



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def A    (							__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     [
			    '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       )    ->   int:
			A__  ,			A__	  =     emb.weight.shape
			A__	  =     nn.Linear(__UpperCamelCase			,						__UpperCamelCase			,						bias=__UpperCamelCase       )
			A__	  =     emb.weight.data
			return lin_layer


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

			A__	  =     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'			,						)

			A__	  =     state_dict['decoder.embed_tokens.weight']
			A__	  =     MaMaaaForConditionalGeneration(__UpperCamelCase       )
			model.model.load_state_dict(__UpperCamelCase			,						strict=__UpperCamelCase       )
			A__	  =     make_linear_from_emb(model.model.shared       )

			return model


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












from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__a:     Union[str, Any]   					=					logging.get_logger(__name__)

__a:     str   					=					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__a:     Dict   					=					{
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

__a:     Union[str, Any]   					=					{
    '''facebook/blenderbot_small-90M''': 512,
}
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  VOCAB_FILES_NAMES
  _lowerCamelCase    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase    =  BlenderbotSmallTokenizer


  def __init__(      self		:			List[str]       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			str=None       ,				lowerCamelCase		:			Optional[Any]="<|endoftext|>"       ,				lowerCamelCase		:			Dict="<|endoftext|>"       ,				lowerCamelCase		:			str="<|endoftext|>"       ,				lowerCamelCase		:			str=False       ,				lowerCamelCase		:			Tuple=True       ,				**lowerCamelCase		:			List[str]       ,				)						->	List[Any]:


         """simple docstring"""
         super().__init__(
             ByteLevelBPETokenizer(
                 vocab=lowerCamelCase       ,				merges=lowerCamelCase       ,				add_prefix_space=lowerCamelCase       ,				trim_offsets=lowerCamelCase       ,				)       ,				bos_token=lowerCamelCase       ,				eos_token=lowerCamelCase       ,				unk_token=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    add_prefix_space


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Union[str, Any]=None							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[int]       ,				lowerCamelCase		:			Optional[List[int]] = None							)						->	List[int]:


         """simple docstring"""
         _UpperCAmelCase							=    [self.sep_token_id]
         _UpperCAmelCase							=    [self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep							) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]