



"""simple docstring"""






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()
snake_case   					=				logging.get_logger('''transformers.models.speecht5''')

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''',
}
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''',
}
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''',
}
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''',
}
snake_case   					=				{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
snake_case   					=				{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
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''',
}
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''',
}
snake_case   					=				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
snake_case   					=				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
snake_case   					=				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
snake_case   					=				[]
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''',
]
snake_case   					=				IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
snake_case   					=				IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
snake_case   					=				IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Optional[Any]:
  for attribute in key.split('''.'''	):
    _snake_case   	=      getattr(lowerCAmelCase_  ,						lowerCAmelCase_	)

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

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Optional[Any]:
  for key in ignore_keys:
    if key.endswith('''.*'''	):
      if name.startswith(key[:-1]	):
        return True
    elif ".*." in key:
      _snake_case	,     _snake_case   	=      key.split('''.*.'''	)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				int:
  _snake_case   	=      []

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

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

    _snake_case   	=      False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						hf_model.config.feat_extract_norm == '''group'''  ,						)
      _snake_case   	=      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:
          _snake_case	,     _snake_case   	=      key.split('''.*.'''	)
          if prefix in name and suffix in name:
            _snake_case   	=      suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          _snake_case   	=      True
          if "*" in mapped_key:
            _snake_case   	=      name.split(lowerCAmelCase_	)[0].split('''.'''	)[-2]
            _snake_case   	=      mapped_key.replace('''*'''  ,						lowerCAmelCase_	)
          if "weight_g" in name:
            _snake_case   	=      '''weight_g'''
          elif "weight_v" in name:
            _snake_case   	=      '''weight_v'''
          elif "bias" in name:
            _snake_case   	=      '''bias'''
          elif "weight" in name:
            _snake_case   	=      '''weight'''
          elif "running_mean" in name:
            _snake_case   	=      '''running_mean'''
          elif "running_var" in name:
            _snake_case   	=      '''running_var'''
          elif "num_batches_tracked" in name:
            _snake_case   	=      '''num_batches_tracked'''
          else:
            _snake_case   	=      None
          set_recursively(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)
        continue
    if not is_used:
      unused_weights.append(lowerCAmelCase_	)

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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Any:
  _snake_case   	=      full_name.split('''conv_layers.'''	)[-1]
  _snake_case   	=      name.split('''.'''	)
  _snake_case   	=      int(items[0]	)
  _snake_case   	=      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."""	)
      _snake_case   	=      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."""	)
      _snake_case   	=      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."""	)
      _snake_case   	=      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."""	)
      _snake_case   	=      value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
  else:
    unused_weights.append(lowerCAmelCase_	)




@torch.no_grad()
def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						)						->				Tuple:
  if config_path is not None:
    _snake_case   	=      SpeechTaConfig.from_pretrained(lowerCAmelCase_	)
  else:
    _snake_case   	=      SpeechTaConfig()

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

  if vocab_path:
    _snake_case   	=      SpeechTaTokenizer(lowerCAmelCase_  ,						model_max_length=config.max_text_positions	)

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

  _snake_case   	=      SpeechTaFeatureExtractor()
  _snake_case   	=      SpeechTaProcessor(tokenizer=lowerCAmelCase_  ,						feature_extractor=lowerCAmelCase_	)
  processor.save_pretrained(lowerCAmelCase_	)

  _snake_case   	=      torch.load(lowerCAmelCase_	)
  recursively_load_weights(fairseq_checkpoint['''model''']  ,						lowerCAmelCase_  ,						lowerCAmelCase_	)

  model.save_pretrained(lowerCAmelCase_	)

  if repo_id:
    print('''Pushing to the hub...'''	)
    processor.push_to_hub(lowerCAmelCase_	)
    model.push_to_hub(lowerCAmelCase_	)


if __name__ == "__main__":
     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.'''
     )

     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
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     snake_case   					=				'''pt'''
elif is_tf_available():
     snake_case   					=				'''tf'''
else:
     snake_case   					=				'''jax'''



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Union[str, Any]           =  PerceiverTokenizer
   A__		:	Optional[int]           =  False



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     super().setUp()
     _snake_case   	=      PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname				)



   @cached_property
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       **__lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     return self.tokenizer_class.from_pretrained(self.tmpdirname							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	int=2_0							,       __lowerCamelCase    :	List[Any]=5				):



     """simple docstring"""
     # XXX The default common tokenizer tests assume that every ID is decodable on its own.
     # This assumption is invalid for Perceiver because single bytes might not be
     # valid utf-8 (byte 128 for instance).
     # Here we're overriding the smallest possible method to provide
     # a clean sequence without making the same assumption.

