






'''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 _lowercase	:




							def __init__(							self:				List[Any]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Any=13			,							UpperCamelCase__:				Optional[Any]=30			,							UpperCamelCase__:				Dict=2			,							UpperCamelCase__:				Tuple=3			,							UpperCamelCase__:				Optional[Any]=True			,							UpperCamelCase__:				Optional[Any]=True			,							UpperCamelCase__:				Optional[Any]=32			,							UpperCamelCase__:				Optional[int]=5			,							UpperCamelCase__:				Dict=4			,							UpperCamelCase__:				Optional[Any]=37			,							UpperCamelCase__:				List[str]="gelu"			,							UpperCamelCase__:				str=0.1			,							UpperCamelCase__:				Dict=0.1			,							UpperCamelCase__:				Dict=10			,							UpperCamelCase__:				Tuple=0.02			,							UpperCamelCase__:				Optional[int]=None			,							UpperCamelCase__:				int=2			,							):
														lowerCamelCase__					:	Any         =       parent
														lowerCamelCase__					:	List[str]         =       batch_size
														lowerCamelCase__					:	int         =       image_size
														lowerCamelCase__					:	str         =       patch_size
														lowerCamelCase__					:	Optional[Any]         =       num_channels
														lowerCamelCase__					:	int         =       is_training
														lowerCamelCase__					:	Union[str, Any]         =       use_labels
														lowerCamelCase__					:	Dict         =       hidden_size
														lowerCamelCase__					:	str         =       num_hidden_layers
														lowerCamelCase__					:	List[Any]         =       num_attention_heads
														lowerCamelCase__					:	int         =       intermediate_size
														lowerCamelCase__					:	Union[str, Any]         =       hidden_act
														lowerCamelCase__					:	Optional[Any]         =       hidden_dropout_prob
														lowerCamelCase__					:	List[Any]         =       attention_probs_dropout_prob
														lowerCamelCase__					:	Tuple         =       type_sequence_label_size
														lowerCamelCase__					:	Any         =       initializer_range
														lowerCamelCase__					:	int         =       scope
														lowerCamelCase__					:	Optional[int]         =       encoder_stride

														# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
														lowerCamelCase__					:	List[str]         =       (image_size // patch_size) ** 2
														lowerCamelCase__					:	Dict         =       num_patches + 1




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Optional[int]         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

														lowerCamelCase__					:	Tuple         =       self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_				(							self:				List[str]     ):
														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=UpperCamelCase__			,							initializer_range=self.initializer_range			,							encoder_stride=self.encoder_stride			,							)




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				int			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Union[str, Any]         =       ViTModel(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	Dict         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)     )




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Union[str, Any]         =       ViTForMaskedImageModeling(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__     )
														self.parent.assertEqual(
														    result.reconstruction.shape			,							(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

														# test greyscale images
														lowerCamelCase__					:	Dict         =       1
														lowerCamelCase__					:	Optional[int]         =       ViTForMaskedImageModeling(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()

														lowerCamelCase__					:	Tuple         =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCamelCase__					:	Tuple         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.reconstruction.shape			,							(self.batch_size, 1, self.image_size, self.image_size)     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Union[str, Any]     ):
														lowerCamelCase__					:	List[Any]         =       self.type_sequence_label_size
														lowerCamelCase__					:	Tuple         =       ViTForImageClassification(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	Optional[int]         =       model(UpperCamelCase__			,							labels=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)     )

														# test greyscale images
														lowerCamelCase__					:	Dict         =       1
														lowerCamelCase__					:	Optional[Any]         =       ViTForImageClassification(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()

														lowerCamelCase__					:	Union[str, Any]         =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
														lowerCamelCase__					:	str         =       model(UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)     )




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	int         =       self.prepare_config_and_inputs()
														(
														    (
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		(
														    lowerCamelCase__
														)  ,		
														)					:	str         =       config_and_inputs
														lowerCamelCase__					:	str         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (
							    (
							        ViTModel,
							        ViTForImageClassification,
							        ViTForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							a				      =       (
							    {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
							    if is_torch_available()
							    else {}
							)
							a				      =       True

