"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

lowercase__					       =	{
    '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',
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
lowercase__					       =	[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->str:
  for attribute in key.split('.'   ):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        a__:     str										=	'lm_head'

    a__:     List[str]										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  if weight_type is not None:
    a__:     Tuple										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ).shape
  else:
    a__:     Optional[Any]										=	hf_pointer.shape

  assert hf_shape == value.shape, (
      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":
    a__:     Any										=	value
  elif weight_type == "weight_g":
    a__:     Optional[int]										=	value
  elif weight_type == "weight_v":
    a__:     str										=	value
  elif weight_type == "bias":
    a__:     Optional[Any]										=	value
  else:
    a__:     List[Any]										=	value

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->str:
  a__:     str										=	[]
  a__:     Dict										=	fairseq_model.state_dict()

  a__:     Any										=	hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    a__:     Tuple										=	False
    if "conv_layers" in name:
      load_conv_layer(
          _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     hf_model.config.feat_extract_norm == 'group'		,     )
      a__:     List[str]										=	True
    else:
      for key, mapped_key in MAPPING.items():
        a__:     Tuple										=	'unispeech.' + 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]:
          a__:     Optional[Any]										=	True
          if "*" in mapped_key:
            a__:     List[Any]										=	name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
            a__:     Dict										=	mapped_key.replace('*'		,     _SCREAMING_SNAKE_CASE   )
          if "weight_g" in name:
            a__:     Tuple										=	'weight_g'
          elif "weight_v" in name:
            a__:     str										=	'weight_v'
          elif "bias" in name:
            a__:     Optional[int]										=	'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a__:     List[Any]										=	'weight'
          else:
            a__:     List[Any]										=	None
          set_recursively(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
        continue
    if not is_used:
      unused_weights.append(_SCREAMING_SNAKE_CASE   )

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Tuple:
  a__:     str										=	full_name.split('conv_layers.'   )[-1]
  a__:     List[Any]										=	name.split('.'   )
  a__:     List[Any]										=	int(items[0]   )
  a__:     Tuple										=	int(items[1]   )

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          F'{full_name} has size {value.shape}, but'
          F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
      )
      a__:     int										=	value
      logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          F'{full_name} has size {value.shape}, but'
          F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
      )
      a__:     Dict										=	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:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
          " found."
      )
      a__:     Optional[int]										=	value
      logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          F'{full_name} has size {value.shape}, but'
          F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
      )
      a__:     Union[str, Any]										=	value
      logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
  else:
    unused_weights.append(_SCREAMING_SNAKE_CASE   )







@torch.no_grad()
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=True   )	->Union[str, Any]:
  if config_path is not None:
    a__:     Optional[int]										=	UniSpeechConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     List[str]										=	UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      a__:     Tuple										=	Dictionary.load_from_json(_SCREAMING_SNAKE_CASE   )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      a__:     List[str]										=	target_dict.pad_index
      a__:     Dict										=	target_dict.bos_index
      a__:     List[str]										=	target_dict.eos_index
      a__:     Any										=	len(target_dict.symbols   )
      a__:     Optional[int]										=	os.path.join(_SCREAMING_SNAKE_CASE		,     'vocab.json'   )
      if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_SCREAMING_SNAKE_CASE   )   )
        return
      os.makedirs(_SCREAMING_SNAKE_CASE		,     exist_ok=_SCREAMING_SNAKE_CASE   )
      a__:     Optional[int]										=	target_dict.indices

      # fairseq has the <pad> and <s> switched
      a__:     List[Any]										=	42
      a__:     Optional[Any]										=	43
      with open(_SCREAMING_SNAKE_CASE		,     'w'		,     encoding='utf-8'   ) as vocab_handle:
        json.dump(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      a__:     Dict										=	WavaVecaPhonemeCTCTokenizer(
          _SCREAMING_SNAKE_CASE		,     unk_token=target_dict.unk_word		,     pad_token=target_dict.pad_word		,     bos_token=target_dict.bos_word		,     eos_token=target_dict.eos_word		,     word_delimiter_token='|'		,     do_lower_case=_SCREAMING_SNAKE_CASE		,     )
      a__:     Union[str, Any]										=	True if config.feat_extract_norm == 'layer' else False
      a__:     str										=	WavaVecaFeatureExtractor(
          feature_size=1		,     sampling_rate=16000		,     padding_value=0		,     do_normalize=_SCREAMING_SNAKE_CASE		,     return_attention_mask=_SCREAMING_SNAKE_CASE		,     )
      a__:     List[Any]										=	WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE		,     tokenizer=_SCREAMING_SNAKE_CASE   )
      processor.save_pretrained(_SCREAMING_SNAKE_CASE   )

    a__:     Dict										=	UniSpeechForCTC(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     List[Any]										=	UniSpeechForPreTraining(_SCREAMING_SNAKE_CASE   )

  if is_finetuned:
    a__							,    a__							,    a__:     List[str]										=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]		,     arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   ), 'w2v_path': checkpoint_path}   )
  else:
    a__							,    a__							,    a__:     List[str]										=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

  a__:     str										=	model[0].eval()

  recursively_load_weights(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  hf_unispeech.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 lowercase__					       =	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'
 )
 lowercase__					       =	parser.parse_args()
 convert_unispeech_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )



"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowercase__					       =	TypeVar('T')

lowercase__					       =	Union[List[T], Tuple[T, ...]]
lowercase__					       =	Union[T, List[T], Dict[str, T]]
lowercase__					       =	Union[str, bytes, os.PathLike]


