
'''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()
a__		:   Any            =       logging.get_logger(__name__)

a__		:   List[Any]            =       {
    "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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			List[str]:







      """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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			List[Any]:







      """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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase	)->			Dict:







      """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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase	)->			List[Any]:







      """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        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  UpperCAmelCase=True	)->			int:







      """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__":
 a__		:   Optional[int]            =       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__		:   Optional[int]            =       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 .stack import Stack





def        snake_case							(		UpperCAmelCase	)->			bool:







      """simple docstring"""
      __A       	=			Stack()
      __A       	=			{'(': ')', '[': ']', '{': '}'}
      for bracket in parentheses:
            if bracket in bracket_pairs:
                  stack.push(UpperCAmelCase	)
            elif bracket in (")", "]", "}"):
                  if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                        return False
      return stack.is_empty()


if __name__ == "__main__":
 from doctest import testmod

 testmod()

 a__		:   int            =       ["((()))", "((())", "(()))"]
 print("Balanced parentheses demonstration:\n")
 for example in examples:
  a__		:   int            =       "" if balanced_parentheses(example) else "not "
  print(f'''{example} is {not_str}balanced''')






