"""simple docstring"""




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
     from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor
class 						__A							:






     def __init__(		self				,     a__				,     a__=13				,     a__=30				,     a__=2				,     a__=3				,     a__=True				,     a__=True				,     a__=32				,     a__=5				,     a__=4				,     a__=37				,     a__="gelu"				,     a__=0.1				,     a__=0.1				,     a__=10				,     a__=0.0_2				,     a__=None				,     a__=2				,     ):
       _lowerCAmelCase						:       List[Any]  			=     parent
       _lowerCAmelCase						:       int  			=     batch_size
       _lowerCAmelCase						:       Tuple  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       int  			=     num_channels
       _lowerCAmelCase						:       List[str]  			=     is_training
       _lowerCAmelCase						:       Tuple  			=     use_labels
       _lowerCAmelCase						:       Any  			=     hidden_size
       _lowerCAmelCase						:       List[Any]  			=     num_hidden_layers
       _lowerCAmelCase						:       List[str]  			=     num_attention_heads
       _lowerCAmelCase						:       Optional[Any]  			=     intermediate_size
       _lowerCAmelCase						:       Any  			=     hidden_act
       _lowerCAmelCase						:       Tuple  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Optional[Any]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       Any  			=     type_sequence_label_size
       _lowerCAmelCase						:       int  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     scope
       _lowerCAmelCase						:       Union[str, Any]  			=     encoder_stride

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       _lowerCAmelCase						:       int  			=     (image_size // patch_size) ** 2
       _lowerCAmelCase						:       Optional[Any]  			=     num_patches + 1






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       _lowerCAmelCase						:       Optional[int]  			=     None
       if self.use_labels:
         _lowerCAmelCase						:       str  			=     ids_tensor([self.batch_size]				,     self.type_sequence_label_size  )

       _lowerCAmelCase						:       Dict  			=     self.get_config()

       return config, pixel_values, labels






     def     __A  (		self  ):
       return ViTConfig(
           image_size=self.image_size				,     patch_size=self.patch_size				,     num_channels=self.num_channels				,     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				,     is_decoder=a__				,     initializer_range=self.initializer_range				,     encoder_stride=self.encoder_stride				,     )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     ViTModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       Optional[int]  			=     model(a__  )
       self.parent.assertEqual(result.last_hidden_state.shape				,     (self.batch_size, self.seq_length, self.hidden_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Any  			=     ViTForMaskedImageModeling(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       List[str]  			=     model(a__  )
       self.parent.assertEqual(
           result.reconstruction.shape				,     (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

       # test greyscale images
       _lowerCAmelCase						:       Union[str, Any]  			=     1
       _lowerCAmelCase						:       Union[str, Any]  			=     ViTForMaskedImageModeling(a__  )
       model.to(a__  )
       model.eval()

       _lowerCAmelCase						:       Any  			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       _lowerCAmelCase						:       Dict  			=     model(a__  )
       self.parent.assertEqual(result.reconstruction.shape				,     (self.batch_size, 1, self.image_size, self.image_size)  )






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.type_sequence_label_size
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:       int  			=     model(a__				,     labels=a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)  )

       # test greyscale images
       _lowerCAmelCase						:       int  			=     1
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification(a__  )
       model.to(a__  )
       model.eval()

       _lowerCAmelCase						:       List[str]  			=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
       _lowerCAmelCase						:       Tuple  			=     model(a__  )
       self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	(
           _lowerCAmelCase
       )			,	
       )						:       List[str]  			=     config_and_inputs
       _lowerCAmelCase						:       Optional[Any]  			=     {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			int							=		(
         (
             ViTModel,
             ViTForImageClassification,
             ViTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			Tuple							=		(
         {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
         if is_torch_available()
         else {}
     )
     _UpperCamelCase							:			Dict							=		True

     _UpperCamelCase							:			str							=		False
     _UpperCamelCase							:			Optional[Any]							=		False
     _UpperCamelCase							:			List[str]							=		False






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     ViTModelTester(self  )
       _lowerCAmelCase						:       Any  			=     ConfigTester(self				,     config_class=a__				,     has_text_modality=a__				,     hidden_size=37  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     @unittest.skip(reason="""ViT does not use inputs_embeds"""  )
     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       List[str]  			=     model_class(a__  )
         self.assertIsInstance(model.get_input_embeddings()				,     (nn.Module)  )
         _lowerCAmelCase						:       int  			=     model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(a__				,     nn.Linear  )  )






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       Optional[Any]  			=     model_class(a__  )
         _lowerCAmelCase						:       str  			=     inspect.signature(model.forward  )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCAmelCase						:       List[str]  			=     [*signature.parameters.keys()]

         _lowerCAmelCase						:       List[str]  			=     ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__  )






     @slow
     def     __A  (		self  ):
       for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:       List[str]  			=     ViTModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE     (			)       ->      List[str]:
  _lowerCAmelCase						:       Union[str, Any]  			=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
  return image






@require_torch
@require_vision
class 						__A							( unittest.TestCase		):






     @cached_property
     def     __A  (		self  ):
       return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224"""  ) if is_vision_available() else None






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""  ).to(a__  )

       _lowerCAmelCase						:       str  			=     self.default_image_processor
       _lowerCAmelCase						:       str  			=     prepare_img()
       _lowerCAmelCase						:       Tuple  			=     image_processor(images=a__				,     return_tensors="""pt"""  ).to(a__  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[int]  			=     model(**a__  )

       # verify the logits
       _lowerCAmelCase						:       str  			=     torch.Size((1, 1000)  )
       self.assertEqual(outputs.logits.shape				,     a__  )

       _lowerCAmelCase						:       List[Any]  			=     torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]  ).to(a__  )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]				,     a__				,     atol=1e-4  )  )






     @slow
     def     __A  (		self  ):
       # ViT models have an `interpolate_pos_encoding` argument in their forward method,
       # allowing to interpolate the pre-trained position embeddings in order to use
       # the model on higher resolutions. The DINO model by Facebook AI leverages this
       # to visualize self-attention on higher resolution images.
       _lowerCAmelCase						:       Optional[Any]  			=     ViTModel.from_pretrained("""facebook/dino-vits8"""  ).to(a__  )

       _lowerCAmelCase						:       str  			=     ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""				,     size=480  )
       _lowerCAmelCase						:       Any  			=     prepare_img()
       _lowerCAmelCase						:       Dict  			=     image_processor(images=a__				,     return_tensors="""pt"""  )
       _lowerCAmelCase						:       Optional[Any]  			=     inputs.pixel_values.to(a__  )

       # forward pass
       with torch.no_grad():
         _lowerCAmelCase						:       str  			=     model(a__				,     interpolate_pos_encoding=a__  )

       # verify the logits
       _lowerCAmelCase						:       str  			=     torch.Size((1, 3601, 384)  )
       self.assertEqual(outputs.last_hidden_state.shape				,     a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     torch.tensor(
           [[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]]  ).to(a__  )

       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]				,     a__				,     atol=1e-4  )  )






     @slow
     @require_accelerate
     @require_torch_gpu
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     ViTModel.from_pretrained("""facebook/dino-vits8"""				,     torch_dtype=torch.floataa				,     device_map="""auto"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.default_image_processor

       _lowerCAmelCase						:       Dict  			=     prepare_img()
       _lowerCAmelCase						:       List[str]  			=     image_processor(images=a__				,     return_tensors="""pt"""  )
       _lowerCAmelCase						:       Any  			=     inputs.pixel_values.to(a__  )

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
         _lowerCAmelCase						:       Optional[int]  			=     model(a__  )










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])






