


"""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__		:     Optional[Any]    				=				logging.get_logger(__name__)

snake_case__		:     List[Any]    				=				{
    '''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						(						_snake_case				:			str ,   _snake_case				:			Optional[int] ,   _snake_case				:			Optional[Any] ,   _snake_case				:			List[str] ,   _snake_case				:			Any			):
      for attribute in key.split('''.'''			):
            lowerCAmelCase					:	Tuple       =   getattr(_snake_case ,   _snake_case			)

      if weight_type is not None:
            lowerCAmelCase					:	int       =   getattr(_snake_case ,   _snake_case			).shape
      else:
            lowerCAmelCase					:	List[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":
            lowerCAmelCase					:	Any       =   value
      elif weight_type == "weight_g":
            lowerCAmelCase					:	Optional[Any]       =   value
      elif weight_type == "weight_v":
            lowerCAmelCase					:	Any       =   value
      elif weight_type == "bias":
            lowerCAmelCase					:	Optional[int]       =   value
      else:
            lowerCAmelCase					:	str       =   value

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


def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			Dict ,   _snake_case				:			Tuple			):
      lowerCAmelCase					:	int       =   []
      lowerCAmelCase					:	Dict       =   fairseq_model.state_dict()

      lowerCAmelCase					:	List[str]       =   hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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


def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			str ,   _snake_case				:			List[Any] ,   _snake_case				:			List[Any] ,   _snake_case				:			Tuple			):
      lowerCAmelCase					:	str       =   full_name.split('''conv_layers.'''			)[-1]
      lowerCAmelCase					:	str       =   name.split('''.'''			)
      lowerCAmelCase					:	Tuple       =   int(items[0]			)
      lowerCAmelCase					:	Any       =   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.'''
                  )
                  lowerCAmelCase					:	str       =   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.'''
                  )
                  lowerCAmelCase					:	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."
                  )
                  lowerCAmelCase					:	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.'''
                  )
                  lowerCAmelCase					:	Optional[Any]       =   value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
      else:
            unused_weights.append(_snake_case			)


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			Optional[int]			):
      lowerCAmelCase					:	Tuple       =   SEWConfig()
      if is_finetuned:
            lowerCAmelCase					:	Optional[Any]       =   model.wav_encoder.wav_model.cfg
      else:
            lowerCAmelCase					:	Dict       =   model.cfg

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

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

      lowerCAmelCase					:	List[str]       =   '''Wav2Vec2FeatureExtractor'''
      lowerCAmelCase					:	Any       =   '''Wav2Vec2CTCTokenizer'''

      return config


@torch.no_grad()
def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			Dict ,   _snake_case				:			int=None ,   _snake_case				:			List[str]=None ,   _snake_case				:			List[Any]=True			):

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

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

      lowerCAmelCase					:	Union[str, Any]       =   True if config.feat_extract_norm == '''layer''' else False
      lowerCAmelCase					:	int       =   WavaVecaFeatureExtractor(
          feature_size=1 ,   sampling_rate=16000 ,   padding_value=0 ,   do_normalize=_snake_case ,   return_attention_mask=_snake_case ,   )

      if is_finetuned:
            if dict_path:
                  lowerCAmelCase					:	Dict       =   Dictionary.load(_snake_case			)

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

            lowerCAmelCase					:	List[str]       =   SEWForCTC(_snake_case			)
      else:
            lowerCAmelCase					:	List[str]       =   SEWModel(_snake_case			)
            feature_extractor.save_pretrained(_snake_case			)

      recursively_load_weights(_snake_case ,   _snake_case ,   _snake_case			)

      hf_model.save_pretrained(_snake_case			)


if __name__ == "__main__":
 snake_case__		:     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'''
 )
 snake_case__		:     Union[str, Any]    				=				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 __future__ import annotations



class 		snake_case_:






     def __init__(	self  :					int					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str				):
           lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   text, pattern
           lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   len(UpperCamelCase_				), len(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					List[Any]					,				UpperCamelCase_  :					str				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if char == self.pattern[i]:
                       return i
           return -1






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					int				):

           for i in range(self.patLen - 1					,				-1					,				-1				):
                 if self.pattern[i] != self.text[current_pos + i]:
                       return current_pos + i
           return -1






     def      lowerCamelCase__     (	self  :					Dict				):
           # searches pattern in text and returns index positions
           lowerCAmelCase					:	Union[str, Any]       =   []
           for i in range(self.textLen - self.patLen + 1				):
                 lowerCAmelCase					:	str       =   self.mismatch_in_text(UpperCamelCase_				)
                 if mismatch_index == -1:
                       positions.append(UpperCamelCase_				)
                 else:
                       lowerCAmelCase					:	Optional[Any]       =   self.match_in_pattern(self.text[mismatch_index]				)
                       lowerCAmelCase					:	int       =   (
                           mismatch_index - match_index
                       )  # shifting index lgtm [py/multiple-definition]
           return positions



snake_case__		:     str    				=				'''ABAABA'''
snake_case__		:     List[str]    				=				'''AB'''
snake_case__		:     Union[str, Any]    				=				BoyerMooreSearch(text, pattern)
snake_case__		:     Optional[Any]    				=				bms.bad_character_heuristic()

if len(positions) == 0:
 print('''No match found''')
else:
 print('''Pattern found in following positions: ''')
 print(positions)


