


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						List[str]		):




      """simple docstring"""





      return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						Dict="attention"		):




      """simple docstring"""





      a		:Optional[Any]									=			np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]		)
      a		:int									=			k_tmp.reshape(k_tmp.shape[0]					,    k_tmp.shape[1] * k_tmp.shape[2]		)
      a		:int									=			np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]		)
      a		:Any									=			o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]					,    o_tmp.shape[2]		)
      a		:Dict									=			np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]		)
      a		:Dict									=			q_tmp.reshape(q_tmp.shape[0]					,    q_tmp.shape[1] * q_tmp.shape[2]		)
      a		:Union[str, Any]									=			np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]		)
      a		:str									=			v_tmp.reshape(v_tmp.shape[0]					,    v_tmp.shape[1] * v_tmp.shape[2]		)
      return k, o, q, v
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Union[str, Any]=False		):




      """simple docstring"""





      if split_mlp_wi:
            a		:List[str]									=			params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
            a		:List[Any]									=			params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
            a		:Union[str, Any]									=			(wi_a, wi_a)
      else:
            a		:Any									=			params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

      a		:Union[str, Any]									=			params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
      return wi, wo
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						Tuple					,    UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						Union[str, Any]		):




      """simple docstring"""





      return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]
def 							__lowerCamelCase		(					UpperCAmelCase_		:						dict					,    *, UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						bool					,    UpperCAmelCase_		:						bool = False		):




      """simple docstring"""





      a		:Any									=			traverse_util.flatten_dict(variables['''target''']		)
      a		:Any									=			{'''/'''.join(UpperCAmelCase_		): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      a		:List[Any]									=			'''encoder/encoder/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''					,    UpperCAmelCase_		)

      a		:Union[str, Any]									=			collections.OrderedDict()

      # Shared embeddings.
      a		:Any									=			old['''token_embedder/embedding''']

      # Encoder.
      for i in range(UpperCAmelCase_		):
            # Block i, layer 0 (Self Attention).
            a		:List[Any]									=			tax_layer_norm_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''encoder'''					,    '''pre_attention_layer_norm'''		)
            a				,							a				,							a				,							a		:Optional[Any]									=			tax_attention_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''encoder'''					,    '''attention'''		)
            a		:Dict									=			layer_norm
            a		:Tuple									=			k.T
            a		:Optional[Any]									=			o.T
            a		:Optional[Any]									=			q.T
            a		:int									=			v.T

            # Block i, layer 1 (MLP).
            a		:Optional[int]									=			tax_layer_norm_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''encoder'''					,    '''pre_mlp_layer_norm'''		)
            a				,							a		:Tuple									=			tax_mlp_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''encoder'''					,    UpperCAmelCase_		)
            a		:Any									=			layer_norm
            if split_mlp_wi:
                  a		:Optional[int]									=			wi[0].T
                  a		:List[Any]									=			wi[1].T
            else:
                  a		:Optional[Any]									=			wi.T
            a		:str									=			wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  a		:Any									=			tax_relpos_bias_lookup(
                      UpperCAmelCase_					,    UpperCAmelCase_					,    '''encoder'''		).T

      a		:List[str]									=			old['''encoder/encoder_norm/scale''']

      if not scalable_attention:
            a		:str									=			tax_relpos_bias_lookup(
                UpperCAmelCase_					,    0					,    '''encoder'''		).T
            a		:Optional[Any]									=			tax_relpos_bias_lookup(
                UpperCAmelCase_					,    0					,    '''decoder'''		).T

      if not is_encoder_only:
            # Decoder.
            for i in range(UpperCAmelCase_		):
                  # Block i, layer 0 (Self Attention).
                  a		:List[Any]									=			tax_layer_norm_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''decoder'''					,    '''pre_self_attention_layer_norm'''		)
                  a				,							a				,							a				,							a		:List[str]									=			tax_attention_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''decoder'''					,    '''self_attention'''		)
                  a		:Union[str, Any]									=			layer_norm
                  a		:Tuple									=			k.T
                  a		:Optional[Any]									=			o.T
                  a		:Any									=			q.T
                  a		:int									=			v.T

