

'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			int:
					lowerCamelCase_  				=      [
					    """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   _UpperCamelCase	(		__UpperCamelCase	)       ->			Union[str, Any]:
					lowerCamelCase_  				=      emb.weight.shape
					lowerCamelCase_  				=      nn.Linear(UpperCamelCase__    ,UpperCamelCase__    ,bias=UpperCamelCase__	)
					lowerCamelCase_  				=      emb.weight.data
					return lin_layer







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			Optional[int]:
					lowerCamelCase_  				=      torch.load(UpperCamelCase__    ,map_location='cpu'	)
					lowerCamelCase_  				=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
					lowerCamelCase_  				=      mam_aaa["""model"""]
					remove_ignore_keys_(UpperCamelCase__	)
					lowerCamelCase_  				=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

					lowerCamelCase_  				=      MaMaaaConfig(
					    vocab_size=UpperCamelCase__    ,max_position_embeddings=10_24    ,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'    ,)

					lowerCamelCase_  				=      state_dict["""decoder.embed_tokens.weight"""]
					lowerCamelCase_  				=      MaMaaaForConditionalGeneration(UpperCamelCase__	)
					model.model.load_state_dict(UpperCamelCase__    ,strict=UpperCamelCase__	)
					lowerCamelCase_  				=      make_linear_from_emb(model.model.shared	)

					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_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}