							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Any         =       ViTModelTester(self     )
														lowerCamelCase__					:	Optional[int]         =       ConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__			,							hidden_size=37     )




							def 		lowerCamelCase_				(							self:				Any     ):
														self.config_tester.run_common_tests()




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




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Optional[Any]         =       model_class(UpperCamelCase__     )
																					self.assertIsInstance(model.get_input_embeddings()			,							(nn.Module)     )
																					lowerCamelCase__					:	int         =       model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(UpperCamelCase__			,							nn.Linear     )     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Union[str, Any]         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	List[str]         =       inspect.signature(model.forward     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	Tuple         =       [*signature.parameters.keys()]

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




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	str         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Any         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	List[str]         =       ViTModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Optional[int]:
							lowerCamelCase__					:	int         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



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




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




							@slow
							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	List[str]         =       ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224"""     ).to(UpperCamelCase__     )

														lowerCamelCase__					:	Optional[Any]         =       self.default_image_processor
														lowerCamelCase__					:	Any         =       prepare_img()
														lowerCamelCase__					:	str         =       image_processor(images=UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )

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

														# verify the logits
														lowerCamelCase__					:	Union[str, Any]         =       torch.Size((1, 1_000)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       torch.tensor([-0.2_744, 0.8_215, -0.0_836]     ).to(UpperCamelCase__     )

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




							@slow
							def 		lowerCamelCase_				(							self:				Dict     ):
														# 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__					:	List[Any]         =       ViTModel.from_pretrained("""facebook/dino-vits8"""     ).to(UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       ViTImageProcessor.from_pretrained("""facebook/dino-vits8"""			,							size=480     )
														lowerCamelCase__					:	Optional[int]         =       prepare_img()
														lowerCamelCase__					:	str         =       image_processor(images=UpperCamelCase__			,							return_tensors="""pt"""     )
														lowerCamelCase__					:	Optional[int]         =       inputs.pixel_values.to(UpperCamelCase__     )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							interpolate_pos_encoding=UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	Optional[Any]         =       torch.Size((1, 3_601, 384)     )
														self.assertEqual(outputs.last_hidden_state.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	int         =       torch.tensor(
														    [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]     ).to(UpperCamelCase__     )

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




							@slow
							@require_accelerate
							@require_torch_gpu
							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	str         =       ViTModel.from_pretrained("""facebook/dino-vits8"""			,							torch_dtype=torch.floataa			,							device_map="""auto"""     )
														lowerCamelCase__					:	int         =       self.default_image_processor

														lowerCamelCase__					:	str         =       prepare_img()
														lowerCamelCase__					:	Dict         =       image_processor(images=UpperCamelCase__			,							return_tensors="""pt"""     )
														lowerCamelCase__					:	Dict         =       inputs.pixel_values.to(UpperCamelCase__     )

														# forward pass to make sure inference works in fp16
														with torch.no_grad():
																					lowerCamelCase__					:	Any         =       model(UpperCamelCase__     )









'''simple docstring'''







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():
			_A	:			List[Any]         ='''pt'''
elif is_tf_available():
			_A	:			Any         ='''tf'''
else:
			_A	:			List[str]         ='''jax'''

class _lowercase	(				_lowercase				,      unittest.TestCase		):
							a				      =       ByTaTokenizer
							a				      =       False




							def 		lowerCamelCase_				(							self:				str     ):
														super().setUp()
														lowerCamelCase__					:	str         =       ByTaTokenizer()
														tokenizer.save_pretrained(self.tmpdirname     )




							@cached_property
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														return ByTaTokenizer.from_pretrained("""google/byt5-small"""     )




							def 		lowerCamelCase_				(							self:				Any			,							**UpperCamelCase__:				Tuple     ):
														return self.tokenizer_class.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Any=False			,							UpperCamelCase__:				Union[str, Any]=20			,							UpperCamelCase__:				Optional[int]=5     ):
														# XXX The default common tokenizer tests assume that every ID is decodable on its own.
														# This assumption is invalid for ByT5 because single bytes might not be
														# valid utf-8 (byte 128 for instance).
														# Here we're overriding the smallest possible method to provide
														# a clean sequence without making the same assumption.

