import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=      logging.get_logger("""transformers.models.speecht5""")

_SCREAMING_SNAKE_CASE 		=      {
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
_SCREAMING_SNAKE_CASE 		=      {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_SCREAMING_SNAKE_CASE 		=      {
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
_SCREAMING_SNAKE_CASE 		=      {
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
_SCREAMING_SNAKE_CASE 		=      {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_SCREAMING_SNAKE_CASE 		=      {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_SCREAMING_SNAKE_CASE 		=      {
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
_SCREAMING_SNAKE_CASE 		=      {
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
_SCREAMING_SNAKE_CASE 		=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_SCREAMING_SNAKE_CASE 		=      {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_SCREAMING_SNAKE_CASE 		=      {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_SCREAMING_SNAKE_CASE 		=      []
_SCREAMING_SNAKE_CASE 		=      [
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
_SCREAMING_SNAKE_CASE 		=      IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_SCREAMING_SNAKE_CASE 		=      IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
_SCREAMING_SNAKE_CASE 		=      IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''






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

 if weight_type is not None:
  UpperCamelCase			  =      getattr(__UpperCamelCase							,					__UpperCamelCase       ).shape
 else:
  UpperCamelCase			  =      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":
  UpperCamelCase			  =      value
 elif weight_type == "weight_g":
  UpperCamelCase			  =      value
 elif weight_type == "weight_v":
  UpperCamelCase			  =      value
 elif weight_type == "bias":
  UpperCamelCase			  =      value
 elif weight_type == "running_mean":
  UpperCamelCase			  =      value
 elif weight_type == "running_var":
  UpperCamelCase			  =      value
 elif weight_type == "num_batches_tracked":
  UpperCamelCase			  =      value
 else:
  UpperCamelCase			  =      value

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





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Dict:







 '''simple docstring'''






 for key in ignore_keys:
  if key.endswith(""".*"""       ):
   if name.startswith(key[:-1]       ):
    return True
  elif ".*." in key:
   UpperCamelCase			  =      key.split(""".*."""       )
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 if task == "s2t":
  UpperCamelCase			  =      hf_model.speechta.encoder.prenet.feature_encoder
  UpperCamelCase			  =      MAPPING_S2T
  UpperCamelCase			  =      IGNORE_KEYS_S2T
 elif task == "t2s":
  UpperCamelCase			  =      None
  UpperCamelCase			  =      MAPPING_T2S
  UpperCamelCase			  =      IGNORE_KEYS_T2S
 elif task == "s2s":
  UpperCamelCase			  =      hf_model.speechta.encoder.prenet.feature_encoder
  UpperCamelCase			  =      MAPPING_S2S
  UpperCamelCase			  =      IGNORE_KEYS_S2S
 else:
  raise ValueError(f"""Unsupported task: {task}"""       )

 for name, value in fairseq_dict.items():
  if should_ignore(__UpperCamelCase							,					__UpperCamelCase       ):
   logger.info(f"""{name} was ignored"""       )
   continue

  UpperCamelCase			  =      False
  if "conv_layers" in name:
   load_conv_layer(
       __UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase							,					hf_model.config.feat_extract_norm == """group"""							,					)
   UpperCamelCase			  =      True
  else:
   for key, mapped_key in MAPPING.items():
    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

    if "*" in key:
     UpperCamelCase			  =      key.split(""".*."""       )
     if prefix in name and suffix in name:
      UpperCamelCase			  =      suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
    if key in name:
     UpperCamelCase			  =      True
     if "*" in mapped_key:
      UpperCamelCase			  =      name.split(__UpperCamelCase       )[0].split("""."""       )[-2]
      UpperCamelCase			  =      mapped_key.replace("""*"""							,					__UpperCamelCase       )
     if "weight_g" in name:
      UpperCamelCase			  =      'weight_g'
     elif "weight_v" in name:
      UpperCamelCase			  =      'weight_v'
     elif "bias" in name:
      UpperCamelCase			  =      'bias'
     elif "weight" in name:
      UpperCamelCase			  =      'weight'
     elif "running_mean" in name:
      UpperCamelCase			  =      'running_mean'
     elif "running_var" in name:
      UpperCamelCase			  =      'running_var'
     elif "num_batches_tracked" in name:
      UpperCamelCase			  =      'num_batches_tracked'
     else:
      UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      full_name.split("""conv_layers."""       )[-1]
 UpperCamelCase			  =      name.split("""."""       )
 UpperCamelCase			  =      int(items[0]       )
 UpperCamelCase			  =      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."""       )
   UpperCamelCase			  =      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."""       )
   UpperCamelCase			  =      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."""       )
   UpperCamelCase			  =      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."""       )
   UpperCamelCase			  =      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 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None							,					UpperCamelCase_=None							,					UpperCamelCase_=None							,					)		->	Dict:







