



'''simple docstring'''

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_A				:    Union[str, Any]     						=logging.get_logger(__name__)  # pylint: disable=invalid-name

_A				:    int     						='''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''







def 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase=8					) ->			List[Any]:
	_lowercase					:       str       =		height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	_lowercase					:       List[str]       =		width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	int     ,  UpperCamelCase_				:	UNetaDConditionModel     ,  UpperCamelCase_				:	DDPMScheduler     ,  UpperCamelCase_				:	VQModel     ,  )  ->       Optional[Any]:



						'''simple docstring'''





						super().__init__()

						self.register_modules(
						    unet=UpperCamelCase_     ,  scheduler=UpperCamelCase_     ,  movq=UpperCamelCase_     ,  )
						_lowercase					:       Any       =		2 ** (len(self.movq.config.block_out_channels					) - 1)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Any					)  ->       Optional[int]:



						'''simple docstring'''





						if latents is None:
							_lowercase					:       str       =		randn_tensor(UpperCamelCase_     ,  generator=UpperCamelCase_     ,  device=UpperCamelCase_     ,  dtype=UpperCamelCase_					)
						else:
							if latents.shape != shape:
								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
							_lowercase					:       Dict       =		latents.to(UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		latents * scheduler.init_noise_sigma
						return latents




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	Union[str, Any]=0					)  ->       Dict:



						'''simple docstring'''





						if is_accelerate_available():
							from accelerate import cpu_offload
						else:
							raise ImportError('Please install accelerate via `pip install accelerate`'					)

						_lowercase					:       List[str]       =		torch.device(F'''cuda:{gpu_id}'''					)

						_lowercase					:       Optional[int]       =		[
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
								cpu_offload(UpperCamelCase_     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	List[str]=0					)  ->       Union[str, Any]:



						'''simple docstring'''





						if is_accelerate_available() and is_accelerate_version('>='     ,  '0.17.0.dev0'					):
							from accelerate import cpu_offload_with_hook
						else:
							raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'					)

						_lowercase					:       int       =		torch.device(F'''cuda:{gpu_id}'''					)

						if self.device.type != "cpu":
							self.to('cpu'     ,  silence_dtype_warnings=UpperCamelCase_					)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						_lowercase					:       Union[str, Any]       =		None
						for cpu_offloaded_model in [self.unet, self.movq]:
							_lowercase					:       Optional[int]       =		cpu_offload_with_hook(UpperCamelCase_     ,  UpperCamelCase_     ,  prev_module_hook=UpperCamelCase_					)

						# We'll offload the last model manually.
						_lowercase					:       Optional[int]       =		hook




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





						if not hasattr(self.unet     ,  '_hf_hook'					):
							return self.device
						for module in self.unet.modules():
							if (
							    hasattr(UpperCamelCase_     ,  '_hf_hook'					)
							    and hasattr(module._hf_hook     ,  'execution_device'					)
							    and module._hf_hook.execution_device is not None
							):
								return torch.device(module._hf_hook.execution_device					)
						return self.device




					@torch.no_grad()
					@replace_example_docstring(UpperCamelCase_					)
					def __call__(  self				:	Dict     ,  UpperCamelCase_				:	Union[torch.FloatTensor, List[torch.FloatTensor]]     ,  UpperCamelCase_				:	Union[torch.FloatTensor, List[torch.FloatTensor]]     ,  UpperCamelCase_				:	torch.FloatTensor     ,  UpperCamelCase_				:	int = 512     ,  UpperCamelCase_				:	int = 512     ,  UpperCamelCase_				:	int = 100     ,  UpperCamelCase_				:	float = 4.0     ,  UpperCamelCase_				:	int = 1     ,  UpperCamelCase_				:	Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  UpperCamelCase_				:	Optional[torch.FloatTensor] = None     ,  UpperCamelCase_				:	Optional[str] = "pil"     ,  UpperCamelCase_				:	bool = True     ,  )  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		self._execution_device

						_lowercase					:       List[str]       =		guidance_scale > 1.0

						if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Optional[int]       =		torch.cat(UpperCamelCase_     ,  dim=0					)
						if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Optional[Any]       =		torch.cat(UpperCamelCase_     ,  dim=0					)
						if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Tuple       =		torch.cat(UpperCamelCase_     ,  dim=0					)

						_lowercase					:       Dict       =		image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
							_lowercase					:       Optional[int]       =		image_embeds.repeat_interleave(UpperCamelCase_     ,  dim=0					)
							_lowercase					:       List[str]       =		negative_image_embeds.repeat_interleave(UpperCamelCase_     ,  dim=0					)
							_lowercase					:       int       =		hint.repeat_interleave(UpperCamelCase_     ,  dim=0					)