														lowerCamelCase__					:	List[str]         =       []
														for i in range(len(UpperCamelCase__     )     ):
																					try:
																												lowerCamelCase__					:	Tuple         =       tokenizer.decode([i]			,							clean_up_tokenization_spaces=UpperCamelCase__     )
																					except UnicodeDecodeError:
																												pass
																					toks.append((i, tok)     )

														lowerCamelCase__					:	Union[str, Any]         =       list(filter(lambda UpperCamelCase__     : re.match(R"""^[ a-zA-Z]+$"""			,							t[1]     )			,							UpperCamelCase__     )     )
														lowerCamelCase__					:	Tuple         =       list(filter(lambda UpperCamelCase__     : [t[0]] == tokenizer.encode(t[1]			,							add_special_tokens=UpperCamelCase__     )			,							UpperCamelCase__     )     )
														if max_length is not None and len(UpperCamelCase__     ) > max_length:
																					lowerCamelCase__					:	Dict         =       toks[:max_length]
														if min_length is not None and len(UpperCamelCase__     ) < min_length and len(UpperCamelCase__     ) > 0:
																					while len(UpperCamelCase__     ) < min_length:
																												lowerCamelCase__					:	Optional[Any]         =       toks + toks
        # toks_str = [t[1] for t in toks]
														lowerCamelCase__					:	Union[str, Any]         =       [t[0] for t in toks]

														# Ensure consistency
														lowerCamelCase__					:	Union[str, Any]         =       tokenizer.decode(UpperCamelCase__			,							clean_up_tokenization_spaces=UpperCamelCase__     )
														if " " not in output_txt and len(UpperCamelCase__     ) > 1:
																					lowerCamelCase__					:	Optional[int]         =       (
																					    tokenizer.decode([toks_ids[0]]			,							clean_up_tokenization_spaces=UpperCamelCase__     )
																					    + """ """
																					    + tokenizer.decode(toks_ids[1:]			,							clean_up_tokenization_spaces=UpperCamelCase__     )
																					)
														if with_prefix_space:
																					lowerCamelCase__					:	str         =       """ """ + output_txt
														lowerCamelCase__					:	List[Any]         =       tokenizer.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     )
														return output_txt, output_ids




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	str         =       self.ta_base_tokenizer
														lowerCamelCase__					:	Union[str, Any]         =       tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]     )
														lowerCamelCase__					:	Optional[int]         =       tokenizer(["""hi""", """I went to the gym""", """"""]     )
														self.assertListEqual(batch_with_eos_added["""input_ids"""]			,							batch_without_eos_added["""input_ids"""]     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Optional[Any]         =       self.ta_base_tokenizer
														lowerCamelCase__					:	Dict         =       """Unicode €."""
														lowerCamelCase__					:	List[Any]         =       tokenizer(UpperCamelCase__     )
														lowerCamelCase__					:	List[str]         =       [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
														self.assertEqual(encoded["""input_ids"""]			,							UpperCamelCase__     )

														# decoding
														lowerCamelCase__					:	Tuple         =       tokenizer.decode(UpperCamelCase__     )
														self.assertEqual(UpperCamelCase__			,							"""Unicode €.</s>"""     )

														lowerCamelCase__					:	List[Any]         =       tokenizer("""e è é ê ë"""     )
														lowerCamelCase__					:	List[Any]         =       [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
														self.assertEqual(encoded["""input_ids"""]			,							UpperCamelCase__     )
														# decoding
														lowerCamelCase__					:	str         =       tokenizer.decode(UpperCamelCase__     )
														self.assertEqual(UpperCamelCase__			,							"""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     ):
														lowerCamelCase__					:	int         =       self.ta_base_tokenizer
														lowerCamelCase__					:	Tuple         =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														# fmt: off
														lowerCamelCase__					:	List[str]         =       [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
														lowerCamelCase__					:	int         =       tokenizer(UpperCamelCase__			,							padding=UpperCamelCase__			,							return_tensors=UpperCamelCase__     )
														self.assertIsInstance(UpperCamelCase__			,							UpperCamelCase__     )

