


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Any:
  snake_case__						= [
      '''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(__lowerCAmelCase							,   __lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				str:
  snake_case__				,				snake_case__						= emb.weight.shape
  snake_case__						= nn.Linear(__lowerCAmelCase							,   __lowerCAmelCase							,   bias=__lowerCAmelCase				)
  snake_case__						= emb.weight.data
  return lin_layer







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Dict:
  snake_case__						= torch.load(__lowerCAmelCase							,   map_location='''cpu'''				)
  snake_case__						= mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
  snake_case__						= mam_aaa['''model''']
  remove_ignore_keys_(__lowerCAmelCase				)
  snake_case__						= state_dict['''encoder.embed_tokens.weight'''].shape[0]

  snake_case__						= MaMaaaConfig(
      vocab_size=__lowerCAmelCase							,   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'''							,   )

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

  return model


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[int]							     =   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__							:						Optional[int]							     =   parser.parse_args()
   lowerCamelCase__							:						List[str]							     =   convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
   model.save_pretrained(args.pytorch_dump_folder_path)









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.generation import DisjunctiveConstraint



@require_torch
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           # For consistency across different places the DisjunctiveConstraint is called,
           # dc.token_ids is a list of integers. It is also initialized only by integers.

           __lowerCAmelCase             =       [[1, 2, 4], [1, 2, 3, 4]]
           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)
           self.assertTrue(isinstance(dc.token_ids       ,    __UpperCamelCase						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint([torch.LongTensor([1, 2, 4]						), torch.LongTensor([1, 2, 3, 4, 5]						)]						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           # We can't have constraints that are complete subsets of another. This leads to a preverse
           # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
           # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
           # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
           # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
           __lowerCAmelCase             =       [[1, 2], [1, 2, 3, 4]]

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(__UpperCamelCase						)  # fails here


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(3						)
           __lowerCAmelCase             =       stepped is True and completed is True and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 3]						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(4						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2, 4]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 4, 5]						)

           dc.reset()

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 3						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 2						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.remaining() == 0						)
           self.assertTrue(dc.current_seq == [1, 2, 5]						)






