'''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()
SCREAMING_SNAKE_CASE_	   =			logging.get_logger('transformers.models.speecht5')

SCREAMING_SNAKE_CASE_	   =			{
    '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',
}
SCREAMING_SNAKE_CASE_	   =			{
    'text_encoder_prenet.encoder_prenet.0': 'speecht5.encoder.prenet.embed_tokens',
    'text_encoder_prenet.encoder_prenet.1.alpha': 'speecht5.encoder.prenet.encode_positions.alpha',
}
SCREAMING_SNAKE_CASE_	   =			{
    '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',
}
SCREAMING_SNAKE_CASE_	   =			{
    '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',
}
SCREAMING_SNAKE_CASE_	   =			{
    'text_decoder_prenet.embed_tokens': 'speecht5.decoder.prenet.embed_tokens',
}
SCREAMING_SNAKE_CASE_	   =			{
    'text_decoder_postnet.output_projection': 'text_decoder_postnet.lm_head',
}
SCREAMING_SNAKE_CASE_	   =			{
    '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',
}
SCREAMING_SNAKE_CASE_	   =			{
    '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',
}
SCREAMING_SNAKE_CASE_	   =			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE_	   =			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE_	   =			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE_	   =			[]
SCREAMING_SNAKE_CASE_	   =			[
    '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',
]
SCREAMING_SNAKE_CASE_	   =			IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'speech_decoder_prenet.*',
    'speech_decoder_postnet.*',
]
SCREAMING_SNAKE_CASE_	   =			IGNORE_KEYS + [
    'encoder.proj',
    'speech_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]
SCREAMING_SNAKE_CASE_	   =			IGNORE_KEYS + [
    'encoder.proj',
    'text_encoder_prenet.*',
    'text_decoder_prenet.*',
    'text_decoder_postnet.*',
]






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				Optional[Any]   ,    _lowercase    :				Any   ,    _lowercase    :				Tuple   ,    _lowercase    :				Tuple     )							->	Tuple:

  for attribute in key.split("""."""     ):
    __UpperCAmelCase:					List[Any]		= getattr(snake_case__   ,    snake_case__     )

  if weight_type is not None:
    __UpperCAmelCase:					Optional[Any]		= getattr(snake_case__   ,    snake_case__     ).shape
  else:
    __UpperCAmelCase:					List[str]		= 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":
    __UpperCAmelCase:					Optional[Any]		= value
  elif weight_type == "weight_g":
    __UpperCAmelCase:					List[str]		= value
  elif weight_type == "weight_v":
    __UpperCAmelCase:					int		= value
  elif weight_type == "bias":
    __UpperCAmelCase:					Tuple		= value
  elif weight_type == "running_mean":
    __UpperCAmelCase:					Optional[Any]		= value
  elif weight_type == "running_var":
    __UpperCAmelCase:					Optional[Any]		= value
  elif weight_type == "num_batches_tracked":
    __UpperCAmelCase:					Optional[Any]		= value
  else:
    __UpperCAmelCase:					int		= value

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






def  UpperCamelCase__					(					_lowercase    :				Any   ,    _lowercase    :				int     )							->	Optional[Any]:

  for key in ignore_keys:
    if key.endswith(""".*"""     ):
      if name.startswith(key[:-1]     ):
        return True
    elif ".*." in key:
      __UpperCAmelCase:					int		= key.split(""".*."""     )
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				int   ,    _lowercase    :				Any     )							->	Union[str, Any]:
  __UpperCAmelCase:					Tuple		= []

  if task == "s2t":
    __UpperCAmelCase:					int		= hf_model.speechta.encoder.prenet.feature_encoder
    __UpperCAmelCase:					Tuple		= MAPPING_S2T
    __UpperCAmelCase:					Union[str, Any]		= IGNORE_KEYS_S2T
  elif task == "t2s":
    __UpperCAmelCase:					int		= None
    __UpperCAmelCase:					Union[str, Any]		= MAPPING_T2S
    __UpperCAmelCase:					List[str]		= IGNORE_KEYS_T2S
  elif task == "s2s":
    __UpperCAmelCase:					List[Any]		= hf_model.speechta.encoder.prenet.feature_encoder
    __UpperCAmelCase:					Tuple		= MAPPING_S2S
    __UpperCAmelCase:					Tuple		= IGNORE_KEYS_S2S
  else:
    raise ValueError(F'''Unsupported task: {task}'''     )