                  # Block i, layer 1 (Cross Attention).
                  a		:List[str]									=			tax_layer_norm_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''decoder'''					,    '''pre_cross_attention_layer_norm'''		)
                  a				,							a				,							a				,							a		:Optional[int]									=			tax_attention_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''decoder'''					,    '''encoder_decoder_attention'''		)
                  a		:Any									=			layer_norm
                  a		:str									=			k.T
                  a		:str									=			o.T
                  a		:List[str]									=			q.T
                  a		:Any									=			v.T

                  # Block i, layer 2 (MLP).
                  a		:List[Any]									=			tax_layer_norm_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''decoder'''					,    '''pre_mlp_layer_norm'''		)
                  a				,							a		:List[str]									=			tax_mlp_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''decoder'''					,    UpperCAmelCase_		)
                  a		:List[Any]									=			layer_norm
                  if split_mlp_wi:
                        a		:str									=			wi[0].T
                        a		:Optional[Any]									=			wi[1].T
                  else:
                        a		:str									=			wi.T
                  a		:Union[str, Any]									=			wo.T

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        a		:List[str]									=			tax_relpos_bias_lookup(UpperCAmelCase_					,    UpperCAmelCase_					,    '''decoder'''		).T

            a		:List[Any]									=			old['''decoder/decoder_norm/scale''']

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  a		:Union[str, Any]									=			old['''decoder/logits_dense/kernel'''].T

      return new
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						bool		):




      """simple docstring"""





      a		:str									=			collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            a		:Optional[int]									=			state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  a		:Any									=			state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''		)
                  a		:Tuple									=			state_dict['''shared.weight''']

      return state_dict
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Optional[int]		):




      """simple docstring"""





      a		:Union[str, Any]									=			checkpoints.load_tax_checkpoint(UpperCAmelCase_		)
      a		:List[str]									=			convert_tax_to_pytorch(
          UpperCAmelCase_					,    num_layers=config.num_layers					,    is_encoder_only=UpperCAmelCase_					,    scalable_attention=UpperCAmelCase_		)
      a		:str									=			make_state_dict(UpperCAmelCase_					,    UpperCAmelCase_		)
      model.load_state_dict(UpperCAmelCase_					,    strict=UpperCAmelCase_		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						Tuple					,    UpperCAmelCase_		:						bool = False					,    UpperCAmelCase_		:						bool = False					,    ):




      """simple docstring"""





      a		:str									=			MTaConfig.from_json_file(UpperCAmelCase_		)
      print(F'''Building PyTorch model from configuration: {config}'''		)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            a		:Optional[Any]									=			UMTaEncoderModel(UpperCAmelCase_		)
      else:
            a		:Tuple									=			UMTaForConditionalGeneration(UpperCAmelCase_		)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
      model.save_pretrained(UpperCAmelCase_		)

      # Verify that we can load the checkpoint.
      model.from_pretrained(UpperCAmelCase_		)
      print('''Done'''		)


if __name__ == "__main__":
     snake_case							:       Tuple										=	argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
     )
     parser.add_argument(
         '''--scalable_attention''',
         action='''store_true''',
         help='''Whether the model uses scaled attention (umt5 model)''',
         default=False,
     )
     snake_case							:       Dict										=	parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path,
         args.config_file,
         args.pytorch_dump_path,
         args.is_encoder_only,
         args.scalable_attention,
     )









import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin







class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			CanineTokenizer
 SCREAMING_SNAKE_CASE__ =			False




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().setUp()
       a		:List[str]									=			CanineTokenizer()
       tokenizer.save_pretrained(self.tmpdirname       )




 @cached_property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return CanineTokenizer.from_pretrained('''google/canine-s'''       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       a		:Dict									=			self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **_lowerCamelCase       )
       a		:List[Any]									=			1024
       return tokenizer




