import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_xlnet import XLNetTokenizer
else:
   a_    					=       None


a_    					=       logging.get_logger(__name__)

a_    					=       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a_    					=       {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

a_    					=       {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

a_    					=       '▁'

# Segments (not really needed)
a_    					=       0
a_    					=       1
a_    					=       2
a_    					=       3
a_    					=       4





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   VOCAB_FILES_NAMES
 lowercase				     =   PRETRAINED_VOCAB_FILES_MAP
 lowercase				     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase				     =   'left'
 lowercase				     =   XLNetTokenizer

 def __init__( self	:							Optional[Any]   ,      snake_case	:							Any=None   ,      snake_case	:							int=None   ,      snake_case	:							Any=False   ,      snake_case	:							str=True   ,      snake_case	:							Dict=False   ,      snake_case	:							int="<s>"   ,      snake_case	:							Tuple="</s>"   ,      snake_case	:							Tuple="<unk>"   ,      snake_case	:							List[Any]="<sep>"   ,      snake_case	:							List[str]="<pad>"   ,      snake_case	:							List[Any]="<cls>"   ,      snake_case	:							str="<mask>"   ,      snake_case	:							List[str]=["<eop>", "<eod>"]   ,      **snake_case	:							Optional[int]   ,      )   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					AddedToken(snake_case   ,      lstrip=snake_case   ,      rstrip=snake_case			) if isinstance(snake_case   ,      snake_case			) else mask_token

  super().__init__(
      vocab_file=snake_case   ,      tokenizer_file=snake_case   ,      do_lower_case=snake_case   ,      remove_space=snake_case   ,      keep_accents=snake_case   ,      bos_token=snake_case   ,      eos_token=snake_case   ,      unk_token=snake_case   ,      sep_token=snake_case   ,      pad_token=snake_case   ,      cls_token=snake_case   ,      mask_token=snake_case   ,      additional_special_tokens=snake_case   ,      **snake_case   ,      )

  UpperCamelCase_					:						str =					3
  UpperCamelCase_					:						int =					do_lower_case
  UpperCamelCase_					:						int =					remove_space
  UpperCamelCase_					:						Union[str, Any] =					keep_accents
  UpperCamelCase_					:						Optional[int] =					vocab_file
  UpperCamelCase_					:						Optional[Any] =					False if not self.vocab_file else True

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							List[int]   ,      snake_case	:							Optional[List[int]] = None			)   ->						List[int]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					[self.sep_token_id]
  UpperCamelCase_					:						Optional[Any] =					[self.cls_token_id]
  if token_ids_a is None:
   return token_ids_a + sep + cls
  return token_ids_a + sep + token_ids_a + sep + cls

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							List[int]   ,      snake_case	:							Optional[List[int]] = None			)   ->						List[int]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					[self.sep_token_id]
  UpperCamelCase_					:						Tuple =					[2]

  if token_ids_a is None:
   return len(token_ids_a + sep			) * [0] + cls_segment_id
  return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							str   ,      snake_case	:							Optional[str] = None			)   ->						Tuple[str]:

  """simple docstring"""





  if not self.can_save_slow_tokenizer:
   raise ValueError(
       'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
       'tokenizer.'			)

  if not os.path.isdir(snake_case			):
   logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
   return
  UpperCamelCase_					:						Optional[Any] =					os.path.join(
      snake_case   ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

  if os.path.abspath(self.vocab_file			) != os.path.abspath(snake_case			):
   copyfile(self.vocab_file   ,      snake_case			)

  return (out_vocab_file,)




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

 UpperCamelCase_					:						int =					hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'	):
  UpperCamelCase_					:						Tuple =					convert_classification(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForAudioFrameClassification'	):
  UpperCamelCase_					:						List[str] =					convert_diarization(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForXVector'	):
  UpperCamelCase_					:						Union[str, Any] =					convert_xvector(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 else:
  raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"	)

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   a_    					=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



