




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  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    str  			=   ['''image_processor''', '''tokenizer''']
   lowerCAmelCase      :    List[Any]  			=   '''BridgeTowerImageProcessor'''
   lowerCAmelCase      :    Union[str, Any]  			=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')



   def __init__(	self					:  List[str]		,  _lowercase					:  str		,  _lowercase					:  Tuple							):



    """simple docstring"""



    super().__init__(_lowercase		,  _lowercase							)



   def __call__(	self					:  List[str]		,  _lowercase					:  List[str]		,  _lowercase					:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,  _lowercase					:  bool = True		,  _lowercase					:  Union[bool, str, PaddingStrategy] = False		,  _lowercase					:  Union[bool, str, TruncationStrategy] = None		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  int = 0		,  _lowercase					:  Optional[int] = None		,  _lowercase					:  Optional[bool] = None		,  _lowercase					:  Optional[bool] = None		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = False		,  _lowercase					:  bool = True		,  _lowercase					:  Optional[Union[str, TensorType]] = None		,  **_lowercase					:  Dict		,  ):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= self.tokenizer(
        text=_lowercase		,  add_special_tokens=_lowercase		,  padding=_lowercase		,  truncation=_lowercase		,  max_length=_lowercase		,  stride=_lowercase		,  pad_to_multiple_of=_lowercase		,  return_token_type_ids=_lowercase		,  return_attention_mask=_lowercase		,  return_overflowing_tokens=_lowercase		,  return_special_tokens_mask=_lowercase		,  return_offsets_mapping=_lowercase		,  return_length=_lowercase		,  verbose=_lowercase		,  return_tensors=_lowercase		,  **_lowercase		,  )
    # add pixel_values + pixel_mask
    _UpperCamelCase:     List[Any]							= self.image_processor(
        _lowercase		,  return_tensors=_lowercase		,  do_normalize=_lowercase		,  do_center_crop=_lowercase		,  **_lowercase							)
    encoding.update(_lowercase							)

    return encoding



   def   lowerCAmelCase						(	self					:  List[str]		,  *_lowercase					:  List[str]		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



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



   def   lowerCAmelCase						(	self					:  Optional[int]		,  *_lowercase					:  Any		,  **_lowercase					:  Union[str, Any]							):



    """simple docstring"""



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



   @property
   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= self.tokenizer.model_input_names
    _UpperCamelCase:     Union[str, Any]							= self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def       lowerCAmelCase_     (		lowercase:							Any		)    ->				Optional[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= [
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''decoder.output_projection.weight''',
     '''_float_tensor''',
     '''encoder.embed_positions._float_tensor''',
     '''decoder.embed_positions._float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(lowercase  ,					lowercase		)




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Dict:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     Optional[int]							= emb.weight.shape
 _UpperCamelCase:     int							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     Any							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							Optional[Any]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= torch.load(lowercase  ,					map_location='''cpu'''		)
 _UpperCamelCase:     Any							= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
 _UpperCamelCase:     List[Any]							= mam_aaa['''model''']
 remove_ignore_keys_(lowercase		)
 _UpperCamelCase:     Tuple							= state_dict['''encoder.embed_tokens.weight'''].shape[0]

 _UpperCamelCase:     List[Any]							= MaMaaaConfig(
     vocab_size=lowercase  ,					max_position_embeddings=1_024  ,					encoder_layers=args.encoder_layers  ,					decoder_layers=args.decoder_layers  ,					encoder_attention_heads=args.encoder_attention_heads  ,					decoder_attention_heads=args.decoder_attention_heads  ,					encoder_ffn_dim=args.encoder_ffn_embed_dim  ,					decoder_ffn_dim=args.decoder_ffn_embed_dim  ,					d_model=args.encoder_embed_dim  ,					encoder_layerdrop=args.encoder_layerdrop  ,					decoder_layerdrop=args.decoder_layerdrop  ,					dropout=args.dropout  ,					attention_dropout=args.attention_dropout  ,					activation_dropout=args.activation_dropout  ,					activation_function='''relu'''  ,					)

 _UpperCamelCase:     List[Any]							= state_dict['''decoder.embed_tokens.weight''']
 _UpperCamelCase:     Optional[int]							= MaMaaaForConditionalGeneration(lowercase		)
 model.model.load_state_dict(lowercase  ,					strict=lowercase		)
 _UpperCamelCase:     Optional[Any]							= make_linear_from_emb(model.model.shared		)

 return model


if __name__ == "__main__":
  UpperCAmelCase_			    =							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.''')
  UpperCAmelCase_			    =							parser.parse_args()
  UpperCAmelCase_			    =							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)