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

														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )

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




							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	List[str]         =       self.ta_base_tokenizer
														lowerCamelCase__					:	Tuple         =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
														lowerCamelCase__					:	Tuple         =       tokenizer(UpperCamelCase__			,							padding=UpperCamelCase__			,							return_tensors=UpperCamelCase__     )
														# check if input_ids are returned and no decoder_input_ids
														self.assertIn("""input_ids"""			,							UpperCamelCase__     )
														self.assertIn("""attention_mask"""			,							UpperCamelCase__     )
														self.assertNotIn("""decoder_input_ids"""			,							UpperCamelCase__     )
														self.assertNotIn("""decoder_attention_mask"""			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	str         =       self.ta_base_tokenizer
														lowerCamelCase__					:	List[Any]         =       [
														    """Summary of the text.""",
														    """Another summary.""",
														]
														lowerCamelCase__					:	Union[str, Any]         =       tokenizer(
														    text_target=UpperCamelCase__			,							max_length=32			,							padding="""max_length"""			,							truncation=UpperCamelCase__			,							return_tensors=UpperCamelCase__     )
														self.assertEqual(32			,							targets["""input_ids"""].shape[1]     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Tuple         =       self.ta_base_tokenizer
														lowerCamelCase__					:	str         =       ["""A long paragraph for summarization. </s>"""]
														lowerCamelCase__					:	Optional[Any]         =       ["""Summary of the text. </s>"""]
														# fmt: off
														lowerCamelCase__					:	Tuple         =       [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]
														lowerCamelCase__					:	Any         =       [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
														# fmt: on

														lowerCamelCase__					:	Any         =       tokenizer(UpperCamelCase__			,							text_target=UpperCamelCase__     )

														self.assertEqual(UpperCamelCase__			,							batch["""input_ids"""][0]     )
														self.assertEqual(UpperCamelCase__			,							batch["""labels"""][0]     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														# safety check on max_len default value so we are sure the test works
														lowerCamelCase__					:	Any         =       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
														lowerCamelCase__					:	Any         =       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
																												lowerCamelCase__					:	int         =       tempfile.mkdtemp()

																												lowerCamelCase__					:	List[str]         =       """ He is very happy, UNwant\u00E9d,running"""
																												lowerCamelCase__					:	List[Any]         =       tokenizer.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     )
																												tokenizer.save_pretrained(UpperCamelCase__     )

																												lowerCamelCase__					:	List[Any]         =       tokenizer.__class__.from_pretrained(UpperCamelCase__     )
																												lowerCamelCase__					:	Optional[int]         =       after_tokenizer.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     )
																												self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )

																												shutil.rmtree(UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       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
																												lowerCamelCase__					:	Any         =       tempfile.mkdtemp()

																												lowerCamelCase__					:	Optional[Any]         =       """ He is very happy, UNwant\u00E9d,running"""
																												tokenizer.add_tokens(["""bim""", """bambam"""]     )
																												lowerCamelCase__					:	Optional[int]         =       tokenizer.additional_special_tokens
																												additional_special_tokens.append("""new_additional_special_token"""     )
																												tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}     )
																												lowerCamelCase__					:	List[Any]         =       tokenizer.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     )
																												tokenizer.save_pretrained(UpperCamelCase__     )

																												lowerCamelCase__					:	Union[str, Any]         =       tokenizer.__class__.from_pretrained(UpperCamelCase__     )
																												lowerCamelCase__					:	int         =       after_tokenizer.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     )
																												self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )
																												self.assertIn("""new_additional_special_token"""			,							after_tokenizer.additional_special_tokens     )
																												self.assertEqual(after_tokenizer.model_max_length			,							42     )

																												lowerCamelCase__					:	Any         =       tokenizer.__class__.from_pretrained(UpperCamelCase__			,							model_max_length=43     )
																												self.assertEqual(tokenizer.model_max_length			,							43     )

																												shutil.rmtree(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	List[Any]         =       []
														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(UpperCamelCase__     )

																												with open(os.path.join(UpperCamelCase__			,							"""special_tokens_map.json"""     )			,							encoding="""utf-8"""     ) as json_file:
																																			lowerCamelCase__					:	Union[str, Any]         =       json.load(UpperCamelCase__     )

