





'''simple docstring'''







def    __UpperCamelCase	(	a    : list[list]     ) ->list[list]:
 snake_case				=			current_set.copy()
 for row_index, row in enumerate(a     ):
  snake_case				=			row[0]
  for column_index, column in enumerate(a     ):
   if magnitude == 0:
    snake_case				=			column
    continue
   snake_case				=			column / magnitude
    # Subtract to cancel term
 snake_case				=			current_set[0]
 snake_case				=			[first_row]
 snake_case				=			current_set[1::]
 for row in current_set:
  snake_case				=			[]
  # If first term is 0, it is already in form we want, so we preserve it
  if row[0] == 0:
   final_set.append(a     )
   continue
  for column_index in range(len(a     )     ):
   temp_row.append(first_row[column_index] - row[column_index]     )
  final_set.append(a     )
 # Create next recursion iteration set
 if len(final_set[0]     ) != 3:
  snake_case				=			final_set[0]
  snake_case				=			[]
  snake_case				=			[]
  for row in final_set[1::]:
   current_first_column.append(row[0]     )
   next_iteration.append(row[1::]     )
  snake_case				=			simplify(a     )
  for i in range(len(a     )     ):
   resultant[i].insert(0					,   current_first_column[i]     )
  resultant.insert(0					,   a     )
  snake_case				=			resultant
 return final_set

def    __UpperCamelCase	(	a    : list[list]     ) ->list:
 if len(a     ) == 0:
  raise IndexError('''solve_simultaneous() requires n lists of length n+1'''     )
 snake_case				=			len(a     ) + 1
 if any(len(a     ) != _length for item in equations     ):
  raise IndexError('''solve_simultaneous() requires n lists of length n+1'''     )
 for row in equations:
  if any(not isinstance(a					,   (int, float)     ) for column in row     ):
   raise ValueError('''solve_simultaneous() requires lists of integers'''     )
 if len(a     ) == 1:
  return [equations[0][-1] / equations[0][0]]
 snake_case				=			equations.copy()
 if any(0 in row for row in data_set     ):
  snake_case				=			data_set.copy()
  snake_case				=			[]
  for row_index, row in enumerate(a     ):
   if 0 not in row:
    snake_case				=			data_set.pop(a     )
    break
  if not full_row:
   raise ValueError('''solve_simultaneous() requires at least 1 full equation'''     )
  data_set.insert(0					,   a     )
 snake_case				=			data_set.copy()
 snake_case				=			simplify(a     )
 snake_case				=			simplified[::-1]
 snake_case				=			[]
 for row in simplified:
  snake_case				=			row[-1]
  if not solutions:
   if row[-2] == 0:
    solutions.append(0     )
    continue
   solutions.append(current_solution / row[-2]     )
   continue
  snake_case				=			row.copy()[: len(a     ) - 1 :]
  while temp_row[0] == 0:
   temp_row.pop(0     )
  if len(a     ) == 0:
   solutions.append(0     )
   continue
  snake_case				=			temp_row[1::]
  snake_case				=			temp_row[::-1]
  for column_index, column in enumerate(a     ):
   current_solution -= column * solutions[column_index]
  solutions.append(a     )
 snake_case				=			[]
 for item in solutions:
  final.append(float(round(a					,   5     )     )     )
 return final[::-1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _lowercase       						=			[
       [2, 1, 1, 1, 1, 4],
       [1, 2, 1, 1, 1, 5],
       [1, 1, 2, 1, 1, 6],
       [1, 1, 1, 2, 1, 7],
       [1, 1, 1, 1, 2, 8],
   ]
   print(solve_simultaneous(eq))
   print(solve_simultaneous([[4, 2]]))








'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Dict:
 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(a					,   a     )

def    __UpperCamelCase	(	a    : Optional[Any]     ) ->int:
 snake_case				=			list(s_dict.keys()     )
 for key in keys:
  if "transformer_layers" in key:
   snake_case				=			s_dict.pop(a     )
  elif "subsample" in key:
   snake_case				=			s_dict.pop(a     )

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Optional[int]:
 snake_case				,		snake_case				=			emb.weight.shape
 snake_case				=			nn.Linear(a					,   a					,   bias=a     )
 snake_case				=			emb.weight.data
 return lin_layer

def    __UpperCamelCase	(	a    : Any					,   a    : Tuple     ) ->Tuple:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 snake_case				=			mam_aaa['''args''']
 snake_case				=			mam_aaa['''model''']
 snake_case				=			state_dict['''decoder.output_projection.weight''']

 remove_ignore_keys_(a     )
 rename_keys(a     )

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

 snake_case				=			args.share_decoder_input_output_embed

 snake_case				=			[int(a     ) for i in args.conv_kernel_sizes.split(''','''     )]
 snake_case				=			SpeechaTextConfig(
     vocab_size=a					,   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(a     )					,   conv_channels=args.conv_channels					,   conv_kernel_sizes=a					,   input_feat_per_channel=args.input_feat_per_channel					,   input_channels=args.input_channels					,   tie_word_embeddings=a					,   num_beams=5					,   max_length=200					,   use_cache=a					,   decoder_start_token_id=2					,   early_stopping=a					,   )

 snake_case				=			SpeechaTextForConditionalGeneration(a     )
 snake_case				,		snake_case				=			model.model.load_state_dict(a					,   strict=a     )
 if len(a     ) > 0 and not set(a     ) <= {
     "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				=			make_linear_from_emb(model.model.decoder.embed_tokens     )
 else:
  snake_case				=			lm_head_weights

 model.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			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.')
   _lowercase       						=			parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

