



'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration







def a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    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 a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    List[Any]      							=list(s_dict.keys()					)
			for key in keys:
						if "transformer_layers" in key:
									snake_case:    str      							=s_dict.pop(__UpperCAmelCase					)
						elif "subsample" in key:
									snake_case:    Dict      							=s_dict.pop(__UpperCAmelCase					)







def a_     (    __UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case							,  snake_case:    List[str]      							=emb.weight.shape
			snake_case:    List[str]      							=nn.Linear(__UpperCAmelCase				,							__UpperCAmelCase				,							bias=__UpperCAmelCase					)
			snake_case:    str      							=emb.weight.data
			return lin_layer







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    int      							=torch.load(__UpperCAmelCase				,							map_location='cpu'					)
			snake_case:    Any      							=mam_aaa['args']
			snake_case:    Dict      							=mam_aaa['model']
			snake_case:    Any      							=state_dict['decoder.output_projection.weight']

			remove_ignore_keys_(__UpperCAmelCase					)
			rename_keys(__UpperCAmelCase					)

			snake_case:    List[Any]      							=state_dict['decoder.embed_tokens.weight'].shape[0]

			snake_case:    List[str]      							=args.share_decoder_input_output_embed

			snake_case:    Union[str, Any]      							=[int(__UpperCAmelCase					) for i in args.conv_kernel_sizes.split(','					)]
			snake_case:    Any      							=SpeechaTextConfig(
			    vocab_size=__UpperCAmelCase				,							max_source_positions=args.max_source_positions				,							max_target_positions=args.max_target_positions				,							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				,							dropout=args.dropout				,							attention_dropout=args.attention_dropout				,							activation_dropout=args.activation_dropout				,							activation_function='relu'				,							num_conv_layers=len(__UpperCAmelCase					)				,							conv_channels=args.conv_channels				,							conv_kernel_sizes=__UpperCAmelCase				,							input_feat_per_channel=args.input_feat_per_channel				,							input_channels=args.input_channels				,							tie_word_embeddings=__UpperCAmelCase				,							num_beams=5				,							max_length=2_00				,							use_cache=__UpperCAmelCase				,							decoder_start_token_id=2				,							early_stopping=__UpperCAmelCase				,							)

			snake_case:    Any      							=SpeechaTextForConditionalGeneration(__UpperCAmelCase					)
			snake_case							,  snake_case:    str      							=model.model.load_state_dict(__UpperCAmelCase				,							strict=__UpperCAmelCase					)
			if len(__UpperCAmelCase					) > 0 and not set(__UpperCAmelCase					) <= {
			    "encoder.embed_positions.weights",
			    "decoder.embed_positions.weights",
			}:
						raise ValueError(
						    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
						    f''' but all the following weights are missing {missing}'''					)

			if tie_embeds:
						snake_case:    Dict      							=make_linear_from_emb(model.model.decoder.embed_tokens					)
			else:
						snake_case:    Dict      							=lm_head_weights

			model.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	a							   =				parser.parse_args()
	convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)











'''simple docstring'''






from __future__ import annotations

import numpy as np
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase	,				_lowerCAmelCase					=				np.shape(lowerCAmelCase    )
      if rows != columns:
            _lowerCAmelCase					=				(
                """'table' has to be of square shaped array but got a """
                f"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(lowerCAmelCase    )

      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      _lowerCAmelCase					=				np.zeros((rows, columns)    )
      for i in range(lowerCAmelCase    ):
            for j in range(lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""    )
                  _lowerCAmelCase					=				(table[i][j] - total) / upper[j][j]
            _lowerCAmelCase					=				1
            for j in range(lowerCAmelCase				,      lowerCAmelCase    ):
                  _lowerCAmelCase					=				sum(lower[i][k] * upper[k][j] for k in range(lowerCAmelCase    )    )
                  _lowerCAmelCase					=				table[i][j] - total
      return lower, upper


if __name__ == "__main__":
      import doctest

      doctest.testmod()






