'''simple docstring'''


from timeit import timeit

__snake_case						  =		{
    """MALAYALAM""": True,
    """String""": False,
    """rotor""": True,
    """level""": True,
    """A""": True,
    """BB""": True,
    """ABC""": False,
    """amanaplanacanalpanama""": True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())

def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  lowercase_     =       0
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						) - 1
  while start_i < end_i:
    if s[start_i] == s[end_i]:
      start_i += 1
      end_i -= 1
    else:
      return False
  return True

def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						) // 2
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)

  # We need to traverse till half of the length of string
  # as we can get access of the i'th last element from
  # i'th index.
  # eg: [0,1,2,3,4,5] => 4th index can be accessed
  # with the help of 1st index (i==n-i-1)
  # where n is length of string
  return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE_						)						)

def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  if len(SCREAMING_SNAKE_CASE_						) <= 2:
    return True
  if s[0] == s[len(SCREAMING_SNAKE_CASE_						) - 1]:
    return is_palindrome_recursive(s[1:-1]						)
  else:
    return False

def   A_      (       SCREAMING_SNAKE_CASE_						)			->bool:
  return s == s[::-1]

def   A_      (       SCREAMING_SNAKE_CASE_						)			->None:
  lowercase_     =       f"""all({name}(key) is value for key, value in test_data.items())"""
  lowercase_     =       f"""from __main__ import test_data, {name}"""
  lowercase_     =       50_00_00
  lowercase_     =       timeit(stmt=SCREAMING_SNAKE_CASE_ ,	setup=SCREAMING_SNAKE_CASE_ ,	number=SCREAMING_SNAKE_CASE_						)
  print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""						)


if __name__ == "__main__":
       for key, value in test_data.items():
              assert is_palindrome(key) is is_palindrome_recursive(key)
              assert is_palindrome(key) is is_palindrome_slice(key)
              print(f'''{key:21} {value}''')
       print("""a man a plan a canal panama""")

       # finished 500,000 runs in 0.46793 seconds
       benchmark_function("""is_palindrome_slice""")
       # finished 500,000 runs in 0.85234 seconds
       benchmark_function("""is_palindrome""")
       # finished 500,000 runs in 1.32028 seconds
       benchmark_function("""is_palindrome_recursive""")
       # finished 500,000 runs in 2.08679 seconds
       benchmark_function("""is_palindrome_traversal""")


'''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)

