



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

_lowercase     							=						{
    '''post_extract_proj''': '''feature_projection.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.layer_norm''': '''encoder.layer_norm''',
    '''adapter_layer''': '''encoder.layers.*.adapter_layer''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
    '''pooling_layer.linear''': '''projector''',
    '''pooling_layer.projection''': '''classifier''',
}
_lowercase     							=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]






def      _A				(UpperCamelCase		:      Optional[int] )  ->Optional[int]:



   '''simple docstring'''







   lowerCamelCase__ :      Any								=		{}
   with open(UpperCamelCase      ,			"""r""" ) as file:
      for line_number, line in enumerate(UpperCamelCase ):
         lowerCamelCase__ :      List[str]								=		line.strip()
         if line:
            lowerCamelCase__ :      List[str]								=		line.split()
            lowerCamelCase__ :      Optional[int]								=		line_number
            lowerCamelCase__ :      Union[str, Any]								=		words[0]
            lowerCamelCase__ :      Optional[int]								=		value
   return result






def      _A				(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Any      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      List[Any] )  ->Optional[int]:



   '''simple docstring'''







   for attribute in key.split(""".""" ):
      lowerCamelCase__ :      Any								=		getattr(UpperCamelCase      ,			UpperCamelCase )

   lowerCamelCase__ :      Optional[int]								=		None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(UpperCamelCase ):
         lowerCamelCase__ :      List[str]								=		PARAM_MAPPING[full_name.split(""".""" )[-1]]
         lowerCamelCase__ :      int								=		"""param"""

   if weight_type is not None and weight_type != "param":
      lowerCamelCase__ :      Union[str, Any]								=		getattr(UpperCamelCase      ,			UpperCamelCase ).shape
   elif weight_type is not None and weight_type == "param":
      lowerCamelCase__ :      Optional[int]								=		hf_pointer
      for attribute in hf_param_name.split(""".""" ):
         lowerCamelCase__ :      Optional[Any]								=		getattr(UpperCamelCase      ,			UpperCamelCase )
      lowerCamelCase__ :      List[Any]								=		shape_pointer.shape

      # let's reduce dimension
      lowerCamelCase__ :      Union[str, Any]								=		value[0]
   else:
      lowerCamelCase__ :      List[str]								=		hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          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__ :      List[str]								=		value
   elif weight_type == "weight_g":
      lowerCamelCase__ :      Any								=		value
   elif weight_type == "weight_v":
      lowerCamelCase__ :      Optional[Any]								=		value
   elif weight_type == "bias":
      lowerCamelCase__ :      Any								=		value
   elif weight_type == "param":
      for attribute in hf_param_name.split(""".""" ):
         lowerCamelCase__ :      int								=		getattr(UpperCamelCase      ,			UpperCamelCase )
      lowerCamelCase__ :      int								=		value
   else:
      lowerCamelCase__ :      Optional[int]								=		value

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






def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      Dict      ,			UpperCamelCase		:      List[Any]      ,			UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      Optional[int] )  ->List[str]:



   '''simple docstring'''







   lowerCamelCase__ :      int								=		None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(UpperCamelCase ):
         lowerCamelCase__ :      List[str]								=		PARAM_MAPPING[full_name.split(""".""" )[-1]]
         lowerCamelCase__ :      Tuple								=		"""param"""

   if weight_type is not None and weight_type != "param":
      lowerCamelCase__ :      Dict								=		""".""".join([key, weight_type] )
   elif weight_type is not None and weight_type == "param":
      lowerCamelCase__ :      Tuple								=		""".""".join([key, hf_param_name] )
   else:
      lowerCamelCase__ :      Dict								=		key

   lowerCamelCase__ :      int								=		value if """lm_head""" in full_key else value[0]


_lowercase     							=						{
    '''W_a''': '''linear_1.weight''',
    '''W_b''': '''linear_2.weight''',
    '''b_a''': '''linear_1.bias''',
    '''b_b''': '''linear_2.bias''',
    '''ln_W''': '''norm.weight''',
    '''ln_b''': '''norm.bias''',
}






def      _A				(UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Optional[Any]=None      ,			UpperCamelCase		:      Any=None )  ->List[str]:



