


'''simple docstring'''





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			(			_snake_case						):
 UpperCAmelCase				=					["image_processor", "tokenizer"]
 UpperCAmelCase				=					"BridgeTowerImageProcessor"
 UpperCAmelCase				=					("RobertaTokenizer", "RobertaTokenizerFast")


 def __init__(      self							:	Dict			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	Union[str, Any] ):
    super().__init__(__lowerCamelCase			,       __lowerCamelCase )


 def __call__(      self							:	Any			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Union[bool, str, PaddingStrategy] = False			,       __lowerCamelCase							:	Union[bool, str, TruncationStrategy] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[bool] = None			,       __lowerCamelCase							:	Optional[bool] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[Union[str, TensorType]] = None			,       **__lowerCamelCase							:	Optional[Any]			,       ):
    UpperCAmelCase__	:List[str]      =      self.tokenizer(
        text=__lowerCamelCase			,       add_special_tokens=__lowerCamelCase			,       padding=__lowerCamelCase			,       truncation=__lowerCamelCase			,       max_length=__lowerCamelCase			,       stride=__lowerCamelCase			,       pad_to_multiple_of=__lowerCamelCase			,       return_token_type_ids=__lowerCamelCase			,       return_attention_mask=__lowerCamelCase			,       return_overflowing_tokens=__lowerCamelCase			,       return_special_tokens_mask=__lowerCamelCase			,       return_offsets_mapping=__lowerCamelCase			,       return_length=__lowerCamelCase			,       verbose=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase			,       )
    # add pixel_values + pixel_mask
    UpperCAmelCase__	:List[str]      =      self.image_processor(
        __lowerCamelCase			,       return_tensors=__lowerCamelCase			,       do_normalize=__lowerCamelCase			,       do_center_crop=__lowerCamelCase			,       **__lowerCamelCase )
    encoding.update(__lowerCamelCase )

    return encoding


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       *__lowerCamelCase							:	Optional[int]			,       **__lowerCamelCase							:	Tuple ):
    return self.tokenizer.batch_decode(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	str ):
    return self.tokenizer.decode(*__lowerCamelCase			,       **__lowerCamelCase )





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Dict      =      self.tokenizer.model_input_names
    UpperCAmelCase__	:List[str]      =      self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''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(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   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__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


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



