




import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import BertGenerationDecoder, BertGenerationEncoder







class __snake_case			:







   def __init__(      self		:						Optional[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						List[Any]=13					,						_snake_case		:						Union[str, Any]=7					,						_snake_case		:						Tuple=True					,						_snake_case		:						List[Any]=True					,						_snake_case		:						Tuple=99					,						_snake_case		:						Optional[Any]=32					,						_snake_case		:						List[str]=5					,						_snake_case		:						Dict=4					,						_snake_case		:						List[Any]=37					,						_snake_case		:						Tuple="gelu"					,						_snake_case		:						Any=0.1					,						_snake_case		:						Optional[Any]=0.1					,						_snake_case		:						Dict=50					,						_snake_case		:						Dict=0.0_2					,						_snake_case		:						Dict=True					,						_snake_case		:						List[str]=None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							parent
        UpperCAmelCase_          =							batch_size
        UpperCAmelCase_          =							seq_length
        UpperCAmelCase_          =							is_training
        UpperCAmelCase_          =							use_input_mask
        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							hidden_dropout_prob
        UpperCAmelCase_          =							attention_probs_dropout_prob
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							use_labels
        UpperCAmelCase_          =							scope







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size)

        UpperCAmelCase_          =							None
        if self.use_input_mask:
             UpperCAmelCase_          =							random_attention_mask([self.batch_size, self.seq_length])

        if self.use_labels:
             UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size)

        UpperCAmelCase_          =							self.get_config()

        return config, input_ids, input_mask, token_labels







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        return BertGenerationConfig(
            vocab_size=self.vocab_size					,						hidden_size=self.hidden_size					,						num_hidden_layers=self.num_hidden_layers					,						num_attention_heads=self.num_attention_heads					,						intermediate_size=self.intermediate_size					,						hidden_act=self.hidden_act					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						max_position_embeddings=self.max_position_embeddings					,						is_decoder=_snake_case					,						initializer_range=self.initializer_range					,						)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							self.prepare_config_and_inputs()

        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						vocab_size=2)

        return (
            config,
            input_ids,
            input_mask,
            token_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						int					,						_snake_case		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						Optional[int]					,						**_snake_case		:						str					,						):

        """simple docstring"""


        UpperCAmelCase_          =							BertGenerationEncoder(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)
        UpperCAmelCase_          =							model(_snake_case)
        self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Tuple					,						_snake_case		:						List[Any]					,						_snake_case		:						Optional[int]					,						_snake_case		:						Dict					,						_snake_case		:						Tuple					,						_snake_case		:						Any					,						**_snake_case		:						List[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							BertGenerationEncoder(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						encoder_hidden_states=_snake_case					,						encoder_attention_mask=_snake_case					,						)
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						encoder_hidden_states=_snake_case					,						)
        self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						str					,						_snake_case		:						Dict					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						int					,						**_snake_case		:						Optional[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							BertGenerationDecoder(config=_snake_case).to(_snake_case).eval()

        # first forward pass
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						encoder_hidden_states=_snake_case					,						encoder_attention_mask=_snake_case					,						use_cache=_snake_case					,						)
        UpperCAmelCase_          =							outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        UpperCAmelCase_          =							ids_tensor((self.batch_size, 3)					,						config.vocab_size)
        UpperCAmelCase_          =							ids_tensor((self.batch_size, 3)					,						vocab_size=2)

        # append to next input_ids and
        UpperCAmelCase_          =							torch.cat([input_ids, next_tokens]					,						dim=-1)
        UpperCAmelCase_          =							torch.cat([input_mask, next_mask]					,						dim=-1)

        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						encoder_hidden_states=_snake_case					,						encoder_attention_mask=_snake_case					,						output_hidden_states=_snake_case					,						)['''hidden_states'''][0]
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						encoder_hidden_states=_snake_case					,						encoder_attention_mask=_snake_case					,						past_key_values=_snake_case					,						output_hidden_states=_snake_case					,						)['''hidden_states'''][0]

        # select random slice
        UpperCAmelCase_          =							ids_tensor((1,)					,						output_from_past.shape[-1]).item()
        UpperCAmelCase_          =							output_from_no_past[:, -3:, random_slice_idx].detach()
        UpperCAmelCase_          =							output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(_snake_case					,						_snake_case					,						atol=1e-3))







