





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()
lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """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""",
}
lowerCAmelCase_						   =    [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Any    ,							lowerCAmelCase:	Union[str, Any]    ,							lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	Tuple				)->		Union[str, Any]:
  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
        _snake_case   :				Union[str, Any]														=	'lm_head'

    _snake_case   :				str														=	getattr(lowerCAmelCase    ,							lowerCAmelCase				)

  if weight_type is not None:
    _snake_case   :				Tuple														=	getattr(lowerCAmelCase    ,							lowerCAmelCase				).shape
  else:
    _snake_case   :				List[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":
    _snake_case   :				List[Any]														=	value
  elif weight_type == "weight_g":
    _snake_case   :				List[Any]														=	value
  elif weight_type == "weight_v":
    _snake_case   :				str														=	value
  elif weight_type == "bias":
    _snake_case   :				List[Any]														=	value
  else:
    _snake_case   :				Tuple														=	value

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


def     lowerCamelCase_	(      lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	Any				)->		int:
  _snake_case   :				str														=	[]
  _snake_case   :				str														=	fairseq_model.state_dict()

  _snake_case   :				Tuple														=	hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    _snake_case   :				Optional[int]														=	False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase    ,							lowerCAmelCase    ,							lowerCAmelCase    ,							lowerCAmelCase    ,							hf_model.config.feat_extract_norm == 'group'    ,							)
      _snake_case   :				List[Any]														=	True
    else:
      for key, mapped_key in MAPPING.items():
        _snake_case   :				Any														=	'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]:
          _snake_case   :				Optional[int]														=	True
          if "*" in mapped_key:
            _snake_case   :				List[Any]														=	name.split(lowerCAmelCase				)[0].split('.'				)[-2]
            _snake_case   :				Optional[Any]														=	mapped_key.replace('*'    ,							lowerCAmelCase				)
          if "weight_g" in name:
            _snake_case   :				Optional[Any]														=	'weight_g'
          elif "weight_v" in name:
            _snake_case   :				Dict														=	'weight_v'
          elif "bias" in name:
            _snake_case   :				Optional[Any]														=	'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _snake_case   :				List[str]														=	'weight'
          else:
            _snake_case   :				Optional[int]														=	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     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	str    ,							lowerCAmelCase:	str    ,							lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	Optional[Any]				)->		Union[str, Any]:
  _snake_case   :				Optional[Any]														=	full_name.split('conv_layers.'				)[-1]
  _snake_case   :				Dict														=	name.split('.'				)
  _snake_case   :				List[Any]														=	int(items[0]				)
  _snake_case   :				Optional[Any]														=	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."""
      )
      _snake_case   :				List[Any]														=	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."""
      )
      _snake_case   :				int														=	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."
      )
      _snake_case   :				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."""
      )
      _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     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Optional[int]    ,							lowerCAmelCase:	Union[str, Any]=None    ,							lowerCAmelCase:	str=None    ,							lowerCAmelCase:	Any=True				)->		int:
  if config_path is not None:
    _snake_case   :				Any														=	UniSpeechConfig.from_pretrained(lowerCAmelCase				)
  else:
    _snake_case   :				Union[str, Any]														=	UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      _snake_case   :				Any														=	Dictionary.load_from_json(lowerCAmelCase				)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _snake_case   :				Union[str, Any]														=	target_dict.pad_index
      _snake_case   :				str														=	target_dict.bos_index
      _snake_case   :				Dict														=	target_dict.eos_index
      _snake_case   :				Optional[Any]														=	len(target_dict.symbols				)
      _snake_case   :				Tuple														=	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				)
      _snake_case   :				Optional[Any]														=	target_dict.indices

      # fairseq has the <pad> and <s> switched
      _snake_case   :				Union[str, Any]														=	42
      _snake_case   :				List[str]														=	43
      with open(lowerCAmelCase    ,							'w'    ,							encoding='utf-8'				) as vocab_handle:
        json.dump(lowerCAmelCase    ,							lowerCAmelCase				)
      _snake_case   :				Optional[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    ,							)
      _snake_case   :				List[str]														=	True if config.feat_extract_norm == 'layer' else False
      _snake_case   :				Dict														=	WavaVecaFeatureExtractor(
          feature_size=1    ,							sampling_rate=1_60_00    ,							padding_value=0    ,							do_normalize=lowerCAmelCase    ,							return_attention_mask=lowerCAmelCase    ,							)
      _snake_case   :				Union[str, Any]														=	WavaVecaProcessor(feature_extractor=lowerCAmelCase    ,							tokenizer=lowerCAmelCase				)
      processor.save_pretrained(lowerCAmelCase				)

    _snake_case   :				Dict														=	UniSpeechForCTC(lowerCAmelCase				)
  else:
    _snake_case   :				Dict														=	UniSpeechForPreTraining(lowerCAmelCase				)

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

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

  recursively_load_weights(lowerCAmelCase    ,							lowerCAmelCase    ,							lowerCAmelCase				)

  hf_unispeech.save_pretrained(lowerCAmelCase				)


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











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Union[str, Any]      					=["""image_processor""", """tokenizer"""]
  a_							:       Optional[int]      					="""CLIPImageProcessor"""
  a_							:       Optional[Any]      					=("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				int														=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'      ,				UpperCamelCase      ,				)
      _snake_case   :				Optional[Any]														=	kwargs.pop('feature_extractor'							)

    _snake_case   :				Dict														=	image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'							)
    if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'							)

    super().__init__(UpperCamelCase      ,				UpperCamelCase							)







  def __call__(      self      :					Dict      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    if text is None and images is None:
      raise ValueError('You have to specify either text or images. Both cannot be none.'							)

    if text is not None:
      _snake_case   :				Optional[int]														=	self.tokenizer(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if images is not None:
      _snake_case   :				Optional[int]														=	self.image_processor(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if text is not None and images is not None:
      _snake_case   :				Optional[int]														=	image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**UpperCamelCase							)      ,				tensor_type=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Any      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    return self.tokenizer.batch_decode(*UpperCamelCase      ,				**UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Union[str, Any]      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    return self.tokenizer.decode(*UpperCamelCase      ,				**UpperCamelCase							)







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.tokenizer.model_input_names
    _snake_case   :				List[Any]														=	self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




