






"""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()
_UpperCamelCase :    Union[str, Any]      =			logging.get_logger(__name__)

_UpperCamelCase :    Tuple      =			{
    """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 					a			(  UpperCamelCase_				:					Tuple  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					int		)	->		Any:
 for attribute in key.split('.'		):
  __A						:							List[Any]      =						getattr(UpperCamelCase_  ,						UpperCamelCase_		)

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

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




def 					a			(  UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					Any  ,						UpperCamelCase_				:					List[str]		)	->		Any:
 __A						:							str      =						[]
 __A						:							int      =						fairseq_model.state_dict()

 __A						:							Union[str, Any]      =						hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __A						:							Union[str, Any]      =						False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						hf_model.config.feat_extract_norm == 'group'  ,						)
   __A						:							Dict      =						True
  else:
   for key, mapped_key in MAPPING.items():
    __A						:							Tuple      =						'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						:							Optional[Any]      =						True
     if "*" in mapped_key:
      __A						:							str      =						name.split(UpperCamelCase_		)[0].split('.'		)[-2]
      if not layer_index.isnumeric():
       continue
      __A						:							Any      =						mapped_key.replace('*'  ,						UpperCamelCase_		)
     if "weight_g" in name:
      __A						:							List[str]      =						'weight_g'
     elif "weight_v" in name:
      __A						:							int      =						'weight_v'
     elif "weight" in name:
      __A						:							int      =						'weight'
     elif "bias" in name:
      __A						:							List[str]      =						'bias'
     else:
      __A						:							Tuple      =						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 					a			(  UpperCamelCase_				:					str  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					Union[str, Any]  ,						UpperCamelCase_				:					Optional[int]		)	->		Dict:
 __A						:							Tuple      =						full_name.split('conv_layers.'		)[-1]
 __A						:							List[Any]      =						name.split('.'		)
 __A						:							Optional[int]      =						int(items[0]		)
 __A						:							List[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."
   )
   __A						:							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."
   )
   __A						:							Optional[Any]      =						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						:							Tuple      =						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						:							int      =						value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
 else:
  unused_weights.append(UpperCamelCase_		)




def 					a			(  UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					int		)	->		Any:
 __A						:							List[Any]      =						SEWDConfig()
 if is_finetuned:
  __A						:							Optional[Any]      =						model.wav_encoder.wav_model.cfg
 else:
  __A						:							Any      =						model.cfg

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

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

 __A						:							int      =						'Wav2Vec2FeatureExtractor'
 __A						:							Optional[Any]      =						'Wav2Vec2CTCTokenizer'

 return config




@torch.no_grad()
def 					a			(  UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					Optional[Any]=None  ,						UpperCamelCase_				:					Optional[Any]=None  ,						UpperCamelCase_				:					List[Any]=True		)	->		List[Any]:

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

 if config_path is not None:
  __A						:							Any      =						SEWDConfig.from_pretrained(UpperCamelCase_		)
 else:
  __A						:							Optional[int]      =						convert_config(model[0]  ,						UpperCamelCase_		)
 __A						:							List[Any]      =						model[0].eval()

 __A						:							Any      =						True if config.feat_extract_norm == 'layer' else False
 __A						:							Optional[int]      =						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						:							Optional[int]      =						Dictionary.load(UpperCamelCase_		)

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

  __A						:							List[str]      =						SEWDForCTC(UpperCamelCase_		)
 else:
  __A						:							Dict      =						SEWDModel(UpperCamelCase_		)
  feature_extractor.save_pretrained(UpperCamelCase_		)

 recursively_load_weights(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)

 hf_model.save_pretrained(UpperCamelCase_		)


if __name__ == "__main__":
   _UpperCamelCase :    Optional[Any]      =			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"""
   )
   _UpperCamelCase :    List[str]      =			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"""


def 					a			(  UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int		)	->		int:
 return len_board * len_board_column * row + column




def 					a			(  UpperCamelCase_				:					list[list[str]]  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					set[int]  ,						)	->		bool:

 if board[row][column] != word[word_index]:
  return False

 if word_index == len(UpperCamelCase_		) - 1:
  return True

 __A						:							List[str]      =						[(0, 1), (0, -1), (-1, 0), (1, 0)]
 __A						:							Any      =						len(UpperCamelCase_		)
 __A						:							Union[str, Any]      =						len(board[0]		)
 for direction in traverts_directions:
  __A						:							Any      =						row + direction[0]
  __A						:							List[Any]      =						column + direction[1]
  if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
   continue

  __A						:							Any      =						get_point_key(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)
  if key in visited_points_set:
   continue

  visited_points_set.add(UpperCamelCase_		)
  if exits_word(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						word_index + 1  ,						UpperCamelCase_		):
   return True

  visited_points_set.remove(UpperCamelCase_		)

 return False




def 					a			(  UpperCamelCase_				:					list[list[str]]  ,						UpperCamelCase_				:					str		)	->		bool:
 __A						:							Optional[Any]      =						(
     'The board should be a non empty matrix of single chars strings.'
 )

 __A						:							Dict      =						len(UpperCamelCase_		)
 if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
  raise ValueError(UpperCamelCase_		)

 for row in board:
  if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
   raise ValueError(UpperCamelCase_		)

  for item in row:
   if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) != 1:
    raise ValueError(UpperCamelCase_		)

    # Validate word
 if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
  raise ValueError(
      'The word parameter should be a string of length greater than 0.'		)

 __A						:							List[Any]      =						len(board[0]		)
 for i in range(UpperCamelCase_		):
  for j in range(UpperCamelCase_		):
   if exits_word(
       UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						0  ,						{get_point_key(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)}		):
    return True

 return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()





