
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def 					lowerCamelCase__		(				_lowerCamelCase  )						->Dict:
 _UpperCAmelCase 				=[
     "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(SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__  )
def 					lowerCamelCase__		(				_lowerCamelCase  )						->Dict:
 _UpperCAmelCase 				=list(s_dict.keys()  )
 for key in keys:
  if "transformer_layers" in key:
   _UpperCAmelCase 				=s_dict.pop(SCREAMING_SNAKE_CASE__  )
  elif "subsample" in key:
   _UpperCAmelCase 				=s_dict.pop(SCREAMING_SNAKE_CASE__  )
def 					lowerCamelCase__		(				_lowerCamelCase  )						->Any:
 _UpperCAmelCase 				=emb.weight.shape
 _UpperCAmelCase 				=nn.Linear(SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		bias=SCREAMING_SNAKE_CASE__  )
 _UpperCAmelCase 				=emb.weight.data
 return lin_layer
def 					lowerCamelCase__		(				_lowerCamelCase			,		_lowerCamelCase  )						->Tuple:
 _UpperCAmelCase 				=torch.load(SCREAMING_SNAKE_CASE__			,		map_location="cpu"  )
 _UpperCAmelCase 				=mam_aaa["args"]
 _UpperCAmelCase 				=mam_aaa["model"]
 _UpperCAmelCase 				=state_dict["decoder.output_projection.weight"]

 remove_ignore_keys_(SCREAMING_SNAKE_CASE__  )
 rename_keys(SCREAMING_SNAKE_CASE__  )

 _UpperCAmelCase 				=state_dict["decoder.embed_tokens.weight"].shape[0]

 _UpperCAmelCase 				=args.share_decoder_input_output_embed

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

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

 model.save_pretrained(SCREAMING_SNAKE_CASE__  )


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










"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __lt__(	self	:      int	,      A	:      Dict		):
           return self[-1] < other[-1]



      def __eq__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return self[-1] == other[-1]







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      list[Stack]														=       []
     # sort into stacks
     for element in collection:
          _UpperCAmelCase     :      Any														=       Stack([element]							)
          _UpperCAmelCase     :      str														=       bisect_left(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if i != len(SCREAMING_SNAKE_CASE__							):
               stacks[i].append(SCREAMING_SNAKE_CASE__							)
          else:
               stacks.append(SCREAMING_SNAKE_CASE__							)

    # use a heap-based merge to merge stack efficiently
     _UpperCAmelCase     :      Optional[Any]														=       merge(*(reversed(SCREAMING_SNAKE_CASE__							) for stack in stacks)							)
     return collection


if __name__ == "__main__":
 _lowerCAmelCase							:   Any				=				input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase							:   int				=				[int(item) for item in user_input.split(",")]
 print(patience_sort(unsorted))
