






"""simple docstring"""







import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_UpperCamelCase					      =  logging.get_logger(__name__)







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      '''AutoTokenizer'''
    SCREAMING_SNAKE_CASE		=      ['''tokenizer''']

    SCREAMING_SNAKE_CASE		=      {
        '''semantic_prompt''': 1,
        '''coarse_prompt''': 2,
        '''fine_prompt''': 2,
    }






    def __init__(			self     ,A     ,A=None ):
          super().__init__(A )

          UpperCAmelCase          =	speaker_embeddings






    @classmethod
    def     _UpperCamelCase							(			cls     ,A     ,A="speaker_embeddings_path.json"     ,**A ):

          if speaker_embeddings_dict_path is not None:
                UpperCAmelCase          =	get_file_from_repo(
                    A     ,A     ,subfolder=kwargs.pop("""subfolder"""     ,A )     ,cache_dir=kwargs.pop("""cache_dir"""     ,A )     ,force_download=kwargs.pop("""force_download"""     ,A )     ,proxies=kwargs.pop("""proxies"""     ,A )     ,resume_download=kwargs.pop("""resume_download"""     ,A )     ,local_files_only=kwargs.pop("""local_files_only"""     ,A )     ,use_auth_token=kwargs.pop("""use_auth_token"""     ,A )     ,revision=kwargs.pop("""revision"""     ,A )     ,)
                if speaker_embeddings_path is None:
                      logger.warning(
                          F'''`{os.path.join(A     ,A )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
                      UpperCAmelCase          =	None
                else:
                      with open(A ) as speaker_embeddings_json:
                            UpperCAmelCase          =	json.load(A )
          else:
                UpperCAmelCase          =	None

          UpperCAmelCase          =	AutoTokenizer.from_pretrained(A     ,**A )

          return cls(tokenizer=A     ,speaker_embeddings=A )






    def     _UpperCamelCase							(			self     ,A     ,A="speaker_embeddings_path.json"     ,A="speaker_embeddings"     ,A = False     ,**A     ,):
          if self.speaker_embeddings is not None:
                os.makedirs(os.path.join(A     ,A     ,"""v2""" )     ,exist_ok=A )

                UpperCAmelCase          =	{}

                UpperCAmelCase          =	save_directory

                for prompt_key in self.speaker_embeddings:
                      if prompt_key != "repo_or_path":
                            UpperCAmelCase          =	self._load_voice_preset(A )

                            UpperCAmelCase          =	{}
                            for key in self.speaker_embeddings[prompt_key]:
                                  np.save(
                                      os.path.join(
                                          embeddings_dict["""repo_or_path"""]     ,A     ,F'''{prompt_key}_{key}''' )     ,voice_preset[key]     ,allow_pickle=A     ,)
                                  UpperCAmelCase          =	os.path.join(A     ,F'''{prompt_key}_{key}.npy''' )

                            UpperCAmelCase          =	tmp_dict

                with open(os.path.join(A     ,A )     ,"""w""" ) as fp:
                      json.dump(A     ,A )

          super().save_pretrained(A     ,A     ,**A )






    def     _UpperCamelCase							(			self     ,A = None     ,**A ):
          UpperCAmelCase          =	self.speaker_embeddings[voice_preset]

          UpperCAmelCase          =	{}
          for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                if key not in voice_preset_paths:
                      raise ValueError(
                          F'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )

                UpperCAmelCase          =	get_file_from_repo(
                    self.speaker_embeddings.get("""repo_or_path"""     ,"""/""" )     ,voice_preset_paths[key]     ,subfolder=kwargs.pop("""subfolder"""     ,A )     ,cache_dir=kwargs.pop("""cache_dir"""     ,A )     ,force_download=kwargs.pop("""force_download"""     ,A )     ,proxies=kwargs.pop("""proxies"""     ,A )     ,resume_download=kwargs.pop("""resume_download"""     ,A )     ,local_files_only=kwargs.pop("""local_files_only"""     ,A )     ,use_auth_token=kwargs.pop("""use_auth_token"""     ,A )     ,revision=kwargs.pop("""revision"""     ,A )     ,)
                if path is None:
                      raise ValueError(
                          F'''`{os.path.join(self.speaker_embeddings.get('repo_or_path'     ,'/' )     ,voice_preset_paths[key] )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.''' )

