

"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 					UpperCamelCase    (	_lowerCAmelCase   :  int       ) ->	Dict:
	_UpperCAmelCase			:							Optional[int]	  =    [
	    "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    (	_lowerCAmelCase   :  List[Any]       ) ->	Union[str, Any]:
	_UpperCAmelCase			:							List[Any]	  =    emb.weight.shape
	_UpperCAmelCase			:							Optional[int]	  =    nn.Linear(_UpperCAmelCase,     _UpperCAmelCase,     bias=_UpperCAmelCase       )
	_UpperCAmelCase			:							List[str]	  =    emb.weight.data
	return lin_layer
def 					UpperCamelCase    (	_lowerCAmelCase   :  Any       ) ->	int:
	_UpperCAmelCase			:							List[Any]	  =    torch.load(_UpperCAmelCase,     map_location="""cpu"""       )
	_UpperCAmelCase			:							Any	  =    mam_aaa["args"] or mam_aaa["cfg"]["model"]
	_UpperCAmelCase			:							List[Any]	  =    mam_aaa["model"]
	remove_ignore_keys_(_UpperCAmelCase       )
	_UpperCAmelCase			:							int	  =    state_dict["encoder.embed_tokens.weight"].shape[0]

	_UpperCAmelCase			:							Tuple	  =    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""",     )

	_UpperCAmelCase			:							Union[str, Any]	  =    state_dict["decoder.embed_tokens.weight"]
	_UpperCAmelCase			:							Union[str, Any]	  =    MaMaaaForConditionalGeneration(_UpperCAmelCase       )
	model.model.load_state_dict(_UpperCAmelCase,     strict=_UpperCAmelCase       )
	_UpperCAmelCase			:							Optional[Any]	  =    make_linear_from_emb(model.model.shared       )

	return model


if __name__ == "__main__":
						lowerCamelCase__ :     Union[str, Any]							  =     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__ :     Optional[int]							  =     convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''




from typing import Any

def 	UpperCamelCase_							(					_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      list			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	_UpperCAmelCase  :      dict			,	)    ->    list:


       """simple docstring"""







       _validation(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       # Creates data structures and fill initial step
       _UpperCAmelCase     :  dict							      =    {}
       _UpperCAmelCase     :  dict							      =    {}
       for state in states_space:
              _UpperCAmelCase     :  Union[str, Any]							      =    observations_space[0]
              _UpperCAmelCase     :  Tuple							      =    (
                  initial_probabilities[state] * emission_probabilities[state][observation]
              )
              _UpperCAmelCase     :  List[str]							      =    None

       # Fills the data structure with the probabilities of
       # different transitions and pointers to previous states
       for o in range(1			,	len(_UpperCAmelCase    )    ):
              _UpperCAmelCase     :  Optional[Any]							      =    observations_space[o]
              _UpperCAmelCase     :  int							      =    observations_space[o - 1]
              for state in states_space:
                     # Calculates the argmax for probability function
                     _UpperCAmelCase     :  str							      =    ""
                     _UpperCAmelCase     :  Tuple							      =    -1
                     for k_state in states_space:
                            _UpperCAmelCase     :  Any							      =    (
                                probabilities[(k_state, prior_observation)]
                                * transition_probabilities[k_state][state]
                                * emission_probabilities[state][observation]
                            )
                            if probability > max_probability:
                                   _UpperCAmelCase     :  Union[str, Any]							      =    probability
                                   _UpperCAmelCase     :  str							      =    k_state

            # Update probabilities and pointers dicts
                     _UpperCAmelCase     :  Optional[int]							      =    (
                         probabilities[(arg_max, prior_observation)]
                         * transition_probabilities[arg_max][state]
                         * emission_probabilities[state][observation]
                     )

                     _UpperCAmelCase     :  Tuple							      =    arg_max

    # The final observation
       _UpperCAmelCase     :  Optional[Any]							      =    observations_space[len(_UpperCAmelCase    ) - 1]

       # argmax for given final observation
       _UpperCAmelCase     :  List[str]							      =    ""
       _UpperCAmelCase     :  Any							      =    -1
       for k_state in states_space:
              _UpperCAmelCase     :  Optional[int]							      =    probabilities[(k_state, final_observation)]
              if probability > max_probability:
                     _UpperCAmelCase     :  int							      =    probability
                     _UpperCAmelCase     :  Dict							      =    k_state
       _UpperCAmelCase     :  Dict							      =    arg_max

       # Process pointers backwards
       _UpperCAmelCase     :  List[Any]							      =    last_state
       _UpperCAmelCase     :  str							      =    []
       for o in range(len(_UpperCAmelCase    ) - 1			,	-1			,	-1    ):
              result.append(_UpperCAmelCase    )
              _UpperCAmelCase     :  List[Any]							      =    pointers[previous, observations_space[o]]
       result.reverse()

       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_not_empty(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	)
       _validate_lists(_UpperCAmelCase			,	_UpperCAmelCase    )
       _validate_dicts(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       if not all(
           [
               observations_space,
               states_space,
               initial_probabilities,
               transition_probabilities,
               emission_probabilities,
           ]    ):
              raise ValueError("There's an empty parameter"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any    )    ->    None:


       """simple docstring"""







       _validate_list(_UpperCAmelCase			,	"observations_space"    )
       _validate_list(_UpperCAmelCase			,	"states_space"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list"""
              raise ValueError(_UpperCAmelCase    )
       else:
              for x in _object:
                     if not isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Optional[int]							      =    F"""{var_name} must be a list of strings"""
                            raise ValueError(_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      Any			,	)    ->    None:


       """simple docstring"""







       _validate_dict(_UpperCAmelCase			,	"initial_probabilities"			,	_UpperCAmelCase    )
       _validate_nested_dict(_UpperCAmelCase			,	"transition_probabilities"    )
       _validate_nested_dict(_UpperCAmelCase			,	"emission_probabilities"    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _validate_dict(_object			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       for x in _object.values():
              _validate_dict(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      type			,	_UpperCAmelCase  :      bool = False    )    ->    None:


       """simple docstring"""







       if not isinstance(_object			,	_UpperCAmelCase    ):
              _UpperCAmelCase     :  Any							      =    F"""{var_name} must be a dict"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object    ):
              _UpperCAmelCase     :  Tuple							      =    F"""{var_name} all keys must be strings"""
              raise ValueError(_UpperCAmelCase    )
       if not all(isinstance(_UpperCAmelCase			,	_UpperCAmelCase    ) for x in _object.values()    ):
              _UpperCAmelCase     :  List[str]							      =    "nested dictionary " if nested else ""
              _UpperCAmelCase     :  List[str]							      =    F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
              raise ValueError(_UpperCAmelCase    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()

