





"""simple docstring"""






from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
    from ..models.auto import AutoTokenizer



class 	lowerCAmelCase					:






       def 							__A (   self    ,  a__     ):
              raise NotImplementedError()






       def 							__A (   self     ):
              raise NotImplementedError()



class 	lowerCAmelCase					(      snake_case							):






       def __init__(   self    ,  a__    ,  a__ = False    ,  **a__     ):
              _UpperCAmelCase     =     tokenizer
              _UpperCAmelCase     =     skip_prompt
              _UpperCAmelCase     =     decode_kwargs

              # variables used in the streaming process
              _UpperCAmelCase     =     []
              _UpperCAmelCase     =     0
              _UpperCAmelCase     =     True






       def 							__A (   self    ,  a__     ):
              if len(value.shape     ) > 1 and value.shape[0] > 1:
                     raise ValueError('TextStreamer only supports batch size 1'     )
              elif len(value.shape     ) > 1:
                     _UpperCAmelCase     =     value[0]

              if self.skip_prompt and self.next_tokens_are_prompt:
                     _UpperCAmelCase     =     False
                     return

              # Add the new token to the cache and decodes the entire thing.
              self.token_cache.extend(value.tolist()     )
              _UpperCAmelCase     =     self.tokenizer.decode(self.token_cache    ,  **self.decode_kwargs     )

              # After the symbol for a new line, we flush the cache.
              if text.endswith('\n'     ):
                     _UpperCAmelCase     =     text[self.print_len :]
                     _UpperCAmelCase     =     []
                     _UpperCAmelCase     =     0
              # If the last token is a CJK character, we print the characters.
              elif len(a__     ) > 0 and self._is_chinese_char(ord(text[-1]     )     ):
                     _UpperCAmelCase     =     text[self.print_len :]
                     self.print_len += len(a__     )
              # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
              # which may change with the subsequent token -- there are probably smarter ways to do this!)
              else:
                     _UpperCAmelCase     =     text[self.print_len : text.rfind(' '     ) + 1]
                     self.print_len += len(a__     )

              self.on_finalized_text(a__     )






       def 							__A (   self     ):
              # Flush the cache, if it exists
              if len(self.token_cache     ) > 0:
                     _UpperCAmelCase     =     self.tokenizer.decode(self.token_cache    ,  **self.decode_kwargs     )
                     _UpperCAmelCase     =     text[self.print_len :]
                     _UpperCAmelCase     =     []
                     _UpperCAmelCase     =     0
              else:
                     _UpperCAmelCase     =     ''

              _UpperCAmelCase     =     True
              self.on_finalized_text(a__    ,  stream_end=a__     )






       def 							__A (   self    ,  a__    ,  a__ = False     ):
              print(a__    ,  flush=a__    ,  end='' if not stream_end else None     )






       def 							__A (   self    ,  a__     ):
              # This defines a "chinese character" as anything in the CJK Unicode block:
              #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
              #
              # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
              # despite its name. The modern Korean Hangul alphabet is a different block,
              # as is Japanese Hiragana and Katakana. Those alphabets are used to write
              # space-separated words, so they are not treated specially and handled
              # like the all of the other languages.
              if (
                  (cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
                  or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f)  #
                  or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f)  #
                  or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f)  #
                  or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f)  #
                  or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f)  #
                  or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
                  or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f)  #
              ):  #
                     return True

              return False



class 	lowerCAmelCase					(      snake_case							):






       def __init__(   self    ,  a__    ,  a__ = False    ,  a__ = None    ,  **a__     ):
              super().__init__(a__    ,  a__    ,  **a__     )
              _UpperCAmelCase     =     Queue()
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     timeout






       def 							__A (   self    ,  a__    ,  a__ = False     ):
              self.text_queue.put(a__    ,  timeout=self.timeout     )
              if stream_end:
                     self.text_queue.put(self.stop_signal    ,  timeout=self.timeout     )






       def __iter__(   self     ):
              return self






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.text_queue.get(timeout=self.timeout     )
              if value == self.stop_signal:
                     raise StopIteration()
              else:
                     return value













"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [
           '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(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     emb.weight.shape
       _UpperCAmelCase     =     nn.Linear(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,bias=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     emb.weight.data
       return lin_layer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'							)
       _UpperCAmelCase     =     mam_aaa['args'] or mam_aaa['cfg']['model']
       _UpperCAmelCase     =     mam_aaa['model']
       remove_ignore_keys_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     state_dict['encoder.embed_tokens.weight'].shape[0]

       _UpperCAmelCase     =     MaMaaaConfig(
           vocab_size=SCREAMING_SNAKE_CASE,max_position_embeddings=1024,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     =     state_dict['decoder.embed_tokens.weight']
       _UpperCAmelCase     =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE							)
       model.model.load_state_dict(SCREAMING_SNAKE_CASE,strict=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     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)






