
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  [
      '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(__A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  emb.weight.shape
  UpperCAmelCase					      =  nn.Linear(__A   ,       __A   ,       bias=__A			)
  UpperCAmelCase					      =  emb.weight.data
  return lin_layer
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  torch.load(__A   ,       map_location="cpu"			)
  UpperCAmelCase					      =  mam_aaa['args'] or mam_aaa['cfg']['model']
  UpperCAmelCase					      =  mam_aaa['model']
  remove_ignore_keys_(__A			)
  UpperCAmelCase					      =  state_dict['encoder.embed_tokens.weight'].shape[0]

  UpperCAmelCase					      =  MaMaaaConfig(
      vocab_size=__A   ,       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					      =  state_dict['decoder.embed_tokens.weight']
  UpperCAmelCase					      =  MaMaaaForConditionalGeneration(__A			)
  model.model.load_state_dict(__A   ,       strict=__A			)
  UpperCAmelCase					      =  make_linear_from_emb(model.model.shared			)

  return model


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






import unittest

import numpy as np
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A = None   ,       ):
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)

  if shape_a[0] != shape_b[0]:
    UpperCAmelCase					      =  (
        "Expected the same number of rows for A and B. "
        F"Instead found A of size {shape_a} and B of size {shape_b}"
    )
    raise ValueError(__A			)

  if shape_b[1] != shape_c[1]:
    UpperCAmelCase					      =  (
        "Expected the same number of columns for B and C. "
        F"Instead found B of size {shape_b} and C of size {shape_c}"
    )
    raise ValueError(__A			)

  UpperCAmelCase					      =  pseudo_inv
  if a_inv is None:
    try:
      UpperCAmelCase					      =  np.linalg.inv(__A			)
    except np.linalg.LinAlgError:
      raise ValueError(
          "Input matrix A is not invertible. Cannot compute Schur complement."			)

  return mat_c - mat_b.T @ a_inv @ mat_b






class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[str]    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   UpperCAmelCase					      =  schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  np.block([[a, b], [b.T, c]]    )

   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )

   self.assertAlmostEqual(lowerCAmelCase__			,						det_a * det_s    )


 def _UpperCamelCase	(						self		: str    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1, 3], [6, 3, 5]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()