                UpperCAmelCase          =	np.load(A )

          return voice_preset_dict






    def     _UpperCamelCase							(			self     ,A = None ):
          for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                if key not in voice_preset:
                      raise ValueError(F'''Voice preset unrecognized, missing {key} as a key.''' )

                if not isinstance(voice_preset[key]     ,np.ndarray ):
                      raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )

                if len(voice_preset[key].shape ) != self.preset_shape[key]:
                      raise ValueError(F'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )






    def __call__(			self     ,A=None     ,A=None     ,A="pt"     ,A=256     ,A=False     ,A=True     ,A=False     ,**A     ,):
          if voice_preset is not None and not isinstance(A     ,A ):
                if (
                    isinstance(A     ,A )
                    and self.speaker_embeddings is not None
                    and voice_preset in self.speaker_embeddings
                ):
                      UpperCAmelCase          =	self._load_voice_preset(A )

                else:
                      if isinstance(A     ,A ) and not voice_preset.endswith(""".npz""" ):
                            UpperCAmelCase          =	voice_preset + """.npz"""

                      UpperCAmelCase          =	np.load(A )

          if voice_preset is not None:
                self._validate_voice_preset_dict(A     ,**A )
                UpperCAmelCase          =	BatchFeature(data=A     ,tensor_type=A )

          UpperCAmelCase          =	self.tokenizer(
              A     ,return_tensors=A     ,padding="""max_length"""     ,max_length=A     ,return_attention_mask=A     ,return_token_type_ids=A     ,add_special_tokens=A     ,**A     ,)

          if voice_preset is not None:
                UpperCAmelCase          =	voice_preset

          return encoded_text











"""simple docstring"""







import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
_UpperCamelCase					      =  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      for attribute in key.split("""."""							):
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							)

      if weight_type is not None:
            UpperCAmelCase          =	getattr(_snake_case				,	_snake_case							).shape
      else:
            UpperCAmelCase          =	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          =	value
      elif weight_type == "weight_g":
            UpperCAmelCase          =	value
      elif weight_type == "weight_v":
            UpperCAmelCase          =	value
      elif weight_type == "bias":
            UpperCAmelCase          =	value
      else:
            UpperCAmelCase          =	value

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

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	[]
      UpperCAmelCase          =	fairseq_model.state_dict()

      UpperCAmelCase          =	hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase          =	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          =	True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase          =	"""unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
                              if "layer_norm_for_extract" in name and (".".join(name.split("""."""							)[:-1]							) != key):
                                    # special case since naming is very similar
                                    continue
                              UpperCAmelCase          =	True
                              if "*" in mapped_key:
                                    UpperCAmelCase          =	name.split(_snake_case							)[0].split("""."""							)[-2]
                                    UpperCAmelCase          =	mapped_key.replace("""*"""				,	_snake_case							)
                              if "weight_g" in name:
                                    UpperCAmelCase          =	"""weight_g"""
                              elif "weight_v" in name:
                                    UpperCAmelCase          =	"""weight_v"""
                              elif "bias" in name:
                                    UpperCAmelCase          =	"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase          =	"""weight"""
                              else:
                                    UpperCAmelCase          =	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 _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	full_name.split("""conv_layers."""							)[-1]
      UpperCAmelCase          =	name.split("""."""							)
      UpperCAmelCase          =	int(items[0]							)
      UpperCAmelCase          =	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          =	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          =	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[layer_id].layer_norm.bias.data.shape} was found.'''							)
                  UpperCAmelCase          =	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[layer_id].layer_norm.weight.data.shape} was found.'''							)
                  UpperCAmelCase          =	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 _a			(		_snake_case				,	_snake_case				,	_snake_case=None				,	_snake_case=None				,	_snake_case=True							):







      """simple docstring"""






      if config_path is not None:
            UpperCAmelCase          =	UniSpeechSatConfig.from_pretrained(_snake_case							)
      else:
            UpperCAmelCase          =	UniSpeechSatConfig()

      UpperCAmelCase          =	""""""

      if is_finetuned:
            UpperCAmelCase          =	UniSpeechSatForCTC(_snake_case							)
      else:
            UpperCAmelCase          =	UniSpeechSatForPreTraining(_snake_case							)

      UpperCAmelCase      , UpperCAmelCase      , UpperCAmelCase          =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]				,	arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
      UpperCAmelCase          =	model[0].eval()

      recursively_load_weights(_snake_case				,	_snake_case							)

      hf_wavavec.save_pretrained(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  argparse.ArgumentParser()
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument(
      """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
  )
  _UpperCamelCase					      =  parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )



