




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()
lowerCamelCase       :	List[str]									=     logging.get_logger("transformers.models.speecht5")

lowerCamelCase       :	List[str]									=     {
    "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",
}
lowerCamelCase       :	Optional[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",
}
lowerCamelCase       :	Dict									=     {
    "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",
}
lowerCamelCase       :	Union[str, 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",
}
lowerCamelCase       :	Tuple									=     {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
lowerCamelCase       :	Optional[Any]									=     {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
lowerCamelCase       :	Union[str, 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",
}
lowerCamelCase       :	Tuple									=     {
    "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",
}
lowerCamelCase       :	str									=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCamelCase       :	str									=     {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase       :	Optional[Any]									=     {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCamelCase       :	str									=     []
lowerCamelCase       :	str									=     [
    "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",
]
lowerCamelCase       :	Dict									=     IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
lowerCamelCase       :	List[Any]									=     IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
lowerCamelCase       :	Tuple									=     IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: Optional[int]			,							lowercase			: List[str]			,							lowercase			: List[str]			,							lowercase			: str  ):


      '''simple docstring'''



      for attribute in key.split('.'  ):
            lowerCamelCase_	  =					getattr(__a			,							__a  )

      if weight_type is not None:
            lowerCamelCase_	  =					getattr(__a			,							__a  ).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     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Any  ):


      '''simple docstring'''



      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     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[str, Any]			,							lowercase			: Dict			,							lowercase			: Dict  ):


      '''simple docstring'''



      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(__a			,							__a  ):
                  logger.info(f"""{name} was ignored"""  )
                  continue

            lowerCamelCase_	  =					False
            if "conv_layers" in name:
                  load_conv_layer(
                      __a			,							__a			,							__a			,							__a			,							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(__a  )[0].split('.'  )[-2]
                                    lowerCamelCase_	  =					mapped_key.replace('*'			,							__a  )
                              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(__a			,							__a			,							__a			,							__a			,							__a  )
                        continue
            if not is_used:
                  unused_weights.append(__a  )

      logger.warning(f"""Unused weights: {unused_weights}"""  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int			,							lowercase			: Union[str, Any]			,							lowercase			: int			,							lowercase			: Tuple			,							lowercase			: List[Any]  ):


      '''simple docstring'''



      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(__a  )

@torch.no_grad()
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: List[str]			,							lowercase			: List[str]			,							lowercase			: Any=None			,							lowercase			: str=None			,							lowercase			: Union[str, Any]=None			,							):


      '''simple docstring'''



      if config_path is not None:
            lowerCamelCase_	  =					SpeechTaConfig.from_pretrained(__a  )
      else:
            lowerCamelCase_	  =					SpeechTaConfig()

      if task == "s2t":
            lowerCamelCase_	  =					config.max_text_positions
            lowerCamelCase_	  =					SpeechTaForSpeechToText(__a  )
      elif task == "t2s":
            lowerCamelCase_	  =					18_76
            lowerCamelCase_	  =					6_00
            lowerCamelCase_	  =					config.max_speech_positions
            lowerCamelCase_	  =					SpeechTaForTextToSpeech(__a  )
      elif task == "s2s":
            lowerCamelCase_	  =					18_76
            lowerCamelCase_	  =					config.max_speech_positions
            lowerCamelCase_	  =					SpeechTaForSpeechToSpeech(__a  )
      else:
            raise ValueError(f"""Unknown task name: {task}"""  )

      if vocab_path:
            lowerCamelCase_	  =					SpeechTaTokenizer(__a			,							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=__a			,							rstrip=__a  )
            lowerCamelCase_	  =					mask_token
            tokenizer.add_special_tokens({'mask_token': mask_token}  )
            tokenizer.add_tokens(['<ctc_blank>']  )

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

      lowerCamelCase_	  =					torch.load(__a  )
      recursively_load_weights(fairseq_checkpoint['model']			,							__a			,							__a  )

      model.save_pretrained(__a  )

      if repo_id:
            print('Pushing to the hub...'  )
            processor.push_to_hub(__a  )
            model.push_to_hub(__a  )


if __name__ == "__main__":
 lowerCamelCase       :	Optional[Any]									=     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."
 )

 lowerCamelCase       :	Dict									=     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,
 )






import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[Any]  ):


      '''simple docstring'''



      return EnvironmentCommand()







class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 @staticmethod
 def        a__				(       A_      :						ArgumentParser						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					parser.add_parser('env'						)
       download_parser.set_defaults(func=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					huggingface_hub.__version__

       lowerCamelCase_	  =					'not installed'
       lowerCamelCase_	  =					'NA'
       if is_torch_available():
             import torch

             lowerCamelCase_	  =					torch.__version__
             lowerCamelCase_	  =					torch.cuda.is_available()

       lowerCamelCase_	  =					'not installed'
       if is_transformers_available():
             import transformers

             lowerCamelCase_	  =					transformers.__version__

       lowerCamelCase_	  =					'not installed'
       if is_accelerate_available():
             import accelerate

             lowerCamelCase_	  =					accelerate.__version__

       lowerCamelCase_	  =					'not installed'
       if is_xformers_available():
             import xformers

             lowerCamelCase_	  =					xformers.__version__

       lowerCamelCase_	  =					{
           '`diffusers` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'PyTorch version (GPU?)': f"""{pt_version} ({pt_cuda_available})""",
           'Huggingface_hub version': hub_version,
           'Transformers version': transformers_version,
           'Accelerate version': accelerate_version,
           'xFormers version': xformers_version,
           'Using GPU in script?': '<fill in>',
           'Using distributed or parallel set-up in script?': '<fill in>',
       }

       print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'						)
       print(self.format_dict(A_						)						)

       return info





 @staticmethod
 def        a__				(       A_      :						Dict						)     ->			Any:

       """simple docstring"""






       return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]						) + "\n"



