





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

SCREAMING_SNAKE_CASE_		     = {
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
SCREAMING_SNAKE_CASE_		     = [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		{}
      with open(_SCREAMING_SNAKE_CASE				,		"""r"""			) as file:
            for line_number, line in enumerate(_SCREAMING_SNAKE_CASE			):
                  SCREAMING_SNAKE_CASE	=		line.strip()
                  if line:
                        SCREAMING_SNAKE_CASE	=		line.split()
                        SCREAMING_SNAKE_CASE	=		line_number
                        SCREAMING_SNAKE_CASE	=		words[0]
                        SCREAMING_SNAKE_CASE	=		value
      return result



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Tuple:


      '''simple docstring'''


      for attribute in key.split("""."""			):
            SCREAMING_SNAKE_CASE	=		getattr(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)

      SCREAMING_SNAKE_CASE	=		None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_SCREAMING_SNAKE_CASE			):
                  SCREAMING_SNAKE_CASE	=		PARAM_MAPPING[full_name.split("""."""			)[-1]]
                  SCREAMING_SNAKE_CASE	=		"""param"""

      if weight_type is not None and weight_type != "param":
            SCREAMING_SNAKE_CASE	=		getattr(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			).shape
      elif weight_type is not None and weight_type == "param":
            SCREAMING_SNAKE_CASE	=		hf_pointer
            for attribute in hf_param_name.split("""."""			):
                  SCREAMING_SNAKE_CASE	=		getattr(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		shape_pointer.shape

            # let's reduce dimension
            SCREAMING_SNAKE_CASE	=		value[0]
      else:
            SCREAMING_SNAKE_CASE	=		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":
            SCREAMING_SNAKE_CASE	=		value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE	=		value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE	=		value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE	=		value
      elif weight_type == "param":
            for attribute in hf_param_name.split("""."""			):
                  SCREAMING_SNAKE_CASE	=		getattr(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		value
      else:
            SCREAMING_SNAKE_CASE	=		value

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



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_SCREAMING_SNAKE_CASE			):
                  SCREAMING_SNAKE_CASE	=		PARAM_MAPPING[full_name.split("""."""			)[-1]]
                  SCREAMING_SNAKE_CASE	=		"""param"""

      if weight_type is not None and weight_type != "param":
            SCREAMING_SNAKE_CASE	=		""".""".join([key, weight_type]			)
      elif weight_type is not None and weight_type == "param":
            SCREAMING_SNAKE_CASE	=		""".""".join([key, hf_param_name]			)
      else:
            SCREAMING_SNAKE_CASE	=		key

      SCREAMING_SNAKE_CASE	=		value if """lm_head""" in full_key else value[0]


SCREAMING_SNAKE_CASE_		     = {
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=None				,		_SCREAMING_SNAKE_CASE=None			)				->     Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		False
      for key, mapped_key in MAPPING.items():
            SCREAMING_SNAKE_CASE	=		"""wav2vec2.""" + 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	=		True
                  if "*" in mapped_key:
                        SCREAMING_SNAKE_CASE	=		name.split(_SCREAMING_SNAKE_CASE			)[0].split("""."""			)[-2]
                        SCREAMING_SNAKE_CASE	=		mapped_key.replace("""*"""				,		_SCREAMING_SNAKE_CASE			)
                  if "weight_g" in name:
                        SCREAMING_SNAKE_CASE	=		"""weight_g"""
                  elif "weight_v" in name:
                        SCREAMING_SNAKE_CASE	=		"""weight_v"""
                  elif "bias" in name:
                        SCREAMING_SNAKE_CASE	=		"""bias"""
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        SCREAMING_SNAKE_CASE	=		"""weight"""
                  else:
                        SCREAMING_SNAKE_CASE	=		None
                  if hf_dict is not None:
                        rename_dict(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
                  else:
                        set_recursively(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
                  return is_used
      return is_used



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		[]
      SCREAMING_SNAKE_CASE	=		fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE	=		hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE	=		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"""				,		)
                  SCREAMING_SNAKE_CASE	=		True
            else:
                  SCREAMING_SNAKE_CASE	=		load_wavaveca_layer(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
            if not is_used:
                  unused_weights.append(_SCREAMING_SNAKE_CASE			)

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



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     List[str]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		full_name.split("""conv_layers."""			)[-1]
      SCREAMING_SNAKE_CASE	=		name.split("""."""			)
      SCREAMING_SNAKE_CASE	=		int(items[0]			)
      SCREAMING_SNAKE_CASE	=		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."""			)
                  SCREAMING_SNAKE_CASE	=		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."""			)
                  SCREAMING_SNAKE_CASE	=		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."""			)
                  SCREAMING_SNAKE_CASE	=		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."""			)
                  SCREAMING_SNAKE_CASE	=		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      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE=None				,		_SCREAMING_SNAKE_CASE=None				,		_SCREAMING_SNAKE_CASE=True				,		_SCREAMING_SNAKE_CASE=False			)				->     Optional[Any]:


