import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCamelCase			: Dict							   =		logging.get_logger(__name__)

__lowerCamelCase			: Optional[int]							   =		{
    '''post_extract_proj''': '''feature_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.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Dict ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       str	)							->					str:







  """simple docstring"""





  for attribute in key.split("""."""	):
    SCREAMING_SNAKE_CASE__										=      getattr(_A ,     _A	)

  if weight_type is not None:
    SCREAMING_SNAKE_CASE__										=      getattr(_A ,     _A	).shape
  else:
    SCREAMING_SNAKE_CASE__										=      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__										=      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
  else:
    SCREAMING_SNAKE_CASE__										=      value

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       List[str]	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      []
  SCREAMING_SNAKE_CASE__										=      fairseq_model.state_dict()

  SCREAMING_SNAKE_CASE__										=      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    SCREAMING_SNAKE_CASE__										=      False
    if "conv_layers" in name:
      load_conv_layer(
          _A ,     _A ,     _A ,     _A ,     hf_model.config.feat_extract_norm == """group""" ,     )
      SCREAMING_SNAKE_CASE__										=      True
    else:
      for key, mapped_key in MAPPING.items():
        SCREAMING_SNAKE_CASE__										=      """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") 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(_A	)[0].split("""."""	)[-2]
            SCREAMING_SNAKE_CASE__										=      mapped_key.replace("""*""" ,     _A	)
          if "weight_g" in name:
            SCREAMING_SNAKE_CASE__										=      """weight_g"""
          elif "weight_v" in name:
            SCREAMING_SNAKE_CASE__										=      """weight_v"""
          elif "weight" in name:
            SCREAMING_SNAKE_CASE__										=      """weight"""
          elif "bias" in name:
            SCREAMING_SNAKE_CASE__										=      """bias"""
          else:
            SCREAMING_SNAKE_CASE__										=      None
          set_recursively(_A ,     _A ,     _A ,     _A ,     _A	)
        continue
    if not is_used:
      unused_weights.append(_A	)

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Optional[Any]	)							->					Dict:







  """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:
      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__										=      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__										=      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__										=      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__										=      value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
  else:
    unused_weights.append(_A	)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       Any	)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      SEWConfig()
  if is_finetuned:
    SCREAMING_SNAKE_CASE__										=      model.wav_encoder.wav_model.cfg
  else:
    SCREAMING_SNAKE_CASE__										=      model.cfg

  SCREAMING_SNAKE_CASE__										=      fs_config.conv_bias
  SCREAMING_SNAKE_CASE__										=      eval(fs_config.conv_feature_layers	)
  SCREAMING_SNAKE_CASE__										=      [x[0] for x in conv_layers]
  SCREAMING_SNAKE_CASE__										=      [x[1] for x in conv_layers]
  SCREAMING_SNAKE_CASE__										=      [x[2] for x in conv_layers]
  SCREAMING_SNAKE_CASE__										=      """gelu"""
  SCREAMING_SNAKE_CASE__										=      """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  SCREAMING_SNAKE_CASE__										=      0.0
  SCREAMING_SNAKE_CASE__										=      fs_config.activation_fn.name
  SCREAMING_SNAKE_CASE__										=      fs_config.encoder_embed_dim
  SCREAMING_SNAKE_CASE__										=      0.02
  SCREAMING_SNAKE_CASE__										=      fs_config.encoder_ffn_embed_dim
  SCREAMING_SNAKE_CASE__										=      1E-5
  SCREAMING_SNAKE_CASE__										=      fs_config.encoder_layerdrop
  SCREAMING_SNAKE_CASE__										=      fs_config.encoder_attention_heads
  SCREAMING_SNAKE_CASE__										=      fs_config.conv_pos_groups
  SCREAMING_SNAKE_CASE__										=      fs_config.conv_pos
  SCREAMING_SNAKE_CASE__										=      len(_A	)
  SCREAMING_SNAKE_CASE__										=      fs_config.encoder_layers
  SCREAMING_SNAKE_CASE__										=      fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    SCREAMING_SNAKE_CASE__										=      model.cfg
    SCREAMING_SNAKE_CASE__										=      fs_config.final_dropout
    SCREAMING_SNAKE_CASE__										=      fs_config.layerdrop
  SCREAMING_SNAKE_CASE__										=      fs_config.activation_dropout
  SCREAMING_SNAKE_CASE__										=      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  SCREAMING_SNAKE_CASE__										=      fs_config.attention_dropout
  SCREAMING_SNAKE_CASE__										=      fs_config.dropout_input
  SCREAMING_SNAKE_CASE__										=      fs_config.dropout
  SCREAMING_SNAKE_CASE__										=      fs_config.mask_channel_length
  SCREAMING_SNAKE_CASE__										=      fs_config.mask_channel_prob
  SCREAMING_SNAKE_CASE__										=      fs_config.mask_length
  SCREAMING_SNAKE_CASE__										=      fs_config.mask_prob