																												with open(os.path.join(UpperCamelCase__			,							"""tokenizer_config.json"""     )			,							encoding="""utf-8"""     ) as json_file:
																																			lowerCamelCase__					:	Optional[Any]         =       json.load(UpperCamelCase__     )

																												lowerCamelCase__					:	Optional[int]         =       [F'''<extra_id_{i}>''' for i in range(125     )]

																												lowerCamelCase__					:	int         =       added_tokens_extra_ids + [
																												    """an_additional_special_token"""
																												]
																												lowerCamelCase__					:	Optional[Any]         =       added_tokens_extra_ids + [
																												    """an_additional_special_token"""
																												]

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

																												# 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
																												lowerCamelCase__					:	Dict         =       tokenizer_class.from_pretrained(
																												    UpperCamelCase__			,							)
																												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
																												lowerCamelCase__					:	Optional[Any]         =       added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""			,							lstrip=UpperCamelCase__     )]
																												lowerCamelCase__					:	Any         =       tokenizer_class.from_pretrained(
																												    UpperCamelCase__			,							additional_special_tokens=UpperCamelCase__			,							)

																												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:				Union[str, Any]     ):
														lowerCamelCase__					:	Dict         =       []
														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(UpperCamelCase__     )

																												lowerCamelCase__					:	str         =       tokenizer_class.from_pretrained(UpperCamelCase__     )

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




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							def 		lowerCamelCase_				(							self:				str     ):
														pass




							def 		lowerCamelCase_				(							self:				List[str]     ):
														pass




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							def 		lowerCamelCase_				(							self:				int     ):
														# The default common tokenizer tests uses invalid tokens for ByT5 that can only accept one-character strings
														# and special added tokens as tokens
														lowerCamelCase__					:	Dict         =       self.get_tokenizers(fast=UpperCamelCase__			,							do_lower_case=UpperCamelCase__     )
														for tokenizer in tokenizers:
																					with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
																												lowerCamelCase__					:	Dict         =       ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
																												lowerCamelCase__					:	Optional[int]         =       tokenizer.convert_tokens_to_string(UpperCamelCase__     )

																												self.assertIsInstance(UpperCamelCase__			,							UpperCamelCase__     )




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

																												lowerCamelCase__					:	str         =       0
																												lowerCamelCase__					:	Any         =       tokenizer.convert_ids_to_tokens(
																												    UpperCamelCase__			,							skip_special_tokens=UpperCamelCase__     )

																												for attr in attributes_list:
																																			setattr(UpperCamelCase__			,							attr + """_id"""			,							UpperCamelCase__     )
																																			self.assertEqual(getattr(UpperCamelCase__			,							UpperCamelCase__     )			,							UpperCamelCase__     )
																																			self.assertEqual(getattr(UpperCamelCase__			,							attr + """_id"""     )			,							UpperCamelCase__     )

																																			setattr(UpperCamelCase__			,							attr + """_id"""			,							UpperCamelCase__     )
																																			self.assertEqual(getattr(UpperCamelCase__			,							UpperCamelCase__     )			,							UpperCamelCase__     )
																																			self.assertEqual(getattr(UpperCamelCase__			,							attr + """_id"""     )			,							UpperCamelCase__     )

																												setattr(UpperCamelCase__			,							"""additional_special_tokens_ids"""			,							[]     )
																												self.assertListEqual(getattr(UpperCamelCase__			,							"""additional_special_tokens"""     )			,							[]     )
																												self.assertListEqual(getattr(UpperCamelCase__			,							"""additional_special_tokens_ids"""     )			,							[]     )

																												setattr(UpperCamelCase__			,							"""additional_special_tokens_ids"""			,							[token_id_to_test_setters]     )
																												self.assertListEqual(getattr(UpperCamelCase__			,							"""additional_special_tokens"""     )			,							[token_to_test_setters]     )
																												self.assertListEqual(getattr(UpperCamelCase__			,							"""additional_special_tokens_ids"""     )			,							[token_id_to_test_setters]     )

