






"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_snake_case          =   {
    '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 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for attribute in key.split("""."""				):
    _a  :       Optional[int]             =	getattr(UpperCamelCase__     ,   UpperCamelCase__				)

  if weight_type is not None:
    _a  :       Optional[Any]             =	getattr(UpperCamelCase__     ,   UpperCamelCase__				).shape
  else:
    _a  :       str             =	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  :       Any             =	value
  elif weight_type == "weight_g":
    _a  :       List[str]             =	value
  elif weight_type == "weight_v":
    _a  :       Union[str, Any]             =	value
  elif weight_type == "bias":
    _a  :       Optional[Any]             =	value
  else:
    _a  :       int             =	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  :       Union[str, Any]             =	[]
  _a  :       List[str]             =	fairseq_model.state_dict()

  _a  :       Dict             =	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _a  :       Tuple             =	False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   hf_model.config.feat_extract_norm == """group"""     ,   )
      _a  :       List[Any]             =	True
    else:
      for key, mapped_key in MAPPING.items():
        _a  :       Union[str, Any]             =	"""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]:
          _a  :       str             =	True
          if "*" in mapped_key:
            _a  :       Optional[int]             =	name.split(UpperCamelCase__				)[0].split("""."""				)[-2]
            _a  :       Optional[Any]             =	mapped_key.replace("""*"""     ,   UpperCamelCase__				)
          if "weight_g" in name:
            _a  :       Tuple             =	"""weight_g"""
          elif "weight_v" in name:
            _a  :       List[Any]             =	"""weight_v"""
          elif "weight" in name:
            _a  :       Union[str, Any]             =	"""weight"""
          elif "bias" in name:
            _a  :       Tuple             =	"""bias"""
          else:
            _a  :       int             =	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  :       Optional[int]             =	full_name.split("""conv_layers."""				)[-1]
  _a  :       Dict             =	name.split("""."""				)
  _a  :       List[Any]             =	int(items[0]				)
  _a  :       Tuple             =	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  :       Union[str, 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."""
      )
      _a  :       Tuple             =	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  :       Dict             =	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  :       Optional[Any]             =	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  :       int             =	SEWConfig()
  if is_finetuned:
    _a  :       List[str]             =	model.wav_encoder.wav_model.cfg
  else:
    _a  :       Optional[int]             =	model.cfg

  _a  :       List[Any]             =	fs_config.conv_bias
  _a  :       str             =	eval(fs_config.conv_feature_layers				)
  _a  :       List[Any]             =	[x[0] for x in conv_layers]
  _a  :       int             =	[x[1] for x in conv_layers]
  _a  :       Optional[int]             =	[x[2] for x in conv_layers]
  _a  :       Any             =	"""gelu"""
  _a  :       Optional[Any]             =	"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  _a  :       int             =	0.0
  _a  :       List[str]             =	fs_config.activation_fn.name
  _a  :       Union[str, Any]             =	fs_config.encoder_embed_dim
  _a  :       Optional[int]             =	0.02
  _a  :       Optional[int]             =	fs_config.encoder_ffn_embed_dim
  _a  :       Optional[Any]             =	1e-5
  _a  :       Dict             =	fs_config.encoder_layerdrop
  _a  :       Optional[Any]             =	fs_config.encoder_attention_heads
  _a  :       List[str]             =	fs_config.conv_pos_groups
  _a  :       List[Any]             =	fs_config.conv_pos
  _a  :       str             =	len(UpperCamelCase__				)
  _a  :       Optional[Any]             =	fs_config.encoder_layers
  _a  :       List[str]             =	fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    _a  :       str             =	model.cfg
    _a  :       str             =	fs_config.final_dropout
    _a  :       Union[str, Any]             =	fs_config.layerdrop
  _a  :       str             =	fs_config.activation_dropout
  _a  :       Any             =	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  _a  :       Optional[int]             =	fs_config.attention_dropout
  _a  :       List[Any]             =	fs_config.dropout_input
  _a  :       int             =	fs_config.dropout
  _a  :       Any             =	fs_config.mask_channel_length
  _a  :       Tuple             =	fs_config.mask_channel_prob
  _a  :       List[str]             =	fs_config.mask_length
  _a  :       List[Any]             =	fs_config.mask_prob

  _a  :       Tuple             =	"""Wav2Vec2FeatureExtractor"""
  _a  :       Tuple             =	"""Wav2Vec2CTCTokenizer"""

  return config

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=True				):



  '''simple docstring'''

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

  if config_path is not None:
    _a  :       Union[str, Any]             =	SEWConfig.from_pretrained(UpperCamelCase__				)
  else:
    _a  :       Optional[Any]             =	convert_config(model[0]     ,   UpperCamelCase__				)
  _a  :       Any             =	model[0].eval()