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

    __UpperCAmelCase:					Optional[Any]		= False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__   ,    snake_case__   ,    snake_case__   ,    snake_case__   ,    hf_model.config.feat_extract_norm == """group"""   ,    )
      __UpperCAmelCase:					Union[str, Any]		= 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:
          __UpperCAmelCase:					Union[str, Any]		= key.split(""".*."""     )
          if prefix in name and suffix in name:
            __UpperCAmelCase:					Optional[int]		= suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          __UpperCAmelCase:					Union[str, Any]		= True
          if "*" in mapped_key:
            __UpperCAmelCase:					int		= name.split(snake_case__     )[0].split("""."""     )[-2]
            __UpperCAmelCase:					str		= mapped_key.replace("""*"""   ,    snake_case__     )
          if "weight_g" in name:
            __UpperCAmelCase:					str		= """weight_g"""
          elif "weight_v" in name:
            __UpperCAmelCase:					Dict		= """weight_v"""
          elif "bias" in name:
            __UpperCAmelCase:					List[str]		= """bias"""
          elif "weight" in name:
            __UpperCAmelCase:					Optional[int]		= """weight"""
          elif "running_mean" in name:
            __UpperCAmelCase:					int		= """running_mean"""
          elif "running_var" in name:
            __UpperCAmelCase:					int		= """running_var"""
          elif "num_batches_tracked" in name:
            __UpperCAmelCase:					Union[str, Any]		= """num_batches_tracked"""
          else:
            __UpperCAmelCase:					Dict		= None
          set_recursively(snake_case__   ,    snake_case__   ,    snake_case__   ,    snake_case__   ,    snake_case__     )
        continue
    if not is_used:
      unused_weights.append(snake_case__     )

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






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[str]   ,    _lowercase    :				str   ,    _lowercase    :				Optional[int]   ,    _lowercase    :				Dict     )							->	str:
  __UpperCAmelCase:					Dict		= full_name.split("""conv_layers."""     )[-1]
  __UpperCAmelCase:					Dict		= name.split("""."""     )
  __UpperCAmelCase:					Union[str, Any]		= int(items[0]     )
  __UpperCAmelCase:					List[str]		= 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.'''     )
      __UpperCAmelCase:					Optional[int]		= 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.'''     )
      __UpperCAmelCase:					str		= 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.'''     )
      __UpperCAmelCase:					List[str]		= 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.'''     )
      __UpperCAmelCase:					Optional[Any]		= value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
  else:
    unused_weights.append(snake_case__     )






@torch.no_grad()
def  UpperCamelCase__					(					_lowercase    :				Tuple   ,    _lowercase    :				int   ,    _lowercase    :				Any   ,    _lowercase    :				List[Any]=None   ,    _lowercase    :				Tuple=None   ,    _lowercase    :				Dict=None   ,    )							->	List[str]:

  if config_path is not None:
    __UpperCAmelCase:					List[Any]		= SpeechTaConfig.from_pretrained(snake_case__     )
  else:
    __UpperCAmelCase:					List[Any]		= SpeechTaConfig()

  if task == "s2t":
    __UpperCAmelCase:					Union[str, Any]		= config.max_text_positions
    __UpperCAmelCase:					Optional[Any]		= SpeechTaForSpeechToText(snake_case__     )
  elif task == "t2s":
    __UpperCAmelCase:					List[Any]		= 1_8_7_6
    __UpperCAmelCase:					Tuple		= 6_0_0
    __UpperCAmelCase:					Union[str, Any]		= config.max_speech_positions
    __UpperCAmelCase:					Dict		= SpeechTaForTextToSpeech(snake_case__     )
  elif task == "s2s":
    __UpperCAmelCase:					List[str]		= 1_8_7_6
    __UpperCAmelCase:					List[str]		= config.max_speech_positions
    __UpperCAmelCase:					Dict		= SpeechTaForSpeechToSpeech(snake_case__     )
  else:
    raise ValueError(F'''Unknown task name: {task}'''     )

  if vocab_path:
    __UpperCAmelCase:					str		= SpeechTaTokenizer(snake_case__   ,    model_max_length=config.max_text_positions     )

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

  __UpperCAmelCase:					List[Any]		= SpeechTaFeatureExtractor()
  __UpperCAmelCase:					Optional[Any]		= SpeechTaProcessor(tokenizer=snake_case__   ,    feature_extractor=snake_case__     )
  processor.save_pretrained(snake_case__     )

  __UpperCAmelCase:					Optional[Any]		= torch.load(snake_case__     )
  recursively_load_weights(fairseq_checkpoint["""model"""]   ,    snake_case__   ,    snake_case__     )

  model.save_pretrained(snake_case__     )

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


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			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.'
    )

    SCREAMING_SNAKE_CASE_	   =			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'''






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)