






'''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()
a_ 		=       logging.get_logger(__name__)

a_ 		=       {
    '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',
}
a_ 		=       [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def        _a(     UpperCamelCase__ :  str,		UpperCamelCase__ :  List[str],		UpperCamelCase__ :  List[Any],		UpperCamelCase__ :  Any,		UpperCamelCase__ :  str,		UpperCamelCase__ :  Any							):




      '''simple docstring'''






      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
                        SCREAMING_SNAKE_CASE__   : List[str]													='''lm_head'''

            SCREAMING_SNAKE_CASE__   : Any													=getattr(lowerCamelCase__,		lowerCamelCase__							)

      if weight_type is not None:
            SCREAMING_SNAKE_CASE__   : Any													=getattr(lowerCamelCase__,		lowerCamelCase__							).shape
      else:
            SCREAMING_SNAKE_CASE__   : Optional[int]													=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":
            SCREAMING_SNAKE_CASE__   : int													=value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE__   : Optional[Any]													=value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE__   : Dict													=value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE__   : Tuple													=value
      else:
            SCREAMING_SNAKE_CASE__   : List[Any]													=value

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





def        _a(     UpperCamelCase__ :  int,		UpperCamelCase__ :  Optional[int],		UpperCamelCase__ :  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : List[str]													=[]
      SCREAMING_SNAKE_CASE__   : Optional[int]													=fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE__   : int													=hf_model.unispeech.feature_extractor

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

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





def        _a(     UpperCamelCase__ :  Optional[Any],		UpperCamelCase__ :  Optional[Any],		UpperCamelCase__ :  Union[str, Any],		UpperCamelCase__ :  Union[str, Any],		UpperCamelCase__ :  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : List[str]													=full_name.split('''conv_layers.'''							)[-1]
      SCREAMING_SNAKE_CASE__   : Union[str, Any]													=name.split('''.'''							)
      SCREAMING_SNAKE_CASE__   : str													=int(items[0]							)
      SCREAMING_SNAKE_CASE__   : int													=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."
                  )
                  SCREAMING_SNAKE_CASE__   : Dict													=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."
                  )
                  SCREAMING_SNAKE_CASE__   : List[Any]													=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."
                  )
                  SCREAMING_SNAKE_CASE__   : List[Any]													=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."
                  )
                  SCREAMING_SNAKE_CASE__   : Optional[int]													=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        _a(     UpperCamelCase__ :  List[str],		UpperCamelCase__ :  Any,		UpperCamelCase__ :  Any=None,		UpperCamelCase__ :  Dict=None,		UpperCamelCase__ :  Tuple=True							):




      '''simple docstring'''






      if config_path is not None:
            SCREAMING_SNAKE_CASE__   : List[Any]													=UniSpeechConfig.from_pretrained(lowerCamelCase__							)
      else:
            SCREAMING_SNAKE_CASE__   : int													=UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  SCREAMING_SNAKE_CASE__   : List[str]													=Dictionary.load_from_json(lowerCamelCase__							)

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

                  # fairseq has the <pad> and <s> switched
                  SCREAMING_SNAKE_CASE__   : Union[str, Any]													=4_2
                  SCREAMING_SNAKE_CASE__   : str													=4_3
                  with open(lowerCamelCase__,		'''w''',		encoding='''utf-8'''							) as vocab_handle:
                        json.dump(lowerCamelCase__,		lowerCamelCase__							)
                  SCREAMING_SNAKE_CASE__   : Union[str, Any]													=WavaVecaPhonemeCTCTokenizer(
                      lowerCamelCase__,		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=lowerCamelCase__,		)
                  SCREAMING_SNAKE_CASE__   : Union[str, Any]													=True if config.feat_extract_norm == '''layer''' else False
                  SCREAMING_SNAKE_CASE__   : Dict													=WavaVecaFeatureExtractor(
                      feature_size=1,		sampling_rate=1_6_0_0_0,		padding_value=0,		do_normalize=lowerCamelCase__,		return_attention_mask=lowerCamelCase__,		)
                  SCREAMING_SNAKE_CASE__   : List[str]													=WavaVecaProcessor(feature_extractor=lowerCamelCase__,		tokenizer=lowerCamelCase__							)
                  processor.save_pretrained(lowerCamelCase__							)

            SCREAMING_SNAKE_CASE__   : List[Any]													=UniSpeechForCTC(lowerCamelCase__							)
      else:
            SCREAMING_SNAKE_CASE__   : Tuple													=UniSpeechForPreTraining(lowerCamelCase__							)

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

      SCREAMING_SNAKE_CASE__   : Tuple													=model[0].eval()

      recursively_load_weights(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__							)

      hf_unispeech.save_pretrained(lowerCamelCase__							)


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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A				={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''WavLMForAudioFrameClassification''',
			    '''WavLMForCTC''',
			    '''WavLMForSequenceClassification''',
			    '''WavLMForXVector''',
			    '''WavLMModel''',
			    '''WavLMPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