 '''simple docstring'''






 if config_path is not None:
  UpperCamelCase			  =      SpeechTaConfig.from_pretrained(__UpperCamelCase       )
 else:
  UpperCamelCase			  =      SpeechTaConfig()

 if task == "s2t":
  UpperCamelCase			  =      config.max_text_positions
  UpperCamelCase			  =      SpeechTaForSpeechToText(__UpperCamelCase       )
 elif task == "t2s":
  UpperCamelCase			  =      1876
  UpperCamelCase			  =      600
  UpperCamelCase			  =      config.max_speech_positions
  UpperCamelCase			  =      SpeechTaForTextToSpeech(__UpperCamelCase       )
 elif task == "s2s":
  UpperCamelCase			  =      1876
  UpperCamelCase			  =      config.max_speech_positions
  UpperCamelCase			  =      SpeechTaForSpeechToSpeech(__UpperCamelCase       )
 else:
  raise ValueError(f"""Unknown task name: {task}"""       )

 if vocab_path:
  UpperCamelCase			  =      SpeechTaTokenizer(__UpperCamelCase							,					model_max_length=config.max_text_positions       )

  # Mask token behaves like a normal word, i.e. include the space before it
  UpperCamelCase			  =      AddedToken("""<mask>"""							,					lstrip=__UpperCamelCase							,					rstrip=__UpperCamelCase       )
  UpperCamelCase			  =      mask_token
  tokenizer.add_special_tokens({"""mask_token""": mask_token}       )
  tokenizer.add_tokens(["""<ctc_blank>"""]       )

 UpperCamelCase			  =      SpeechTaFeatureExtractor()
 UpperCamelCase			  =      SpeechTaProcessor(tokenizer=__UpperCamelCase							,					feature_extractor=__UpperCamelCase       )
 processor.save_pretrained(__UpperCamelCase       )

 UpperCamelCase			  =      torch.load(__UpperCamelCase       )
 recursively_load_weights(fairseq_checkpoint["""model"""]							,					__UpperCamelCase							,					__UpperCamelCase       )

 model.save_pretrained(__UpperCamelCase       )

 if repo_id:
  print("""Pushing to the hub..."""       )
  processor.push_to_hub(__UpperCamelCase       )
  model.push_to_hub(__UpperCamelCase       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     parser.add_argument(
         """--task""",
         default="""s2t""",
         type=str,
         help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
     )
     parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
     )

     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )






'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


UpperCamelCase_    	=				logging.get_logger(__name__)

UpperCamelCase_    	=				{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCamelCase_    	=				{
    "vocab_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
        ),
        "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
        "distilbert-base-uncased-distilled-squad": (
            "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
        "distilbert-base-cased-distilled-squad": (
            "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
        ),
        "distilbert-base-german-cased": (
            "https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
        ),
        "distilbert-base-multilingual-cased": (
            "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase_    	=				{
    "distilbert-base-uncased": 5_1_2,
    "distilbert-base-uncased-distilled-squad": 5_1_2,
    "distilbert-base-cased": 5_1_2,
    "distilbert-base-cased-distilled-squad": 5_1_2,
    "distilbert-base-german-cased": 5_1_2,
    "distilbert-base-multilingual-cased": 5_1_2,
}


UpperCamelCase_    	=				{
    "distilbert-base-uncased": {"do_lower_case": True},
    "distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
    "distilbert-base-cased": {"do_lower_case": False},
    "distilbert-base-cased-distilled-squad": {"do_lower_case": False},
    "distilbert-base-german-cased": {"do_lower_case": False},
    "distilbert-base-multilingual-cased": {"do_lower_case": False},
}






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				List[Any]        =						VOCAB_FILES_NAMES
       A						:				Dict        =						PRETRAINED_VOCAB_FILES_MAP
       A						:				Optional[int]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				Optional[Any]        =						PRETRAINED_INIT_CONFIGURATION
       A						:				Optional[int]        =						['''input_ids''', '''attention_mask''']
       A						:				List[Any]        =						DistilBertTokenizer







       def __init__(      self,							A=None,							A=None,							A=True,							A="[UNK]",							A="[SEP]",							A="[PAD]",							A="[CLS]",							A="[MASK]",							A=True,							A=None,							**A,							):







             '''simple docstring'''







             super().__init__(
                 A,							tokenizer_file=A,							do_lower_case=A,							unk_token=A,							sep_token=A,							pad_token=A,							cls_token=A,							mask_token=A,							tokenize_chinese_chars=A,							strip_accents=A,							**A,							)

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
             if (
                 normalizer_state.get('lowercase',							A  ) != do_lower_case
                 or normalizer_state.get('strip_accents',							A  ) != strip_accents
                 or normalizer_state.get('handle_chinese_chars',							A  ) != tokenize_chinese_chars
             ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						getattr(A,							normalizer_state.pop('type'  )  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						do_lower_case
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						strip_accents
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenize_chinese_chars
                   SCREAMING_SNAKE_CASE   :      Dict    					=						normalizer_class(**A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						do_lower_case







       def        UpperCamelCase_							(      self,							A,							A=None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

             if token_ids_a:
                   output += token_ids_a + [self.sep_token_id]

             return output







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						[self.sep_token_id]
             SCREAMING_SNAKE_CASE   :      str    					=						[self.cls_token_id]
             if token_ids_a is None:
                   return len(cls + token_ids_a + sep  ) * [0]
             return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self._tokenizer.model.save(A,							name=A  )
             return tuple(A  )