  _a  :       List[str]             =	True if config.feat_extract_norm == """layer""" else False
  _a  :       List[str]             =	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  :       List[str]             =	Dictionary.load(UpperCamelCase__				)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _a  :       Dict             =	target_dict.pad_index
      _a  :       str             =	target_dict.bos_index
      _a  :       Union[str, Any]             =	target_dict.pad_index
      _a  :       Union[str, Any]             =	target_dict.bos_index
      _a  :       Union[str, Any]             =	target_dict.eos_index
      _a  :       List[Any]             =	len(target_dict.symbols				)
      _a  :       Tuple             =	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  :       Tuple             =	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  :       Any             =	WavaVecaProcessor(feature_extractor=UpperCamelCase__     ,   tokenizer=UpperCamelCase__				)
      processor.save_pretrained(UpperCamelCase__				)

    _a  :       Dict             =	SEWForCTC(UpperCamelCase__				)
  else:
    _a  :       Dict             =	SEWModel(UpperCamelCase__				)
    feature_extractor.save_pretrained(UpperCamelCase__				)

  recursively_load_weights(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  hf_model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
 _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(
     '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
 )
 _snake_case          =   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"""




import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list






@require_torchaudio
@require_sentencepiece
class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       List[str]:
   _a  :       Any             =	"""laion/clap-htsat-unfused"""
   _a  :       Union[str, Any]             =	tempfile.mkdtemp()


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						Any							)					->       Dict:
   return RobertaTokenizer.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							,						**UpperCAmelCase__		:						List[str]							)					->       int:
   return ClapFeatureExtractor.from_pretrained(self.checkpoint							,						**UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       Tuple:
   shutil.rmtree(self.tmpdirname							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_tokenizer()
   _a  :       Any             =	self.get_feature_extractor()

   _a  :       Optional[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   processor.save_pretrained(self.tmpdirname							)
   _a  :       List[str]             =	ClapProcessor.from_pretrained(self.tmpdirname							)

   self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer.get_vocab()							)
   self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase__							)

   self.assertEqual(processor.feature_extractor.to_json_string()							,						feature_extractor.to_json_string()							)
   self.assertIsInstance(processor.feature_extractor							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						Dict							)					->       Optional[int]:
   _a  :       Tuple             =	ClapProcessor(tokenizer=self.get_tokenizer()							,						feature_extractor=self.get_feature_extractor()							)
   processor.save_pretrained(self.tmpdirname							)

   _a  :       Dict             =	self.get_tokenizer(bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							)
   _a  :       Union[str, Any]             =	self.get_feature_extractor(do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   _a  :       Union[str, Any]             =	ClapProcessor.from_pretrained(
       self.tmpdirname							,						bos_token="""(BOS)"""							,						eos_token="""(EOS)"""							,						do_normalize=UpperCAmelCase__							,						padding_value=1.0							)

   self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab()							)
   self.assertIsInstance(processor.tokenizer							,						UpperCAmelCase__							)

   self.assertEqual(processor.feature_extractor.to_json_string()							,						feature_extractor_add_kwargs.to_json_string()							)
   self.assertIsInstance(processor.feature_extractor							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       Optional[int]             =	self.get_feature_extractor()
   _a  :       Tuple             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	floats_list((3, 1000)							)

   _a  :       List[Any]             =	feature_extractor(UpperCAmelCase__							,						return_tensors="""np"""							)
   _a  :       List[str]             =	processor(audios=UpperCAmelCase__							,						return_tensors="""np"""							)

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum()							,						input_processor[key].sum()							,						delta=1E-2							)


 def _lowercase	(     self		:						Tuple							)					->       Optional[int]:
   _a  :       List[str]             =	self.get_feature_extractor()
   _a  :       Any             =	self.get_tokenizer()

   _a  :       Any             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Optional[int]             =	"""This is a test string"""

   _a  :       Tuple             =	processor(text=UpperCAmelCase__							)

   _a  :       int             =	tokenizer(UpperCAmelCase__							)

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key]							,						encoded_processor[key]							)


 def _lowercase	(     self		:						List[Any]							)					->       Any:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       List[str]             =	self.get_tokenizer()

   _a  :       List[Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   _a  :       Any             =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   _a  :       Dict             =	processor.batch_decode(UpperCAmelCase__							)
   _a  :       Any             =	tokenizer.batch_decode(UpperCAmelCase__							)

   self.assertListEqual(UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						Dict							)					->       List[str]:
   _a  :       str             =	self.get_feature_extractor()
   _a  :       Optional[Any]             =	self.get_tokenizer()

   _a  :       Union[str, Any]             =	ClapProcessor(tokenizer=UpperCAmelCase__							,						feature_extractor=UpperCAmelCase__							)

   self.assertListEqual(
       processor.model_input_names[2:]							,						feature_extractor.model_input_names							,						msg="""`processor` and `feature_extractor` model input names do not match"""							,						)

