]






  @property
  def    __a     (    self						:      List[Any] )						->       Optional[Any]:






       """simple docstring"""







       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,		_UpperCAmelCase ,		)
       return self.image_processor_class






  @property
  def    __a     (    self						:      Optional[Any] )						->       Any:






       """simple docstring"""







       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,		_UpperCAmelCase ,		)
       return self.image_processor






'''simple docstring'''




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)