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.canine_tokenizer
       a		:Tuple									=			['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
       # fmt: off
       a		:Any									=			[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
       # fmt: on
       a		:Dict									=			tokenizer(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors='''pt'''       )
       self.assertIsInstance(_lowerCamelCase   ,    _lowerCamelCase       )

       a		:str									=			list(batch.input_ids.numpy()[0]       )

       self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

       self.assertEqual((2, 39)   ,    batch.input_ids.shape       )
       self.assertEqual((2, 39)   ,    batch.attention_mask.shape       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.canine_tokenizer
       a		:Optional[int]									=			['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
       a		:Dict									=			tokenizer(_lowerCamelCase   ,    padding=_lowerCamelCase   ,    return_tensors='''pt'''       )
       # check if input_ids, attention_mask and token_type_ids are returned
       self.assertIn('''input_ids'''   ,    _lowerCamelCase       )
       self.assertIn('''attention_mask'''   ,    _lowerCamelCase       )
       self.assertIn('''token_type_ids'''   ,    _lowerCamelCase       )




 @require_torch
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.canine_tokenizer
       a		:str									=			[
           '''What\'s the weater?''',
           '''It\'s about 25 degrees.''',
       ]
       a		:List[str]									=			tokenizer(
           text_target=_lowerCamelCase   ,    max_length=32   ,    padding='''max_length'''   ,    truncation=_lowerCamelCase   ,    return_tensors='''pt'''       )
       self.assertEqual(32   ,    targets['''input_ids'''].shape[1]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # safety check on max_len default value so we are sure the test works
       a		:Tuple									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   self.assertNotEqual(tokenizer.model_max_length   ,    42       )

        # Now let's start the test
       a		:Tuple									=			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
                   a		:List[str]									=			tempfile.mkdtemp()

                   a		:Any									=			''' He is very happy, UNwant\u00E9d,running'''
                   a		:Dict									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   tokenizer.save_pretrained(_lowerCamelCase       )

                   a		:Optional[Any]									=			tokenizer.__class__.from_pretrained(_lowerCamelCase       )
                   a		:str									=			after_tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

                   shutil.rmtree(_lowerCamelCase       )

       a		:str									=			self.get_tokenizers(model_max_length=42       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   a		:Any									=			tempfile.mkdtemp()

                   a		:Optional[Any]									=			''' He is very happy, UNwant\u00E9d,running'''

                   a		:Optional[int]									=			tokenizer.additional_special_tokens

                   # We can add a new special token for Canine as follows:
                   a		:str									=			chr(0Xe_0_0_7       )
                   additional_special_tokens.append(_lowerCamelCase       )
                   tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}       )
                   a		:Tuple									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   tokenizer.save_pretrained(_lowerCamelCase       )

                   a		:int									=			tokenizer.__class__.from_pretrained(_lowerCamelCase       )
                   a		:Dict									=			after_tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )
                   self.assertIn(_lowerCamelCase   ,    after_tokenizer.additional_special_tokens       )
                   self.assertEqual(after_tokenizer.model_max_length   ,    42       )

                   a		:str									=			tokenizer.__class__.from_pretrained(_lowerCamelCase   ,    model_max_length=43       )
                   self.assertEqual(tokenizer.model_max_length   ,    43       )

                   shutil.rmtree(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a				,							a		:Tuple									=			self.get_clean_sequence(_lowerCamelCase       )

                   # a special token for Canine can be defined as follows:
                   a		:Tuple									=			0Xe_0_0_5
                   a		:Optional[int]									=			chr(_lowerCamelCase       )

                   tokenizer.add_special_tokens({'''cls_token''': special_token}       )
                   a		:Optional[int]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )

                   a		:List[Any]									=			tokenizer.decode(ids + encoded_special_token   ,    clean_up_tokenization_spaces=_lowerCamelCase       )
                   a		:List[str]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )

                   a		:Tuple									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   a		:Optional[Any]									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   self.assertEqual(_lowerCamelCase   ,    input_encoded + special_token_id       )

                   a		:Any									=			tokenizer.decode(_lowerCamelCase   ,    skip_special_tokens=_lowerCamelCase       )
                   self.assertTrue(special_token not in decoded       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:int									=			chr(0Xe_0_0_5       )
                   a		:str									=			chr(0Xe_0_0_6       )

                   # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                   tokenizer.add_tokens([SPECIAL_TOKEN_1]   ,    special_tokens=_lowerCamelCase       )
                   # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                   # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                   tokenizer.add_special_tokens({'''additional_special_tokens''': [SPECIAL_TOKEN_2]}       )

                   a		:Optional[int]									=			tokenizer.tokenize(_lowerCamelCase       )
                   a		:Optional[Any]									=			tokenizer.tokenize(_lowerCamelCase       )

