import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration



def 		A_    (					_lowerCAmelCase					)						->			Union[str, Any]:
 UpperCamelCase     : List[Any]	     =       [
     "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 		A_    (					_lowerCAmelCase					)						->			Union[str, Any]:
 UpperCamelCase     : int	     =       list(s_dict.keys()					)
 for key in keys:
  if "transformer_layers" in key:
   UpperCamelCase     : Optional[Any]	     =       s_dict.pop(_lowerCAmelCase					)
  elif "subsample" in key:
   UpperCamelCase     : Dict	     =       s_dict.pop(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase					)						->			Optional[int]:
 UpperCamelCase     : Any	     =       emb.weight.shape
 UpperCamelCase     : Union[str, Any]	     =       nn.Linear(_lowerCAmelCase							,       _lowerCAmelCase							,       bias=_lowerCAmelCase					)
 UpperCamelCase     : str	     =       emb.weight.data
 return lin_layer



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Union[str, Any]:
 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase							,       map_location="cpu"					)
 UpperCamelCase     : Optional[Any]	     =       mam_aaa["args"]
 UpperCamelCase     : str	     =       mam_aaa["model"]
 UpperCamelCase     : List[Any]	     =       state_dict["decoder.output_projection.weight"]

 remove_ignore_keys_(_lowerCAmelCase					)
 rename_keys(_lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       state_dict["decoder.embed_tokens.weight"].shape[0]

 UpperCamelCase     : str	     =       args.share_decoder_input_output_embed

 UpperCamelCase     : int	     =       [int(_lowerCAmelCase					) for i in args.conv_kernel_sizes.split(","					)]
 UpperCamelCase     : List[str]	     =       SpeechaTextConfig(
     vocab_size=_lowerCAmelCase							,       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(_lowerCAmelCase					)							,       conv_channels=args.conv_channels							,       conv_kernel_sizes=_lowerCAmelCase							,       input_feat_per_channel=args.input_feat_per_channel							,       input_channels=args.input_channels							,       tie_word_embeddings=_lowerCAmelCase							,       num_beams=5							,       max_length=200							,       use_cache=_lowerCAmelCase							,       decoder_start_token_id=2							,       early_stopping=_lowerCAmelCase							,       )

 UpperCamelCase     : Tuple	     =       SpeechaTextForConditionalGeneration(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       model.model.load_state_dict(_lowerCAmelCase							,       strict=_lowerCAmelCase					)
 if len(_lowerCAmelCase					) > 0 and not set(_lowerCAmelCase					) <= {
     "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:
  UpperCamelCase     : Union[str, Any]	     =       make_linear_from_emb(model.model.decoder.embed_tokens					)
 else:
  UpperCamelCase     : Tuple	     =       lm_head_weights

 model.save_pretrained(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Any			  =					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.""")
    __lowerCamelCase	:					Optional[Any]			  =					parser.parse_args()
    convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       )						->			list[float]:
 UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       coefficient_matrix.shape
 UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       constant_matrix.shape

 if rowsa != colsa:
  UpperCamelCase     : List[Any]	     =       F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if colsa != 1:
  UpperCamelCase     : Any	     =       F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
  raise ValueError(_lowerCAmelCase					)

 if rowsa != rowsa:
  UpperCamelCase     : Tuple	     =       (
      "Coefficient and constant matrices dimensions must be nxn and nx1 but "
      F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if len(_lowerCAmelCase					) != rowsa:
  UpperCamelCase     : Any	     =       (
      "Number of initial values must be equal to number of rows in coefficient "
      F"""matrix but received {len(_lowerCAmelCase					)} and {rowsa}"""
  )
  raise ValueError(_lowerCAmelCase					)

 if iterations <= 0:
  raise ValueError("Iterations must be at least 1"					)

 UpperCamelCase     : NDArray[floataa]	     =       np.concatenate(
     (coefficient_matrix, constant_matrix)							,       axis=1					)

 UpperCamelCase     ,   UpperCamelCase     : str	     =       table.shape

 strictly_diagonally_dominant(_lowerCAmelCase					)

 # Iterates the whole matrix for given number of times
 for _ in range(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       []
  for row in range(_lowerCAmelCase					):
   UpperCamelCase     : Optional[int]	     =       0
   for col in range(_lowerCAmelCase					):
    if col == row:
     UpperCamelCase     : Union[str, Any]	     =       table[row][col]
    elif col == cols - 1:
     UpperCamelCase     : List[Any]	     =       table[row][col]
    else:
     temp += (-1) * table[row][col] * init_val[col]
   UpperCamelCase     : Dict	     =       (temp + val) / denom
   new_val.append(_lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       new_val

 return [float(_lowerCAmelCase					) for i in new_val]



def 		A_    (					_lowerCAmelCase					)						->			bool:
 UpperCamelCase     ,   UpperCamelCase     : Dict	     =       table.shape

 UpperCamelCase     : List[Any]	     =       True

 for i in range(0							,       _lowerCAmelCase					):
  UpperCamelCase     : Union[str, Any]	     =       0
  for j in range(0							,       cols - 1					):
   if i == j:
    continue
   else:
    total += table[i][j]

  if table[i][i] <= total:
   raise ValueError("Coefficient matrix is not strictly diagonally dominant"					)

 return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
    import doctest

    doctest.testmod()