     _snake_case   	=      []
     for i in range(len(__lowerCamelCase				)				):
       try:
         _snake_case   	=      tokenizer.decode([i]							,       clean_up_tokenization_spaces=__lowerCamelCase				)
       except UnicodeDecodeError:
         pass
       toks.append((i, tok)				)

     _snake_case   	=      list(filter(lambda __lowerCamelCase				: re.match(R'''^[ a-zA-Z]+$'''							,       t[1]				)							,       __lowerCamelCase				)				)
     _snake_case   	=      list(filter(lambda __lowerCamelCase				: [t[0]] == tokenizer.encode(t[1]							,       add_special_tokens=__lowerCamelCase				)							,       __lowerCamelCase				)				)
     if max_length is not None and len(__lowerCamelCase				) > max_length:
       _snake_case   	=      toks[:max_length]
     if min_length is not None and len(__lowerCamelCase				) < min_length and len(__lowerCamelCase				) > 0:
       while len(__lowerCamelCase				) < min_length:
         _snake_case   	=      toks + toks
        # toks_str = [t[1] for t in toks]
     _snake_case   	=      [t[0] for t in toks]

     # Ensure consistency
     _snake_case   	=      tokenizer.decode(__lowerCamelCase							,       clean_up_tokenization_spaces=__lowerCamelCase				)
     if " " not in output_txt and len(__lowerCamelCase				) > 1:
       _snake_case   	=      (
           tokenizer.decode([toks_ids[0]]							,       clean_up_tokenization_spaces=__lowerCamelCase				)
           + ''' '''
           + tokenizer.decode(toks_ids[1:]							,       clean_up_tokenization_spaces=__lowerCamelCase				)
       )
     if with_prefix_space:
       _snake_case   	=      ''' ''' + output_txt
     _snake_case   	=      tokenizer.encode(__lowerCamelCase							,       add_special_tokens=__lowerCamelCase				)
     return output_txt, output_ids



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     _snake_case   	=      '''Unicode €.'''
     _snake_case   	=      tokenizer(__lowerCamelCase				)
     _snake_case   	=      [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
     self.assertEqual(encoded['''input_ids''']							,       __lowerCamelCase				)

     # decoding
     _snake_case   	=      tokenizer.decode(__lowerCamelCase				)
     self.assertEqual(__lowerCamelCase							,       '''[CLS]Unicode €.[SEP]'''				)

     _snake_case   	=      tokenizer('''e è é ê ë'''				)
     _snake_case   	=      [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
     self.assertEqual(encoded['''input_ids''']							,       __lowerCamelCase				)
     # decoding
     _snake_case   	=      tokenizer.decode(__lowerCamelCase				)
     self.assertEqual(__lowerCamelCase							,       '''[CLS]e è é ê ë[SEP]'''				)

     # encode/decode, but with `encode` instead of `__call__`
     self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''				)				)							,       '''[CLS]e è é ê ë[SEP]'''				)



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     _snake_case   	=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     _snake_case   	=      [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
     # fmt: on
     _snake_case   	=      tokenizer(__lowerCamelCase							,       padding=__lowerCamelCase							,       return_tensors=__lowerCamelCase				)
     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)

     if FRAMEWORK != "jax":
       _snake_case   	=      list(batch.input_ids.numpy()[0]				)
     else:
       _snake_case   	=      list(batch.input_ids.tolist()[0]				)

     self.assertListEqual(__lowerCamelCase							,       __lowerCamelCase				)

     self.assertEqual((2, 3_8)							,       batch.input_ids.shape				)
     self.assertEqual((2, 3_8)							,       batch.attention_mask.shape				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     _snake_case   	=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     _snake_case   	=      tokenizer(__lowerCamelCase							,       padding=__lowerCamelCase							,       return_tensors=__lowerCamelCase				)
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn('''input_ids'''							,       __lowerCamelCase				)
     self.assertIn('''attention_mask'''							,       __lowerCamelCase				)
     self.assertNotIn('''decoder_input_ids'''							,       __lowerCamelCase				)
     self.assertNotIn('''decoder_attention_mask'''							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     _snake_case   	=      [
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     _snake_case   	=      tokenizer(
         text_target=__lowerCamelCase							,       max_length=3_2							,       padding='''max_length'''							,       truncation=__lowerCamelCase							,       return_tensors=__lowerCamelCase				)
     self.assertEqual(3_2							,       targets['''input_ids'''].shape[1]				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     # safety check on max_len default value so we are sure the test works
     _snake_case   	=      self.get_tokenizers()
     for tokenizer in tokenizers:
       with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
         self.assertNotEqual(tokenizer.model_max_length							,       4_2				)

        # Now let's start the test
     _snake_case   	=      self.get_tokenizers()
     for tokenizer in tokenizers:
       with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
         # Isolate this from the other tests because we save additional tokens/etc
         _snake_case   	=      tempfile.mkdtemp()

         _snake_case   	=      ''' He is very happy, UNwant\u00E9d,running'''
         _snake_case   	=      tokenizer.encode(__lowerCamelCase							,       add_special_tokens=__lowerCamelCase				)
         tokenizer.save_pretrained(__lowerCamelCase				)

