


'''simple docstring'''

import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
a_  :				Optional[int]						= logging.get_logger("""transformers.models.speecht5""")

a_  :				Tuple						= {
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
a_  :				int						= {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
a_  :				List[Any]						= {
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
a_  :				Optional[Any]						= {
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
a_  :				str						= {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
a_  :				Tuple						= {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
a_  :				List[Any]						= {
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
a_  :				Union[str, Any]						= {
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
a_  :				Union[str, Any]						= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
a_  :				Tuple						= {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a_  :				Optional[Any]						= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
a_  :				int						= []
a_  :				int						= [
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
a_  :				Dict						= IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
a_  :				str						= IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
a_  :				Tuple						= IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]





def __snake_case					(      UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Dict		):
 for attribute in key.split("."		):
  lowerCamelCase_      =  getattr(__lowerCamelCase					,				__lowerCamelCase		)

 if weight_type is not None:
  lowerCamelCase_      =  getattr(__lowerCamelCase					,				__lowerCamelCase		).shape
 else:
  lowerCamelCase_      =  hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
      F''' {value.shape} for {full_name}'''		)

 if weight_type == "weight":
  lowerCamelCase_      =  value
 elif weight_type == "weight_g":
  lowerCamelCase_      =  value
 elif weight_type == "weight_v":
  lowerCamelCase_      =  value
 elif weight_type == "bias":
  lowerCamelCase_      =  value
 elif weight_type == "running_mean":
  lowerCamelCase_      =  value
 elif weight_type == "running_var":
  lowerCamelCase_      =  value
 elif weight_type == "num_batches_tracked":
  lowerCamelCase_      =  value
 else:
  lowerCamelCase_      =  value

 logger.info(F'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'''		)





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Optional[int]		):
 for key in ignore_keys:
  if key.endswith(".*"		):
   if name.startswith(key[:-1]		):
    return True
  elif ".*." in key:
   lowerCamelCase_ ,lowerCamelCase_      =  key.split(".*."		)
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False





def __snake_case					(      UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  []

 if task == "s2t":
  lowerCamelCase_      =  hf_model.speechta.encoder.prenet.feature_encoder
  lowerCamelCase_      =  MAPPING_S2T
  lowerCamelCase_      =  IGNORE_KEYS_S2T
 elif task == "t2s":
  lowerCamelCase_      =  None
  lowerCamelCase_      =  MAPPING_T2S
  lowerCamelCase_      =  IGNORE_KEYS_T2S
 elif task == "s2s":
  lowerCamelCase_      =  hf_model.speechta.encoder.prenet.feature_encoder
  lowerCamelCase_      =  MAPPING_S2S
  lowerCamelCase_      =  IGNORE_KEYS_S2S
 else:
  raise ValueError(F'''Unsupported task: {task}'''		)

 for name, value in fairseq_dict.items():
  if should_ignore(__lowerCamelCase					,				__lowerCamelCase		):
   logger.info(F'''{name} was ignored'''		)
   continue

  lowerCamelCase_      =  False
  if "conv_layers" in name:
   load_conv_layer(
       __lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase					,				hf_model.config.feat_extract_norm == "group"					,				)
   lowerCamelCase_      =  True
  else:
   for key, mapped_key in MAPPING.items():
    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

    if "*" in key:
     lowerCamelCase_ ,lowerCamelCase_      =  key.split(".*."		)
     if prefix in name and suffix in name:
      lowerCamelCase_      =  suffix

          # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
    if key in name:
     lowerCamelCase_      =  True
     if "*" in mapped_key:
      lowerCamelCase_      =  name.split(__lowerCamelCase		)[0].split("."		)[-2]
      lowerCamelCase_      =  mapped_key.replace("*"					,				__lowerCamelCase		)
     if "weight_g" in name:
      lowerCamelCase_      =  "weight_g"
     elif "weight_v" in name:
      lowerCamelCase_      =  "weight_v"
     elif "bias" in name:
      lowerCamelCase_      =  "bias"
     elif "weight" in name:
      lowerCamelCase_      =  "weight"
     elif "running_mean" in name:
      lowerCamelCase_      =  "running_mean"
     elif "running_var" in name:
      lowerCamelCase_      =  "running_var"
     elif "num_batches_tracked" in name:
      lowerCamelCase_      =  "num_batches_tracked"
     else:
      lowerCamelCase_      =  None
     set_recursively(__lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase		)
    continue
  if not is_used:
   unused_weights.append(__lowerCamelCase		)

 logger.warning(F'''Unused weights: {unused_weights}'''		)





def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  full_name.split("conv_layers."		)[-1]
 lowerCamelCase_      =  name.split("."		)
 lowerCamelCase_      =  int(items[0]		)
 lowerCamelCase_      =  int(items[1]		)

