






"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int				)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							[
         "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(snake_case			,					snake_case				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[str]				)->		Optional[int]:






     '''simple docstring'''



     UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							emb.weight.shape
     UpperCAmelCase__						:    str							=							nn.Linear(snake_case			,					snake_case			,					bias=snake_case				)
     UpperCAmelCase__						:    Union[str, Any]							=							emb.weight.data
     return lin_layer


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		Dict:






     '''simple docstring'''



     UpperCAmelCase__						:    int							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Union[str, Any]							=							mam_aaa["args"] or mam_aaa["cfg"]["model"]
     UpperCAmelCase__						:    Optional[int]							=							mam_aaa["model"]
     remove_ignore_keys_(snake_case				)
     UpperCAmelCase__						:    List[str]							=							state_dict["encoder.embed_tokens.weight"].shape[0]

     UpperCAmelCase__						:    Tuple							=							MaMaaaConfig(
         vocab_size=snake_case			,					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__						:    Optional[int]							=							state_dict["decoder.embed_tokens.weight"]
     UpperCAmelCase__						:    Union[str, Any]							=							MaMaaaForConditionalGeneration(snake_case				)
     model.model.load_state_dict(snake_case			,					strict=snake_case				)
     UpperCAmelCase__						:    List[str]							=							make_linear_from_emb(model.model.shared				)

     return model


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










"""simple docstring"""



import numpy as np


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : np.ndarray			,					snake_case       : np.ndarray			,					snake_case       : float = 1E-1_2			,					snake_case       : int = 100			,					)->		tuple[float, np.ndarray]:






     '''simple docstring'''



     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[1]
     # Ensure proper dimensionality.
     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(snake_case				) == np.iscomplexobj(snake_case				)
     UpperCAmelCase__						:    str							=							np.iscomplexobj(snake_case				)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(snake_case			,					input_matrix.conj().T				)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     UpperCAmelCase__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    List[Any]							=							0
     UpperCAmelCase__						:    Any							=							0
     UpperCAmelCase__						:    List[str]							=							1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          UpperCAmelCase__						:    Optional[Any]							=							np.dot(snake_case			,					snake_case				)
          # Normalize the resulting output vector.
          UpperCAmelCase__						:    Tuple							=							w / np.linalg.norm(snake_case				)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          UpperCAmelCase__						:    Any							=							vector.conj().T if is_complex else vector.T
          UpperCAmelCase__						:    List[str]							=							np.dot(snake_case			,					np.dot(snake_case			,					snake_case				)				)

          # Check convergence.
          UpperCAmelCase__						:    Optional[Any]							=							np.abs(lambda_ - lambda_previous				) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               UpperCAmelCase__						:    Tuple							=							True

          UpperCAmelCase__						:    Tuple							=							lambda_

     if is_complex:
          UpperCAmelCase__						:    str							=							np.real(lambda_				)

     return lambda_, vector


def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
     UpperCAmelCase__						:    Dict							=							np.array([41, 4, 20]				)
     UpperCAmelCase__						:    Union[str, Any]							=							real_input_matrix.astype(np.complexaaa				)
     UpperCAmelCase__						:    Optional[int]							=							np.triu(1J * complex_input_matrix			,					1				)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     UpperCAmelCase__						:    int							=							np.array([41, 4, 20]				).astype(np.complexaaa				)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               UpperCAmelCase__						:    List[str]							=							real_input_matrix
               UpperCAmelCase__						:    Any							=							real_vector
          elif problem_type == "complex":
               UpperCAmelCase__						:    str							=							complex_input_matrix
               UpperCAmelCase__						:    List[Any]							=							complex_vector

          # Our implementation.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							power_iteration(snake_case			,					snake_case				)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							np.linalg.eigh(snake_case				)
          # Last eigenvalue is the maximum one.
          UpperCAmelCase__						:    str							=							eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCAmelCase__						:    Optional[int]							=							eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max				) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(snake_case				) - np.abs(snake_case				)				) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()


