



"""simple docstring"""







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       UpperCamelCase__   (	__UpperCAmelCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     ["""image_processor""", """tokenizer"""]
      _SCREAMING_SNAKE_CASE  				=     """LayoutLMv2ImageProcessor"""
      _SCREAMING_SNAKE_CASE  				=     ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")



      def __init__(      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):



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

        lowerCAmelCase_   :				Union[str, Any]								= 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				:      str    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[Any] = None    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      Any = None    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      Dict = True    ,			SCREAMING_SNAKE_CASE_				:      List[str] = False    ,			SCREAMING_SNAKE_CASE_				:      List[str] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Dict = 0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Tuple = None    ,			SCREAMING_SNAKE_CASE_				:      Tuple = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any] = False    ,			SCREAMING_SNAKE_CASE_				:      str = False    ,			SCREAMING_SNAKE_CASE_				:      str = False    ,			SCREAMING_SNAKE_CASE_				:      Any = False    ,			SCREAMING_SNAKE_CASE_				:      Dict = True    ,			SCREAMING_SNAKE_CASE_				:      int = None    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			):



        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
        lowerCAmelCase_   :				Tuple								= 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__       ):
            lowerCAmelCase_   :				Union[str, Any]								= [text]  # add batch dimension (as the image processor always adds a batch dimension)
          lowerCAmelCase_   :				Optional[Any]								= features['words']

        lowerCAmelCase_   :				Optional[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
        lowerCAmelCase_   :				List[Any]								= features.pop('pixel_values'       )
        if return_overflowing_tokens is True:
          lowerCAmelCase_   :				Dict								= self.get_overflowing_images(UpperCamelCase__    ,			encoded_inputs['overflow_to_sample_mapping']       )
        lowerCAmelCase_   :				Union[str, Any]								= images

        return encoded_inputs



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				Any								= []
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			*SCREAMING_SNAKE_CASE_				:      Tuple    ,			**SCREAMING_SNAKE_CASE_				:      Tuple       ):
        return self.tokenizer.batch_decode(*UpperCamelCase__    ,			**UpperCamelCase__       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      Tuple       ):
        return self.tokenizer.decode(*UpperCamelCase__    ,			**UpperCamelCase__       )



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        return ["input_ids", "bbox", "attention_mask", "image"]



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,			UpperCamelCase__    ,			)
        return self.image_processor











import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

						lowerCAmelCase_      			=  {key.replace('''decoder''' ,  '''model'''  ): val for key, val in state_dict.items()}

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  max_position_embeddings=args.max_target_positions ,  num_layers=args.decoder_layers ,  attention_heads=args.decoder_attention_heads ,  ffn_dim=args.decoder_ffn_embed_dim ,  d_model=args.decoder_embed_dim ,  layerdrop=args.decoder_layerdrop ,  dropout=args.dropout ,  attention_dropout=args.attention_dropout ,  activation_dropout=args.activation_dropout ,  activation_function='''gelu''' ,  scale_embedding=not args.no_scale_embedding ,  tie_word_embeddings=args.share_decoder_input_output_embed ,  )

						lowerCAmelCase_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
						_A     =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						_A     =			parser.parse_args()
						_A     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)