  SCREAMING_SNAKE_CASE__										=      """Wav2Vec2FeatureExtractor"""
  SCREAMING_SNAKE_CASE__										=      """Wav2Vec2CTCTokenizer"""

  return config



@torch.no_grad()
def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       List[str]=None ,     __UpperCamelCase    :       Tuple=None ,     __UpperCamelCase    :       Optional[Any]=True	)							->					Dict:







  """simple docstring"""





  if is_finetuned:
    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__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

  if config_path is not None:
    SCREAMING_SNAKE_CASE__										=      SEWConfig.from_pretrained(_A	)
  else:
    SCREAMING_SNAKE_CASE__										=      convert_config(model[0] ,     _A	)
  SCREAMING_SNAKE_CASE__										=      model[0].eval()

  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=_A ,     return_attention_mask=_A ,     )

  if is_finetuned:
    if dict_path:
      SCREAMING_SNAKE_CASE__										=      Dictionary.load(_A	)

      # 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.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(_A ,     """vocab.json"""	)
      if not os.path.isdir(_A	):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_A	)	)
        return
      os.makedirs(_A ,     exist_ok=_A	)
      with open(_A ,     """w""" ,     encoding="""utf-8"""	) as vocab_handle:
        json.dump(target_dict.indices ,     _A	)
      SCREAMING_SNAKE_CASE__										=      WavaVecaCTCTokenizer(
          _A ,     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=_A ,     )
      SCREAMING_SNAKE_CASE__										=      WavaVecaProcessor(feature_extractor=_A ,     tokenizer=_A	)
      processor.save_pretrained(_A	)

    SCREAMING_SNAKE_CASE__										=      SEWForCTC(_A	)
  else:
    SCREAMING_SNAKE_CASE__										=      SEWModel(_A	)
    feature_extractor.save_pretrained(_A	)

  recursively_load_weights(_A ,     _A ,     _A	)

  hf_model.save_pretrained(_A	)


if __name__ == "__main__":
   __lowerCamelCase			: Tuple							   =		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(
       '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
   )
   __lowerCamelCase			: Any							   =		parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )






from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A     =			logging.get_logger(__name__)


_A     =			{
    '''nielsr/canine-s''': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
_A     =			1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_A     =			0

_A     =			0xe0_00
_A     =			0xe0_01
_A     =			0xe0_02
_A     =			0xe0_03
_A     =			0xe0_04

# Maps special codepoints to human-readable names.
_A     =			{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_A     =			{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



		def __init__(     self,	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=False,	UpperCamelCase__=2048,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else bos_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else eos_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else sep_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else cls_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else mask_token

								super().__init__(
								    bos_token=UpperCamelCase__,	eos_token=UpperCamelCase__,	sep_token=UpperCamelCase__,	cls_token=UpperCamelCase__,	pad_token=UpperCamelCase__,	mask_token=UpperCamelCase__,	add_prefix_space=UpperCamelCase__,	model_max_length=UpperCamelCase__,	**UpperCamelCase__,	)

								# Creates a mapping for looking up the IDs of special symbols.
								lowerCAmelCase_      			=  {}
								for codepoint, name in SPECIAL_CODEPOINTS.items():
														lowerCAmelCase_      			=  codepoint

								# Creates a mapping for looking up the string forms of special symbol IDs.
								lowerCAmelCase_      			=  {
								    codepoint: name for name, codepoint in self._special_codepoints.items()
								}

								lowerCAmelCase_      			=  UNICODE_VOCAB_SIZE
								lowerCAmelCase_      			=  len(self._special_codepoints   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._unicode_vocab_size



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return list(UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								try:
														return ord(UpperCamelCase__   )
								except TypeError:
														raise ValueError(f"invalid token: '{token}'"   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								try:
														if index in SPECIAL_CODEPOINTS:
																				return SPECIAL_CODEPOINTS[index]
														return chr(UpperCamelCase__   )
								except TypeError:
														raise ValueError(f"invalid id: {index}"   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return "".join(UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  [self.sep_token_id]
								lowerCAmelCase_      			=  [self.cls_token_id]

								lowerCAmelCase_      			=  cls + token_ids_a + sep
								if token_ids_a is not None:
														result += token_ids_a + sep
								return result



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None,	UpperCamelCase__ = False   ):







								"""simple docstring"""



								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCamelCase__,	token_ids_a=UpperCamelCase__,	already_has_special_tokens=UpperCamelCase__   )

								lowerCAmelCase_      			=  [1] + ([0] * len(UpperCamelCase__   )) + [1]
								if token_ids_a is not None:
														result += ([0] * len(UpperCamelCase__   )) + [1]
								return result



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  [self.sep_token_id]
								lowerCAmelCase_      			=  [self.cls_token_id]

								lowerCAmelCase_      			=  len(cls + token_ids_a + sep   ) * [0]
								if token_ids_a is not None:
														result += len(token_ids_a + sep   ) * [1]
								return result







		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								return ()




