





'''simple docstring'''







def    __UpperCamelCase	(	a    : int					,   a    : list     ) ->Any:
     _enforce_args(snake_case__					,   snake_case__     )
     if n == 0:
          return 0
     snake_case				=			float('''-inf'''     )
     for i in range(1					,   n + 1     ):
          snake_case				=			max(
              snake_case__					,   prices[i - 1] + naive_cut_rod_recursive(n - i					,   snake_case__     )     )

     return max_revue

def    __UpperCamelCase	(	a    : int					,   a    : list     ) ->Optional[Any]:
     _enforce_args(snake_case__					,   snake_case__     )
     snake_case				=			[float('''-inf'''     ) for _ in range(n + 1     )]
     return _top_down_cut_rod_recursive(snake_case__					,   snake_case__					,   snake_case__     )

def    __UpperCamelCase	(	a    : int					,   a    : list					,   a    : list     ) ->Optional[Any]:
     if max_rev[n] >= 0:
          return max_rev[n]
     elif n == 0:
          return 0
     else:
          snake_case				=			float('''-inf'''     )
          for i in range(1					,   n + 1     ):
               snake_case				=			max(
                   snake_case__					,   prices[i - 1] + _top_down_cut_rod_recursive(n - i					,   snake_case__					,   snake_case__     )					,   )

          snake_case				=			max_revenue

     return max_rev[n]

def    __UpperCamelCase	(	a    : int					,   a    : list     ) ->Optional[Any]:
     _enforce_args(snake_case__					,   snake_case__     )

     # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
     # length 0.
     snake_case				=			[float('''-inf'''     ) for _ in range(n + 1     )]
     snake_case				=			0

     for i in range(1					,   n + 1     ):
          snake_case				=			max_rev[i]
          for j in range(1					,   i + 1     ):
               snake_case				=			max(snake_case__					,   prices[j - 1] + max_rev[i - j]     )

          snake_case				=			max_revenue_i

     return max_rev[n]

def    __UpperCamelCase	(	a    : int					,   a    : list     ) ->Union[str, Any]:
     if n < 0:
          snake_case				=			f"""n must be greater than or equal to 0. Got n = {n}"""
          raise ValueError(snake_case__     )

     if n > len(snake_case__     ):
          snake_case				=			(
              '''Each integral piece of rod must have a corresponding price. '''
              f"""Got n = {n} but length of prices = {len(snake_case__     )}"""
          )
          raise ValueError(snake_case__     )

def    __UpperCamelCase	(	) ->List[str]:
     snake_case				=			[6, 10, 12, 15, 20, 23]
     snake_case				=			len(snake_case__     )

     # the best revenue comes from cutting the rod into 6 pieces, each
     # of length 1 resulting in a revenue of 6 * 6 = 36.
     snake_case				=			36

     snake_case				=			top_down_cut_rod(snake_case__					,   snake_case__     )
     snake_case				=			bottom_up_cut_rod(snake_case__					,   snake_case__     )
     snake_case				=			naive_cut_rod_recursive(snake_case__					,   snake_case__     )

     assert expected_max_revenue == max_rev_top_down
     assert max_rev_top_down == max_rev_bottom_up
     assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
   main()








'''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)