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						List[str]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Any					,						*_snake_case		:						Optional[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							BertGenerationDecoder(_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size))







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self.prepare_config_and_inputs()
        UpperCAmelCase_          =							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict







@require_torch
class __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    int          =  (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
   UpperCAmelCase__  :    int          =  (BertGenerationDecoder,) if is_torch_available() else ()
   UpperCAmelCase__  :    Optional[int]          =  (
       {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
       if is_torch_available()
       else {}
   )







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							BertGenerationEncoderTester(self)
        UpperCAmelCase_          =							ConfigTester(self					,						config_class=_snake_case					,						hidden_size=37)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        self.config_tester.run_common_tests()







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_snake_case)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        UpperCAmelCase_          =							'''bert'''
        self.model_tester.create_and_check_model(_snake_case					,						_snake_case					,						_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							self.model_tester.prepare_config_and_inputs_for_decoder()

        UpperCAmelCase_          =							None

        self.model_tester.create_and_check_model_as_decoder(
            _snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_for_causal_lm(*_snake_case)







   @slow
   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
        self.assertIsNotNone(_snake_case)







@require_torch
class __snake_case			(			unittest.TestCase		):







   @slow
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
        UpperCAmelCase_          =							torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
        with torch.no_grad():
             UpperCAmelCase_          =							model(_snake_case)[0]
        UpperCAmelCase_          =							torch.Size([1, 8, 1024])
        self.assertEqual(output.shape					,						_snake_case)
        UpperCAmelCase_          =							torch.tensor(
            [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]])
        self.assertTrue(torch.allclose(output[:, :3, :3]					,						_snake_case					,						atol=1e-4))







@require_torch
class __snake_case			(			unittest.TestCase		):







   @slow
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
        UpperCAmelCase_          =							torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]])
        with torch.no_grad():
             UpperCAmelCase_          =							model(_snake_case)[0]
        UpperCAmelCase_          =							torch.Size([1, 8, 50358])
        self.assertEqual(output.shape					,						_snake_case)
        UpperCAmelCase_          =							torch.tensor(
            [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]])
        self.assertTrue(torch.allclose(output[:, :3, :3]					,						_snake_case					,						atol=1e-4))












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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    snake_case_ : Optional[Any]    		=       "pt"
elif is_tf_available():
    snake_case_ : Union[str, Any]    		=       "tf"
else:
    snake_case_ : str    		=       "jax"







class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  ByTaTokenizer
   UpperCAmelCase__  :    int          =  False







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().setUp()
        UpperCAmelCase_          =							ByTaTokenizer()
        tokenizer.save_pretrained(self.tmpdirname)







   @cached_property
   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        return ByTaTokenizer.from_pretrained('''google/byt5-small''')







   def lowerCamelCase						(      self		:						List[str]					,						**_snake_case		:						Union[str, Any]):

        """simple docstring"""


        return self.tokenizer_class.from_pretrained(self.tmpdirname					,						**_snake_case)







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						int					,						_snake_case		:						Tuple=False					,						_snake_case		:						Dict=20					,						_snake_case		:						Optional[Any]=5):

        """simple docstring"""


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

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

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







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''])
        UpperCAmelCase_          =							tokenizer(['''hi''', '''I went to the gym''', ''''''])
        self.assertListEqual(batch_with_eos_added['''input_ids''']					,						batch_without_eos_added['''input_ids'''])







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							'''Unicode €.'''
        UpperCAmelCase_          =							tokenizer(_snake_case)
        UpperCAmelCase_          =							[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
        self.assertEqual(encoded['''input_ids''']					,						_snake_case)

        # decoding
        UpperCAmelCase_          =							tokenizer.decode(_snake_case)
        self.assertEqual(_snake_case					,						'''Unicode €.</s>''')

        UpperCAmelCase_          =							tokenizer('''e è é ê ë''')
        UpperCAmelCase_          =							[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
        self.assertEqual(encoded['''input_ids''']					,						_snake_case)
        # decoding
        UpperCAmelCase_          =							tokenizer.decode(_snake_case)
        self.assertEqual(_snake_case					,						'''e è é ê ë</s>''')