   '''simple docstring'''







   lowerCamelCase__ :      str								=		False
   for key, mapped_key in MAPPING.items():
      lowerCamelCase__ :      Tuple								=		"""wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
      if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
         lowerCamelCase__ :      List[Any]								=		True
         if "*" in mapped_key:
            lowerCamelCase__ :      int								=		name.split(UpperCamelCase )[0].split(""".""" )[-2]
            lowerCamelCase__ :      Tuple								=		mapped_key.replace("""*"""      ,			UpperCamelCase )
         if "weight_g" in name:
            lowerCamelCase__ :      Optional[int]								=		"""weight_g"""
         elif "weight_v" in name:
            lowerCamelCase__ :      List[Any]								=		"""weight_v"""
         elif "bias" in name:
            lowerCamelCase__ :      List[str]								=		"""bias"""
         elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            lowerCamelCase__ :      List[str]								=		"""weight"""
         else:
            lowerCamelCase__ :      Optional[int]								=		None
         if hf_dict is not None:
            rename_dict(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
         else:
            set_recursively(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
         return is_used
   return is_used






def      _A				(UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Optional[int]      ,			UpperCamelCase		:      Tuple )  ->int:



   '''simple docstring'''







   lowerCamelCase__ :      Union[str, Any]								=		[]
   lowerCamelCase__ :      Dict								=		fairseq_model.state_dict()

   lowerCamelCase__ :      List[Any]								=		hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      lowerCamelCase__ :      Dict								=		False
      if "conv_layers" in name:
         load_conv_layer(
             UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			hf_model.config.feat_extract_norm == """group"""      ,			)
         lowerCamelCase__ :      Dict								=		True
      else:
         lowerCamelCase__ :      int								=		load_wavaveca_layer(UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
      if not is_used:
         unused_weights.append(UpperCamelCase )

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






def      _A				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      int      ,			UpperCamelCase		:      str      ,			UpperCamelCase		:      Tuple      ,			UpperCamelCase		:      Union[str, Any] )  ->str:



   '''simple docstring'''







   lowerCamelCase__ :      Any								=		full_name.split("""conv_layers.""" )[-1]
   lowerCamelCase__ :      List[str]								=		name.split(""".""" )
   lowerCamelCase__ :      Optional[Any]								=		int(items[0] )
   lowerCamelCase__ :      Dict								=		int(items[1] )

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
         lowerCamelCase__ :      Optional[int]								=		value
         logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
         lowerCamelCase__ :      List[str]								=		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:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." )
         lowerCamelCase__ :      str								=		value
         logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                f"{full_name} has size {value.shape}, but"
                f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." )
         lowerCamelCase__ :      Tuple								=		value
         logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
   else:
      unused_weights.append(UpperCamelCase )






@torch.no_grad()
def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      str      ,			UpperCamelCase		:      List[Any]=None      ,			UpperCamelCase		:      List[Any]=None      ,			UpperCamelCase		:      Any=True      ,			UpperCamelCase		:      Optional[int]=False )  ->List[str]:



   '''simple docstring'''







   if config_path is not None:
      lowerCamelCase__ :      Optional[Any]								=		WavaVecaConfig.from_pretrained(UpperCamelCase )
   else:
      lowerCamelCase__ :      Any								=		WavaVecaConfig()

   if is_seq_class:
      lowerCamelCase__ :      int								=		read_txt_into_dict(UpperCamelCase )
      lowerCamelCase__ :      List[str]								=		idalabel
      lowerCamelCase__ :      Union[str, Any]								=		WavaVecaForSequenceClassification(UpperCamelCase )
      lowerCamelCase__ :      Optional[Any]								=		WavaVecaFeatureExtractor(
          feature_size=1      ,			sampling_rate=16000      ,			padding_value=0      ,			do_normalize=UpperCamelCase      ,			return_attention_mask=UpperCamelCase      ,			)
      feature_extractor.save_pretrained(UpperCamelCase )

   elif is_finetuned:
      if dict_path:
         lowerCamelCase__ :      List[Any]								=		Dictionary.load(UpperCamelCase )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         lowerCamelCase__ :      Optional[Any]								=		target_dict.pad_index
         lowerCamelCase__ :      Any								=		target_dict.bos_index
         lowerCamelCase__ :      Any								=		target_dict.eos_index
         lowerCamelCase__ :      str								=		len(target_dict.symbols )
         lowerCamelCase__ :      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 )
         lowerCamelCase__ :      Optional[int]								=		target_dict.indices