      '''simple docstring'''


      if config_path is not None:
            SCREAMING_SNAKE_CASE	=		WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
      else:
            SCREAMING_SNAKE_CASE	=		WavaVecaConfig()

      if is_seq_class:
            SCREAMING_SNAKE_CASE	=		read_txt_into_dict(_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		idalabel
            SCREAMING_SNAKE_CASE	=		WavaVecaForSequenceClassification(_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		WavaVecaFeatureExtractor(
                feature_size=1				,		sampling_rate=1_60_00				,		padding_value=0				,		do_normalize=_SCREAMING_SNAKE_CASE				,		return_attention_mask=_SCREAMING_SNAKE_CASE				,		)
            feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE			)

      elif is_finetuned:
            if dict_path:
                  SCREAMING_SNAKE_CASE	=		Dictionary.load(_SCREAMING_SNAKE_CASE			)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  SCREAMING_SNAKE_CASE	=		target_dict.pad_index
                  SCREAMING_SNAKE_CASE	=		target_dict.bos_index
                  SCREAMING_SNAKE_CASE	=		target_dict.eos_index
                  SCREAMING_SNAKE_CASE	=		len(target_dict.symbols			)
                  SCREAMING_SNAKE_CASE	=		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			)
                  SCREAMING_SNAKE_CASE	=		target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  SCREAMING_SNAKE_CASE	=		0
                  SCREAMING_SNAKE_CASE	=		1
                  with open(_SCREAMING_SNAKE_CASE				,		"""w"""				,		encoding="""utf-8"""			) as vocab_handle:
                        json.dump(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
                  SCREAMING_SNAKE_CASE	=		WavaVecaCTCTokenizer(
                      _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				,		)
                  SCREAMING_SNAKE_CASE	=		True if config.feat_extract_norm == """layer""" else False
                  SCREAMING_SNAKE_CASE	=		WavaVecaFeatureExtractor(
                      feature_size=1				,		sampling_rate=1_60_00				,		padding_value=0				,		do_normalize=_SCREAMING_SNAKE_CASE				,		return_attention_mask=_SCREAMING_SNAKE_CASE				,		)
                  SCREAMING_SNAKE_CASE	=		WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE				,		tokenizer=_SCREAMING_SNAKE_CASE			)
                  processor.save_pretrained(_SCREAMING_SNAKE_CASE			)

            SCREAMING_SNAKE_CASE	=		WavaVecaForCTC(_SCREAMING_SNAKE_CASE			)
      else:
            SCREAMING_SNAKE_CASE	=		WavaVecaForPreTraining(_SCREAMING_SNAKE_CASE			)

      if is_finetuned or is_seq_class:
            SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]				,		arg_overrides={"""data""": """/""".join(dict_path.split("""/"""			)[:-1]			)}			)
      else:
            SCREAMING_SNAKE_CASE	=		argparse.Namespace(task="""audio_pretraining"""			)
            SCREAMING_SNAKE_CASE	=		fairseq.tasks.setup_task(_SCREAMING_SNAKE_CASE			)

            SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,		task=_SCREAMING_SNAKE_CASE			)

      SCREAMING_SNAKE_CASE	=		model[0].eval()

      recursively_load_weights(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		not is_finetuned			)

      hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = 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"""
      )
      parser.add_argument(
          """--is_seq_class""",
          action="""store_true""",
          help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
      )
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      SCREAMING_SNAKE_CASE_		     = not args.not_finetuned and not args.is_seq_class
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.dict_path,
          is_finetuned,
          args.is_seq_class,
      )









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(a - b			) for a, b in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     None:


      '''simple docstring'''


      if point:
            if isinstance(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			):
                  for item in point:
                        if not isinstance(_SCREAMING_SNAKE_CASE				,		(int, float)			):
                              SCREAMING_SNAKE_CASE	=		(
                                  """Expected a list of numbers as input, found """
                                  F"""{type(_SCREAMING_SNAKE_CASE			).__name__}"""
                              )
                              raise TypeError(_SCREAMING_SNAKE_CASE			)
            else:
                  SCREAMING_SNAKE_CASE	=		F"""Expected a list of numbers as input, found {type(_SCREAMING_SNAKE_CASE			).__name__}"""
                  raise TypeError(_SCREAMING_SNAKE_CASE			)
      else:
            raise ValueError("""Missing an input"""			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(x - y			) for x, y in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