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))					,						'''e è é ê ë</s>''')







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        # fmt: off
        UpperCAmelCase_          =							[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
        # fmt: on
        UpperCAmelCase_          =							tokenizer(_snake_case					,						padding=_snake_case					,						return_tensors=_snake_case)
        self.assertIsInstance(_snake_case					,						_snake_case)

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

        self.assertListEqual(_snake_case					,						_snake_case)

        self.assertEqual((2, 37)					,						batch.input_ids.shape)
        self.assertEqual((2, 37)					,						batch.attention_mask.shape)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        UpperCAmelCase_          =							tokenizer(
            text_target=_snake_case					,						max_length=32					,						padding='''max_length'''					,						truncation=_snake_case					,						return_tensors=_snake_case)
        self.assertEqual(32					,						targets['''input_ids'''].shape[1])







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							['''A long paragraph for summarization. </s>''']
        UpperCAmelCase_          =							['''Summary of the text. </s>''']
        # fmt: off
        UpperCAmelCase_          =							[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
        UpperCAmelCase_          =							[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
        # fmt: on

        UpperCAmelCase_          =							tokenizer(_snake_case					,						text_target=_snake_case)

        self.assertEqual(_snake_case					,						batch['''input_ids'''][0])
        self.assertEqual(_snake_case					,						batch['''labels'''][0])







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							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
        UpperCAmelCase_          =							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
                  UpperCAmelCase_          =							tempfile.mkdtemp()

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

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

                  shutil.rmtree(_snake_case)

        UpperCAmelCase_          =							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
                  UpperCAmelCase_          =							tempfile.mkdtemp()

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

                  UpperCAmelCase_          =							tokenizer.__class__.from_pretrained(_snake_case)
                  UpperCAmelCase_          =							after_tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
                  self.assertListEqual(_snake_case					,						_snake_case)
                  self.assertIn('''new_additional_special_token'''					,						after_tokenizer.additional_special_tokens)
                  self.assertEqual(after_tokenizer.model_max_length					,						42)

                  UpperCAmelCase_          =							tokenizer.__class__.from_pretrained(_snake_case					,						model_max_length=43)
                  self.assertEqual(tokenizer.model_max_length					,						43)

                  shutil.rmtree(_snake_case)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        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(_snake_case)

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

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

                  UpperCAmelCase_          =							[F"""<extra_id_{i}>""" for i in range(125)]

                  UpperCAmelCase_          =							added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]
                  UpperCAmelCase_          =							added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]

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

                  # 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
                  UpperCAmelCase_          =							tokenizer_class.from_pretrained(
                      _snake_case					,						)
                  self.assertIn(
                      '''an_additional_special_token'''					,						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(
                      ['''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
                  UpperCAmelCase_          =							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''					,						lstrip=_snake_case)]
                  UpperCAmelCase_          =							tokenizer_class.from_pretrained(
                      _snake_case					,						additional_special_tokens=_snake_case					,						)

                  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 lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        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(_snake_case)

                  UpperCAmelCase_          =							tokenizer_class.from_pretrained(_snake_case)

                  self.assertTrue(tokenizer.decode([255]) == '''''')







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_tokenizers(fast=_snake_case					,						do_lower_case=_snake_case)
        for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""):
                  UpperCAmelCase_          =							['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                  UpperCAmelCase_          =							tokenizer.convert_tokens_to_string(_snake_case)

                  self.assertIsInstance(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


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

                  UpperCAmelCase_          =							0
                  UpperCAmelCase_          =							tokenizer.convert_ids_to_tokens(
                      _snake_case					,						skip_special_tokens=_snake_case)

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

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

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

                  setattr(_snake_case					,						'''additional_special_tokens_ids'''					,						[token_id_to_test_setters])
                  self.assertListEqual(getattr(_snake_case					,						'''additional_special_tokens''')					,						[token_to_test_setters])
                  self.assertListEqual(getattr(_snake_case					,						'''additional_special_tokens_ids''')					,						[token_id_to_test_setters])