         # fairseq has the <pad> and <s> switched
         lowerCamelCase__ :      List[str]								=		0
         lowerCamelCase__ :      List[str]								=		1
         with open(UpperCamelCase      ,			"""w"""      ,			encoding="""utf-8""" ) as vocab_handle:
            json.dump(UpperCamelCase      ,			UpperCamelCase )
         lowerCamelCase__ :      Optional[int]								=		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      ,			)
         lowerCamelCase__ :      Optional[int]								=		True if config.feat_extract_norm == """layer""" else False
         lowerCamelCase__ :      Optional[int]								=		WavaVecaFeatureExtractor(
             feature_size=1      ,			sampling_rate=16000      ,			padding_value=0      ,			do_normalize=UpperCamelCase      ,			return_attention_mask=UpperCamelCase      ,			)
         lowerCamelCase__ :      Optional[int]								=		WavaVecaProcessor(feature_extractor=UpperCamelCase      ,			tokenizer=UpperCamelCase )
         processor.save_pretrained(UpperCamelCase )

      lowerCamelCase__ :      List[str]								=		WavaVecaForCTC(UpperCamelCase )
   else:
      lowerCamelCase__ :      Optional[Any]								=		WavaVecaForPreTraining(UpperCamelCase )

   if is_finetuned or is_seq_class:
      lowerCamelCase__ :      Optional[Any]								=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]      ,			arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
   else:
      lowerCamelCase__ :      int								=		argparse.Namespace(task="""audio_pretraining""" )
      lowerCamelCase__ :      Tuple								=		fairseq.tasks.setup_task(UpperCamelCase )

      lowerCamelCase__ :      Any								=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,			task=UpperCamelCase )

   lowerCamelCase__ :      Any								=		model[0].eval()

   recursively_load_weights(UpperCamelCase      ,			UpperCamelCase      ,			not is_finetuned )

   hf_wavavec.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
    _lowercase     							=						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(
        '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
    )
    parser.add_argument(
        '''--is_seq_class''',
        action='''store_true''',
        help='''Whether the model to convert is a fine-tuned sequence classification model or not''',
    )
    _lowercase     							=						parser.parse_args()

    _lowercase     							=						not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )








from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_lowercase     							=						(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowercase     							=						[ord(letter) for letter in string.ascii_lowercase]
_lowercase     							=						{ord(char) for char in VALID_CHARS}

_lowercase     							=						["the", "be", "to", "of", "and", "in", "that", "have"]






def      _A				(UpperCamelCase		:      list[int]      ,			UpperCamelCase		:      tuple[int, ...] )  ->str | None:



			'''simple docstring'''







			lowerCamelCase__ :      str								=		""
			lowerCamelCase__ :      int
			lowerCamelCase__ :      int
			lowerCamelCase__ :      int

			for keychar, cipherchar in zip(cycle(UpperCamelCase )      ,			UpperCamelCase ):
						lowerCamelCase__ :      str								=		cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(UpperCamelCase )

			return decoded






def      _A				(UpperCamelCase		:      list[int] )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      list[str]								=		[]
			for key in product(UpperCamelCase      ,			repeat=3 ):
						lowerCamelCase__ :      Union[str, Any]								=		try_key(UpperCamelCase      ,			UpperCamelCase )
						if encoded is not None:
									possibles.append(UpperCamelCase )
			return possibles






def      _A				(UpperCamelCase		:      list[str]      ,			UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			return [possible for possible in possibles if common_word in possible.lower()]






def      _A				(UpperCamelCase		:      str = "p059_cipher.txt" )  ->int:



			'''simple docstring'''







			lowerCamelCase__ :      list[int]
			lowerCamelCase__ :      list[str]
			lowerCamelCase__ :      str
			lowerCamelCase__ :      str
			lowerCamelCase__ :      str								=		Path(UpperCamelCase ).parent.joinpath(UpperCamelCase ).read_text(encoding="""utf-8""" )

			lowerCamelCase__ :      str								=		[int(UpperCamelCase ) for number in data.strip().split(""",""" )]

			lowerCamelCase__ :      Optional[Any]								=		filter_valid_chars(UpperCamelCase )
			for common_word in COMMON_WORDS:
						lowerCamelCase__ :      Optional[int]								=		filter_common_word(UpperCamelCase      ,			UpperCamelCase )
						if len(UpperCamelCase ) == 1:
									break

			lowerCamelCase__ :      Union[str, Any]								=		possibles[0]
			return sum(ord(UpperCamelCase ) for char in decoded_text )


if __name__ == "__main__":
				print(F'''{solution() = }''')



