






"""simple docstring"""


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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowercase_    					=					{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    '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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







  for attribute in key.split('''.'''			):
    __A						      =			getattr(__UpperCamelCase		,	__UpperCamelCase			)

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

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



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







  __A						      =			[]
  __A						      =			fairseq_model.state_dict()

  __A						      =			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __A						      =			False
    if "conv_layers" in name:
      load_conv_layer(
          __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	hf_model.config.feat_extract_norm == '''group'''		,	)
      __A						      =			True
    else:
      for key, mapped_key in MAPPING.items():
        __A						      =			'''sew_d.''' + 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]:
          __A						      =			True
          if "*" in mapped_key:
            __A						      =			name.split(__UpperCamelCase			)[0].split('''.'''			)[-2]
            if not layer_index.isnumeric():
              continue
            __A						      =			mapped_key.replace('''*'''		,	__UpperCamelCase			)
          if "weight_g" in name:
            __A						      =			'''weight_g'''
          elif "weight_v" in name:
            __A						      =			'''weight_v'''
          elif "weight" in name:
            __A						      =			'''weight'''
          elif "bias" in name:
            __A						      =			'''bias'''
          else:
            __A						      =			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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







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



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







  __A						      =			SEWDConfig()
  if is_finetuned:
    __A						      =			model.wav_encoder.wav_model.cfg
  else:
    __A						      =			model.cfg

  __A						      =			fs_config.conv_bias
  __A						      =			eval(fs_config.conv_feature_layers			)
  __A						      =			[x[0] for x in conv_layers]
  __A						      =			[x[1] for x in conv_layers]
  __A						      =			[x[2] for x in conv_layers]
  __A						      =			'''gelu'''
  __A						      =			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
  __A						      =			0.0
  __A						      =			fs_config.activation_fn.name
  __A						      =			fs_config.encoder_embed_dim
  __A						      =			0.02
  __A						      =			fs_config.encoder_ffn_embed_dim
  __A						      =			1e-5
  __A						      =			fs_config.encoder_layerdrop
  __A						      =			fs_config.encoder_attention_heads
  __A						      =			fs_config.conv_pos_groups
  __A						      =			fs_config.conv_pos
  __A						      =			len(__UpperCamelCase			)
  __A						      =			fs_config.encoder_layers
  __A						      =			fs_config.squeeze_factor
  # DeBERTa-specific parameters:
  __A						      =			fs_config.max_position_embeddings
  __A						      =			fs_config.position_buckets
  __A						      =			fs_config.share_att_key
  __A						      =			fs_config.relative_attention
  __A						      =			fs_config.position_biased_input
  __A						      =			tuple(fs_config.pos_att_type.split('''|'''			)			)
  __A						      =			fs_config.norm_rel_ebd

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

  __A						      =			'''Wav2Vec2FeatureExtractor'''
  __A						      =			'''Wav2Vec2CTCTokenizer'''

  return config



@torch.no_grad()
def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase=None		,	__UpperCamelCase=None		,	__UpperCamelCase=True			):




  """simple docstring"""







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

  if config_path is not None:
    __A						      =			SEWDConfig.from_pretrained(__UpperCamelCase			)
  else:
    __A						      =			convert_config(model[0]		,	__UpperCamelCase			)
  __A						      =			model[0].eval()

  __A						      =			True if config.feat_extract_norm == '''layer''' else False
  __A						      =			WavaVecaFeatureExtractor(
      feature_size=1		,	sampling_rate=1_6_0_0_0		,	padding_value=0		,	do_normalize=__UpperCamelCase		,	return_attention_mask=__UpperCamelCase		,	)

  if is_finetuned:
    if dict_path:
      __A						      =			Dictionary.load(__UpperCamelCase			)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      __A						      =			target_dict.pad_index
      __A						      =			target_dict.bos_index
      __A						      =			target_dict.pad_index
      __A						      =			target_dict.bos_index
      __A						      =			target_dict.eos_index
      __A						      =			len(target_dict.symbols			)
      __A						      =			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			)
      __A						      =			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		,	)
      __A						      =			WavaVecaProcessor(feature_extractor=__UpperCamelCase		,	tokenizer=__UpperCamelCase			)
      processor.save_pretrained(__UpperCamelCase			)

    __A						      =			SEWDForCTC(__UpperCamelCase			)
  else:
    __A						      =			SEWDModel(__UpperCamelCase			)
    feature_extractor.save_pretrained(__UpperCamelCase			)

  recursively_load_weights(__UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			)

  hf_model.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
       lowercase_    					=					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'
       )
       lowercase_    					=					parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )












"""simple docstring"""


from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if is_torch_available():
    import torch

    if isinstance(__UpperCamelCase		,	torch.Tensor			):
      return True

  return isinstance(__UpperCamelCase		,	np.ndarray			)





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     __A						      =			fields(self  )

     # Safety and consistency checks
     if not len(_lowerCamelCase  ):
       raise ValueError(f'{self.__class__.__name__} has no fields.'  )

     __A						      =			getattr(self,	class_fields[0].name  )
     __A						      =			all(getattr(self,	field.name  ) is None for field in class_fields[1:]  )

     if other_fields_are_none and isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       for key, value in first_field.items():
         __A						      =			value
     else:
       for field in class_fields:
         __A						      =			getattr(self,	field.name  )
         if v is not None:
           __A						      =			v


   def __delitem__(     self			:					Dict,	*_lowerCamelCase			:					int,	**_lowerCamelCase			:					Any  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	*_lowerCamelCase			:					List[str],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	*_lowerCamelCase			:					Optional[Any],	**_lowerCamelCase			:					str  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``pop`` on a {self.__class__.__name__} instance.'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	*_lowerCamelCase			:					Any,	**_lowerCamelCase			:					Optional[int]  ):

     '''simple docstring'''
     raise Exception(f'You cannot use ``update`` on a {self.__class__.__name__} instance.'  )


   def __getitem__(     self			:					int,	_lowerCamelCase			:					List[Any]  ):

     '''simple docstring'''
     if isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       __A						      =			dict(self.items()  )
       return inner_dict[k]
     else:
       return self.to_tuple()[k]


   def __setattr__(     self			:					Optional[int],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     if name in self.keys() and value is not None:
       # Don't call self.__setitem__ to avoid recursion errors
       super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )


   def __setitem__(     self			:					List[str],	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     # Will raise a KeyException if needed
     super().__setitem__(_lowerCamelCase,	_lowerCamelCase  )
     # Don't call self.__setattr__ to avoid recursion errors
     super().__setattr__(_lowerCamelCase,	_lowerCamelCase  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     return tuple(self[k] for k in self.keys()  )