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''		)
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''		)
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''		)
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        F'''{full_name} has size {value.shape}, but'''
        F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''		)
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
 else:
  unused_weights.append(__lowerCamelCase		)





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Optional[int]=None					,				UpperCAmelCase_						:						int=None					,				UpperCAmelCase_						:						Union[str, Any]=None					,				):
 if config_path is not None:
  lowerCamelCase_      =  SpeechTaConfig.from_pretrained(__lowerCamelCase		)
 else:
  lowerCamelCase_      =  SpeechTaConfig()

 if task == "s2t":
  lowerCamelCase_      =  config.max_text_positions
  lowerCamelCase_      =  SpeechTaForSpeechToText(__lowerCamelCase		)
 elif task == "t2s":
  lowerCamelCase_      =  1876
  lowerCamelCase_      =  600
  lowerCamelCase_      =  config.max_speech_positions
  lowerCamelCase_      =  SpeechTaForTextToSpeech(__lowerCamelCase		)
 elif task == "s2s":
  lowerCamelCase_      =  1876
  lowerCamelCase_      =  config.max_speech_positions
  lowerCamelCase_      =  SpeechTaForSpeechToSpeech(__lowerCamelCase		)
 else:
  raise ValueError(F'''Unknown task name: {task}'''		)

 if vocab_path:
  lowerCamelCase_      =  SpeechTaTokenizer(__lowerCamelCase					,				model_max_length=config.max_text_positions		)

  # Mask token behaves like a normal word, i.e. include the space before it
  lowerCamelCase_      =  AddedToken("<mask>"					,				lstrip=__lowerCamelCase					,				rstrip=__lowerCamelCase		)
  lowerCamelCase_      =  mask_token
  tokenizer.add_special_tokens({"mask_token": mask_token}		)
  tokenizer.add_tokens(["<ctc_blank>"]		)

 lowerCamelCase_      =  SpeechTaFeatureExtractor()
 lowerCamelCase_      =  SpeechTaProcessor(tokenizer=__lowerCamelCase					,				feature_extractor=__lowerCamelCase		)
 processor.save_pretrained(__lowerCamelCase		)

 lowerCamelCase_      =  torch.load(__lowerCamelCase		)
 recursively_load_weights(fairseq_checkpoint["model"]					,				__lowerCamelCase					,				__lowerCamelCase		)

 model.save_pretrained(__lowerCamelCase		)

 if repo_id:
  print("Pushing to the hub..."		)
  processor.push_to_hub(__lowerCamelCase		)
  model.push_to_hub(__lowerCamelCase		)


if __name__ == "__main__":
     a_  :				Dict						= argparse.ArgumentParser()
     parser.add_argument(
         """--task""",
         default="""s2t""",
         type=str,
         help="""Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.""",
     )
     parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
     )

     a_  :				str						= parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )





"""simple docstring"""



from math import pow
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int ,					__lowerCamelCase   :       int ,					)     ->					tuple[int, int]:
  if current_sum == needed_sum:
    # If the sum of the powers is equal to needed_sum, then we have a solution.
    solutions_count += 1
    return current_sum, solutions_count

  _snake_case					     =       int(pow(__lowerCamelCase ,					__lowerCamelCase		)		)
  if current_sum + i_to_n <= needed_sum:
    # If the sum of the powers is less than needed_sum, then continue adding powers.
    current_sum += i_to_n
    _snake_case     ,					_snake_case					     =       backtrack(
        __lowerCamelCase ,					__lowerCamelCase ,					current_number + 1 ,					__lowerCamelCase ,					__lowerCamelCase		)
    current_sum -= i_to_n
  if i_to_n < needed_sum:
    # If the power of i is less than needed_sum, then try with the next power.
    _snake_case     ,					_snake_case					     =       backtrack(
        __lowerCamelCase ,					__lowerCamelCase ,					current_number + 1 ,					__lowerCamelCase ,					__lowerCamelCase		)
  return current_sum, solutions_count
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       int		)     ->					int:
  if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
    raise ValueError(
        '''Invalid input\n'''
        '''needed_sum must be between 1 and 1000, power between 2 and 10.'''		)

  return backtrack(__lowerCamelCase ,					__lowerCamelCase ,					1 ,					0 ,					0		)[1]  # Return the solutions_count


if __name__ == "__main__":
 import doctest

 doctest.testmod()



