




'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str]				):




     """simple docstring"""


     a_     :							Union[str, Any]     						=  [
         """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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Dict				):




     """simple docstring"""


     a_						,     a_     :							str     						=  emb.weight.shape
     a_     :							Optional[Any]     						=  nn.Linear(UpperCamelCase__ ,    UpperCamelCase__ ,    bias=UpperCamelCase__				)
     a_     :							List[Any]     						=  emb.weight.data
     return lin_layer



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[Any]				):




     """simple docstring"""


     a_     :							Optional[Any]     						=  torch.load(UpperCamelCase__ ,    map_location="""cpu"""				)
     a_     :							int     						=  mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
     a_     :							List[str]     						=  mam_aaa["""model"""]
     remove_ignore_keys_(UpperCamelCase__				)
     a_     :							Tuple     						=  state_dict["""encoder.embed_tokens.weight"""].shape[0]

     a_     :							str     						=  MaMaaaConfig(
         vocab_size=UpperCamelCase__ ,    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""" ,    )

     a_     :							int     						=  state_dict["""decoder.embed_tokens.weight"""]
     a_     :							Dict     						=  MaMaaaForConditionalGeneration(UpperCamelCase__				)
     model.model.load_state_dict(UpperCamelCase__ ,    strict=UpperCamelCase__				)
     a_     :							Optional[Any]     						=  make_linear_from_emb(model.model.shared				)

     return model


if __name__ == "__main__":
 lowerCAmelCase_      :  str				     =  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_      :  int				     =  parser.parse_args()
 lowerCAmelCase_      :  Any				     =  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

           if self.pad_token_id is not None:
                # pad the tokens up to max length
                a_     :							int     						=  max_length if max_length is not None else self.max_length

                if max_length is not None:
                     a_						,     a_     :							int     						=  pad_model_inputs(
                         lowercase__						,						max_seq_length=lowercase__						,						pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}