         _snake_case   	=      tokenizer.__class__.from_pretrained(__lowerCamelCase				)
         _snake_case   	=      after_tokenizer.encode(__lowerCamelCase							,       add_special_tokens=__lowerCamelCase				)
         self.assertListEqual(__lowerCamelCase							,       __lowerCamelCase				)

         shutil.rmtree(__lowerCamelCase				)

     _snake_case   	=      self.get_tokenizers(model_max_length=4_2				)
     for tokenizer in tokenizers:
       with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
         # Isolate this from the other tests because we save additional tokens/etc
         _snake_case   	=      tempfile.mkdtemp()

         _snake_case   	=      ''' He is very happy, UNwant\u00E9d,running'''
         tokenizer.add_tokens(['''bim''', '''bambam''']				)
         _snake_case   	=      tokenizer.additional_special_tokens
         additional_special_tokens.append('''new_additional_special_token'''				)
         tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}				)
         _snake_case   	=      tokenizer.encode(__lowerCamelCase							,       add_special_tokens=__lowerCamelCase				)
         tokenizer.save_pretrained(__lowerCamelCase				)

         _snake_case   	=      tokenizer.__class__.from_pretrained(__lowerCamelCase				)
         _snake_case   	=      after_tokenizer.encode(__lowerCamelCase							,       add_special_tokens=__lowerCamelCase				)
         self.assertListEqual(__lowerCamelCase							,       __lowerCamelCase				)
         self.assertIn('''new_additional_special_token'''							,       after_tokenizer.additional_special_tokens				)
         self.assertEqual(after_tokenizer.model_max_length							,       4_2				)

         _snake_case   	=      tokenizer.__class__.from_pretrained(__lowerCamelCase							,       model_max_length=4_3				)
         self.assertEqual(tokenizer.model_max_length							,       4_3				)

         shutil.rmtree(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      []
     if self.test_slow_tokenizer:
       tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())				)

     if self.test_rust_tokenizer:
       tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())				)

     for tokenizer_class, tokenizer_utils in tokenizer_list:
       with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer_utils.save_pretrained(__lowerCamelCase				)

         with open(os.path.join(__lowerCamelCase							,       '''special_tokens_map.json'''				)							,       encoding='''utf-8'''				) as json_file:
           _snake_case   	=      json.load(__lowerCamelCase				)

         with open(os.path.join(__lowerCamelCase							,       '''tokenizer_config.json'''				)							,       encoding='''utf-8'''				) as json_file:
           _snake_case   	=      json.load(__lowerCamelCase				)

         _snake_case   	=      [f"""<extra_id_{i}>""" for i in range(1_2_5				)]

         _snake_case   	=      added_tokens_extra_ids + [
             '''an_additional_special_token'''
         ]
         _snake_case   	=      added_tokens_extra_ids + [
             '''an_additional_special_token'''
         ]

         with open(os.path.join(__lowerCamelCase							,       '''special_tokens_map.json'''				)							,       '''w'''							,       encoding='''utf-8'''				) as outfile:
           json.dump(__lowerCamelCase							,       __lowerCamelCase				)
         with open(os.path.join(__lowerCamelCase							,       '''tokenizer_config.json'''				)							,       '''w'''							,       encoding='''utf-8'''				) as outfile:
           json.dump(__lowerCamelCase							,       __lowerCamelCase				)

         # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
         # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
         # "special_tokens_map.json" files
         _snake_case   	=      tokenizer_class.from_pretrained(
             __lowerCamelCase							,       )
         self.assertIn(
             '''an_additional_special_token'''							,       tokenizer_without_change_in_init.additional_special_tokens				)
         self.assertEqual(
             ['''an_additional_special_token''']							,       tokenizer_without_change_in_init.convert_ids_to_tokens(
                 tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']				)				)							,       )

         # Now we test that we can change the value of additional_special_tokens in the from_pretrained
         _snake_case   	=      added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''							,       lstrip=__lowerCamelCase				)]
         _snake_case   	=      tokenizer_class.from_pretrained(
             __lowerCamelCase							,       additional_special_tokens=__lowerCamelCase							,       )

         self.assertIn('''a_new_additional_special_token'''							,       tokenizer.additional_special_tokens				)
         self.assertEqual(
             ['''a_new_additional_special_token''']							,       tokenizer.convert_ids_to_tokens(
                 tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']				)				)							,       )



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([1_7_8]				)							,       '''�'''				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     pass





   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     _snake_case   	=      self.get_tokenizers(fast=__lowerCamelCase							,       do_lower_case=__lowerCamelCase				)
     for tokenizer in tokenizers:
       with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
         _snake_case   	=      ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
         _snake_case   	=      tokenizer.convert_tokens_to_string(__lowerCamelCase				)

         self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)





