

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()
__A      		=     logging.get_logger(__name__)

__A      		=     {
    '''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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  for attribute in key.split('''.'''					):
    _snake_case						=							getattr(_UpperCamelCase			,	_UpperCamelCase					)

  if weight_type is not None:
    _snake_case						=							getattr(_UpperCamelCase			,	_UpperCamelCase					).shape
  else:
    _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":
    _snake_case						=							value
  elif weight_type == "weight_g":
    _snake_case						=							value
  elif weight_type == "weight_v":
    _snake_case						=							value
  elif weight_type == "bias":
    _snake_case						=							value
  else:
    _snake_case						=							value

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



  """simple docstring"""






  _snake_case						=							[]
  _snake_case						=							fairseq_model.state_dict()

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

  for name, value in fairseq_dict.items():
    _snake_case						=							False
    if "conv_layers" in name:
      load_conv_layer(
          _UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	hf_model.config.feat_extract_norm == '''group'''			,	)
      _snake_case						=							True
    else:
      for key, mapped_key in MAPPING.items():
        _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]:
          _snake_case						=							True
          if "*" in mapped_key:
            _snake_case						=							name.split(_UpperCamelCase					)[0].split('''.'''					)[-2]
            _snake_case						=							mapped_key.replace('''*'''			,	_UpperCamelCase					)
          if "weight_g" in name:
            _snake_case						=							'''weight_g'''
          elif "weight_v" in name:
            _snake_case						=							'''weight_v'''
          elif "weight" in name:
            _snake_case						=							'''weight'''
          elif "bias" in name:
            _snake_case						=							'''bias'''
          else:
            _snake_case						=							None
          set_recursively(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)
        continue
    if not is_used:
      unused_weights.append(_UpperCamelCase					)

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



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							full_name.split('''conv_layers.'''					)[-1]
  _snake_case						=							name.split('''.'''					)
  _snake_case						=							int(items[0]					)
  _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."""
      )
      _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."""
      )
      _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."
      )
      _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."""
      )
      _snake_case						=							value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
  else:
    unused_weights.append(_UpperCamelCase					)



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Union[str, Any]:



  """simple docstring"""






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

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

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

  _snake_case						=							'''Wav2Vec2FeatureExtractor'''
  _snake_case						=							'''Wav2Vec2CTCTokenizer'''

  return config



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=None			,	_UpperCamelCase=None			,	_UpperCamelCase=True					)							->     List[Any]:



  """simple docstring"""






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

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

  _snake_case						=							True if config.feat_extract_norm == '''layer''' else False
  _snake_case						=							WavaVecaFeatureExtractor(
      feature_size=1			,	sampling_rate=16_000			,	padding_value=0			,	do_normalize=_UpperCamelCase			,	return_attention_mask=_UpperCamelCase			,	)

  if is_finetuned:
    if dict_path:
      _snake_case						=							Dictionary.load(_UpperCamelCase					)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _snake_case						=							target_dict.pad_index
      _snake_case						=							target_dict.bos_index
      _snake_case						=							target_dict.pad_index
      _snake_case						=							target_dict.bos_index
      _snake_case						=							target_dict.eos_index
      _snake_case						=							len(target_dict.symbols					)
      _snake_case						=							os.path.join(_UpperCamelCase			,	'''vocab.json'''					)
      if not os.path.isdir(_UpperCamelCase					):
        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_UpperCamelCase					)					)
        return
      os.makedirs(_UpperCamelCase			,	exist_ok=_UpperCamelCase					)
      with open(_UpperCamelCase			,	'''w'''			,	encoding='''utf-8'''					) as vocab_handle:
        json.dump(target_dict.indices			,	_UpperCamelCase					)
      _snake_case						=							WavaVecaCTCTokenizer(
          _UpperCamelCase			,	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=_UpperCamelCase			,	)
      _snake_case						=							WavaVecaProcessor(feature_extractor=_UpperCamelCase			,	tokenizer=_UpperCamelCase					)
      processor.save_pretrained(_UpperCamelCase					)

    _snake_case						=							SEWForCTC(_UpperCamelCase					)
  else:
    _snake_case						=							SEWModel(_UpperCamelCase					)
    feature_extractor.save_pretrained(_UpperCamelCase					)

  recursively_load_weights(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

  hf_model.save_pretrained(_UpperCamelCase					)


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(
     '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 __A      		=     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 List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__A      		=     logging.get_logger(__name__)

__A      		=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A      		=     {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__A      		=     {'''allegro/herbert-base-cased''': 5_14}
__A      		=     {}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Any										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_      :		Dict										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Optional[Any]										=    HerbertTokenizer



      def __init__(      self				:    Tuple      , A__				:    str=None      , A__				:    Optional[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[int]="<s>"      , A__				:    Optional[int]="<unk>"      , A__				:    str="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Dict="</s>"      , **A__				:    Optional[int]      , )   ->					Optional[int]:
        super().__init__(
            A__      , A__      , tokenizer_file=A__      , cls_token=A__      , unk_token=A__      , pad_token=A__      , mask_token=A__      , sep_token=A__      , **A__      , )



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[self.sep_token_id]
        if token_ids_a is None:
          return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__      , token_ids_a=A__      , already_has_special_tokens=A__		)

        if token_ids_a is None:
          return [1] + ([0] * len(A__		)) + [1]
        return [1] + ([0] * len(A__		)) + [1] + ([0] * len(A__		)) + [1]



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _snake_case						=							[self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							self._tokenizer.model.save(A__      , name=A__		)
        return tuple(A__		)