                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )
                   self.assertEqual(len(_lowerCamelCase       )   ,    1       )
                   self.assertEqual(token_a[0]   ,    _lowerCamelCase       )
                   self.assertEqual(token_a[0]   ,    _lowerCamelCase       )




 @require_tokenizers
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   # a special token for Canine can be defined as follows:
                   a		:Optional[int]									=			0Xe_0_0_6
                   a		:List[str]									=			chr(_lowerCamelCase       )

                   a		:Optional[int]									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase       )
                   tokenizer.add_special_tokens({'''additional_special_tokens''': [new_token]}       )

                   with tempfile.TemporaryDirectory() as tmp_dir_name:
                         tokenizer.save_pretrained(_lowerCamelCase       )
                         tokenizer.from_pretrained(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			[]
       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:
                         a		:Optional[Any]									=			json.load(_lowerCamelCase       )

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

                   # a special token for Canine can be defined as follows:
                   a		:int									=			0Xe_0_0_6
                   a		:Optional[Any]									=			chr(_lowerCamelCase       )

                   a		:Union[str, Any]									=			[new_token_a]
                   a		:Optional[int]									=			[new_token_a]

                   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
                   a		:str									=			tokenizer_class.from_pretrained(_lowerCamelCase   ,    extra_ids=0       )
                   self.assertIn(_lowerCamelCase   ,    tokenizer_without_change_in_init.additional_special_tokens       )
                   # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                   self.assertEqual(
                       [new_token_a]   ,    tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]       )       )   ,    )

                   a		:Optional[int]									=			0Xe_0_0_7
                   a		:Any									=			chr(_lowerCamelCase       )
                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   a		:Tuple									=			[AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase       )]
                   a		:Optional[Any]									=			tokenizer_class.from_pretrained(
                       _lowerCamelCase   ,    additional_special_tokens=_lowerCamelCase   ,    extra_ids=0       )

                   self.assertIn(_lowerCamelCase   ,    tokenizer.additional_special_tokens       )
                   # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                   self.assertEqual(
                       [new_token_a]   ,    tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]       )       )       )




 @require_tokenizers
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.get_tokenizers(do_lower_case=_lowerCamelCase       )
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:List[str]									=			'''hello world'''
                   if self.space_between_special_tokens:
                         a		:Optional[Any]									=			'''[CLS] hello world [SEP]'''
                   else:
                         a		:Tuple									=			input
                   a		:Any									=			tokenizer.encode(_lowerCamelCase   ,    add_special_tokens=_lowerCamelCase       )
                   a		:List[Any]									=			tokenizer.decode(_lowerCamelCase   ,    spaces_between_special_tokens=self.space_between_special_tokens       )
                   self.assertIn(_lowerCamelCase   ,    [output, output.lower()]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                   a		:Any									=			[
                       '''bos_token''',
                       '''eos_token''',
                       '''unk_token''',
                       '''sep_token''',
                       '''pad_token''',
                       '''cls_token''',
                       '''mask_token''',
                   ]

       a		:str									=			'''a'''
       a		:List[Any]									=			ord(_lowerCamelCase       )

       for attr in attributes_list:
             setattr(_lowerCamelCase   ,    attr + '''_id'''   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    _lowerCamelCase       )   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    attr + '''_id'''       )   ,    _lowerCamelCase       )

             setattr(_lowerCamelCase   ,    attr + '''_id'''   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    _lowerCamelCase       )   ,    _lowerCamelCase       )
             self.assertEqual(getattr(_lowerCamelCase   ,    attr + '''_id'''       )   ,    _lowerCamelCase       )

       setattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''   ,    []       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens'''       )   ,    []       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''       )   ,    []       )

       a		:List[Any]									=			0Xe_0_0_6
       a		:str									=			chr(_lowerCamelCase       )
       setattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''   ,    [additional_special_token_id]       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens'''       )   ,    [additional_special_token]       )
       self.assertListEqual(getattr(_lowerCamelCase   ,    '''additional_special_tokens_ids'''       )   ,    [additional_special_token_id]       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       pass





