'''simple docstring'''


from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class       _a    :


  """simple docstring"""

  def __init__(		self      :       List[str]   ,				lowercase_      :       Any      ):







    '''simple docstring'''


    lowercase_     =       data
    lowercase_     =       None






class       _a    :


  """simple docstring"""

  def __init__(		self      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       None
    lowercase_     =       None




  def __iter__(		self      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       self.head
    while self.head:
      yield node.data
      lowercase_     =       node.next
      if node == self.head:
        break




  def __len__(		self      :       List[str]      ):







    '''simple docstring'''


    return sum(1 for _ in self      )




  def __repr__(		self      :       List[str]      ):







    '''simple docstring'''


    return "->".join(str(lowercase_      ) for item in iter(self      )      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]   ,				lowercase_      :       Any      ):







    '''simple docstring'''


    self.insert_nth(len(self      )   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Tuple   ,				lowercase_      :       Any      ):







    '''simple docstring'''


    self.insert_nth(0   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       int   ,				lowercase_      :       int   ,				lowercase_      :       Any      ):







    '''simple docstring'''


    if index < 0 or index > len(self      ):
      raise IndexError("""list index out of range."""      )
    lowercase_     =       Node(lowercase_      )
    if self.head is None:
      lowercase_     =       new_node  # first node points itself
      lowercase_     =       lowercase_     =       new_node
    elif index == 0:  # insert at head
      lowercase_     =       self.head
      lowercase_     =       lowercase_     =       new_node
    else:
      lowercase_     =       self.head
      for _ in range(index - 1      ):
        lowercase_     =       temp.next
      lowercase_     =       temp.next
      lowercase_     =       new_node
      if index == len(self      ) - 1:  # insert at tail
        lowercase_     =       new_node




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    return self.delete_nth(0      )




  def 		lowerCamelCase__  (		self      :       Optional[int]      ):







    '''simple docstring'''


    return self.delete_nth(len(self      ) - 1      )




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				lowercase_      :       int = 0      ):







    '''simple docstring'''


    if not 0 <= index < len(self      ):
      raise IndexError("""list index out of range."""      )
    lowercase_     =       self.head
    if self.head == self.tail:  # just one node
      lowercase_     =       lowercase_     =       None
    elif index == 0:  # delete head node
      lowercase_     =       self.tail.next.next
      lowercase_     =       self.head.next
    else:
      lowercase_     =       self.head
      for _ in range(index - 1      ):
        lowercase_     =       temp.next
      lowercase_     =       temp.next
      lowercase_     =       temp.next.next
      if index == len(self      ) - 1:  # delete at tail
        lowercase_     =       temp
    return delete_node.data





  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    return len(self      ) == 0






def   A_      (       )			->None:
  lowercase_     =       CircularLinkedList()
  assert len(SCREAMING_SNAKE_CASE_						) == 0
  assert circular_linked_list.is_empty() is True
  assert str(SCREAMING_SNAKE_CASE_						) == ""

  try:
    circular_linked_list.delete_front()
    raise AssertionError  # This should not happen
  except IndexError:
    assert True  # This should happen

  try:
    circular_linked_list.delete_tail()
    raise AssertionError  # This should not happen
  except IndexError:
    assert True  # This should happen

  try:
    circular_linked_list.delete_nth(-1						)
    raise AssertionError
  except IndexError:
    assert True

  try:
    circular_linked_list.delete_nth(0						)
    raise AssertionError
  except IndexError:
    assert True

  assert circular_linked_list.is_empty() is True
  for i in range(5						):
    assert len(SCREAMING_SNAKE_CASE_						) == i
    circular_linked_list.insert_nth(SCREAMING_SNAKE_CASE_ ,	i + 1						)
  assert str(SCREAMING_SNAKE_CASE_						) == "->".join(str(SCREAMING_SNAKE_CASE_						) for i in range(1 ,	6						)						)

  circular_linked_list.insert_tail(6						)
  assert str(SCREAMING_SNAKE_CASE_						) == "->".join(str(SCREAMING_SNAKE_CASE_						) for i in range(1 ,	7						)						)
  circular_linked_list.insert_head(0						)
  assert str(SCREAMING_SNAKE_CASE_						) == "->".join(str(SCREAMING_SNAKE_CASE_						) for i in range(0 ,	7						)						)

  assert circular_linked_list.delete_front() == 0
  assert circular_linked_list.delete_tail() == 6
  assert str(SCREAMING_SNAKE_CASE_						) == "->".join(str(SCREAMING_SNAKE_CASE_						) for i in range(1 ,	6						)						)
  assert circular_linked_list.delete_nth(2						) == 3

  circular_linked_list.insert_nth(2 ,	3						)
  assert str(SCREAMING_SNAKE_CASE_						) == "->".join(str(SCREAMING_SNAKE_CASE_						) for i in range(1 ,	6						)						)

  assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
       import doctest

       doctest.testmod()


'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   A_      (       SCREAMING_SNAKE_CASE_						)			->List[Any]:
  lowercase_     =       [
      """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   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  lowercase_     =       list(s_dict.keys()						)
  for key in keys:
    if "transformer_layers" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)
    elif "subsample" in key:
      lowercase_     =       s_dict.pop(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  lowercase_     ,	lowercase_     =       emb.weight.shape
  lowercase_     =       nn.Linear(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	bias=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       emb.weight.data
  return lin_layer

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_ ,	map_location="""cpu"""						)
  lowercase_     =       mam_aaa["""args"""]
  lowercase_     =       mam_aaa["""model"""]
  lowercase_     =       state_dict["""decoder.output_projection.weight"""]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
  rename_keys(SCREAMING_SNAKE_CASE_						)

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

  lowercase_     =       args.share_decoder_input_output_embed

  lowercase_     =       [int(SCREAMING_SNAKE_CASE_						) for i in args.conv_kernel_sizes.split(""","""						)]
  lowercase_     =       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=2_00 ,	use_cache=SCREAMING_SNAKE_CASE_ ,	decoder_start_token_id=2 ,	early_stopping=SCREAMING_SNAKE_CASE_ ,	)

  lowercase_     =       SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       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:
    lowercase_     =       make_linear_from_emb(model.model.decoder.embed_tokens						)
  else:
    lowercase_     =       lm_head_weights

  model.save_pretrained(SCREAMING_SNAKE_CASE_						)


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

