



"""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()
_UpperCAmelCase     =			logging.get_logger(__name__)

_UpperCAmelCase     =			{
    """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      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase ):
   for attribute in key.split(""".""" ):
      SCREAMING_SNAKE_CASE_: int     			=getattr(lowercase							,			lowercase )

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

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





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=[]
   SCREAMING_SNAKE_CASE_: Any     			=fairseq_model.state_dict()

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

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

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





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





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=SEWConfig()
   if is_finetuned:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model.wav_encoder.wav_model.cfg
   else:
      SCREAMING_SNAKE_CASE_: Tuple     			=model.cfg

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

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

   SCREAMING_SNAKE_CASE_: List[str]     			="""Wav2Vec2FeatureExtractor"""
   SCREAMING_SNAKE_CASE_: Tuple     			="""Wav2Vec2CTCTokenizer"""

   return config





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			lowercase=True ):

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

   if config_path is not None:
      SCREAMING_SNAKE_CASE_: int     			=SEWConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Dict     			=convert_config(model[0]							,			lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=model[0].eval()

   SCREAMING_SNAKE_CASE_: Optional[int]     			=True if config.feat_extract_norm == """layer""" else False
   SCREAMING_SNAKE_CASE_: Optional[int]     			=WavaVecaFeatureExtractor(
       feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=lowercase							,			return_attention_mask=lowercase							,			)

   if is_finetuned:
      if dict_path:
         SCREAMING_SNAKE_CASE_: int     			=Dictionary.load(lowercase )

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

      SCREAMING_SNAKE_CASE_: Optional[int]     			=SEWForCTC(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: int     			=SEWModel(lowercase )
      feature_extractor.save_pretrained(lowercase )

   recursively_load_weights(lowercase							,			lowercase							,			lowercase )

   hf_model.save_pretrained(lowercase )


if __name__ == "__main__":
   _UpperCAmelCase     =			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     =			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 string





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=""""""
   for i in sequence:
      SCREAMING_SNAKE_CASE_: List[Any]     			=ord(lowercase )
      if 65 <= extract <= 90:
         output += chr(155 - extract )
      elif 97 <= extract <= 122:
         output += chr(219 - extract )
      else:
         output += i
   return output





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=string.ascii_letters
   SCREAMING_SNAKE_CASE_: Tuple     			=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
   return "".join(
       letters_reversed[letters.index(lowercase )] if c in letters else c for c in sequence )





def      __magic_name__				(							):
   from timeit import timeit

   print("""Running performance benchmarks...""" )
   SCREAMING_SNAKE_CASE_: int     			="""from string import printable ; from __main__ import atbash, atbash_slow"""
   print(f'''> atbash_slow(): {timeit("atbash_slow(printable)"							,			setup=lowercase )} seconds''' )
   print(f'''>      atbash(): {timeit("atbash(printable)"							,			setup=lowercase )} seconds''' )


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f"""{example} encrypted in atbash: {atbash(example)}""")
   benchmark()