						_lowercase					:       Optional[int]       =		torch.cat([negative_image_embeds, image_embeds]     ,  dim=0					).to(dtype=self.unet.dtype     ,  device=UpperCamelCase_					)
						_lowercase					:       List[str]       =		torch.cat([hint, hint]     ,  dim=0					).to(dtype=self.unet.dtype     ,  device=UpperCamelCase_					)
						self.scheduler.set_timesteps(UpperCamelCase_     ,  device=UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		self.scheduler.timesteps

						_lowercase					:       List[Any]       =		self.movq.config.latent_channels

						_lowercase					:       List[str]       =		downscale_height_and_width(UpperCamelCase_     ,  UpperCamelCase_     ,  self.movq_scale_factor					)

						# create initial latent
						_lowercase					:       Union[str, Any]       =		self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)     ,  image_embeds.dtype     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  self.scheduler     ,  )

						for i, t in enumerate(self.progress_bar(UpperCamelCase_					)					):
							# expand the latents if we are doing classifier free guidance
							_lowercase					:       Any       =		torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

							_lowercase					:       Dict       =		{'image_embeds': image_embeds, 'hint': hint}
							_lowercase					:       str       =		self.unet(
							    sample=UpperCamelCase_     ,  timestep=UpperCamelCase_     ,  encoder_hidden_states=UpperCamelCase_     ,  added_cond_kwargs=UpperCamelCase_     ,  return_dict=UpperCamelCase_     ,  )[0]

							if do_classifier_free_guidance:
								_lowercase					:       str       =		noise_pred.split(latents.shape[1]     ,  dim=1					)
								_lowercase					:       Optional[int]       =		noise_pred.chunk(2					)
								_lowercase					:       Dict       =		variance_pred.chunk(2					)
								_lowercase					:       List[Any]       =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
								_lowercase					:       int       =		torch.cat([noise_pred, variance_pred_text]     ,  dim=1					)

							if not (
							    hasattr(self.scheduler.config     ,  'variance_type'					)
							    and self.scheduler.config.variance_type in ["learned", "learned_range"]
							):
								_lowercase					:       Optional[Any]       =		noise_pred.split(latents.shape[1]     ,  dim=1					)

							# compute the previous noisy sample x_t -> x_t-1
							_lowercase					:       int       =		self.scheduler.step(
							    UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  generator=UpperCamelCase_     ,  )[0]
						# post-processing
						_lowercase					:       Tuple       =		self.movq.decode(UpperCamelCase_     ,  force_not_quantize=UpperCamelCase_					)['sample']

						if output_type not in ["pt", "np", "pil"]:
							raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''					)

						if output_type in ["np", "pil"]:
							_lowercase					:       Any       =		image * 0.5 + 0.5
							_lowercase					:       Optional[int]       =		image.clamp(0     ,  1					)
							_lowercase					:       int       =		image.cpu().permute(0     ,  2     ,  3     ,  1					).float().numpy()

						if output_type == "pil":
							_lowercase					:       Any       =		self.numpy_to_pil(UpperCamelCase_					)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=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[str]     						='''pt'''
elif is_tf_available():
		_A				:    Tuple     						='''tf'''
else:
		_A				:    Optional[int]     						='''jax'''



class 		lowerCamelCase__      (						A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						ByTaTokenizer
					A_						    =						False




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Union[str, Any]:



						'''simple docstring'''





						super().setUp()
						_lowercase					:       Any       =		ByTaTokenizer()
						tokenizer.save_pretrained(self.tmpdirname					)




					@cached_property
					def 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	int     ,  **UpperCamelCase_				:	List[Any]					)  ->       ByTaTokenizer:



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Union[str, Any]=False     ,  UpperCamelCase_				:	Tuple=20     ,  UpperCamelCase_				:	Optional[int]=5					)  ->       Tuple[str, list]:



						'''simple docstring'''





						_lowercase					:       Dict       =		[]
						for i in range(len(UpperCamelCase_					)					):
							try:
								_lowercase					:       List[Any]       =		tokenizer.decode([i]     ,  clean_up_tokenization_spaces=UpperCamelCase_					)
							except UnicodeDecodeError:
								pass
							toks.append((i, tok)					)

						_lowercase					:       Optional[Any]       =		list(filter(lambda UpperCamelCase_					: re.match(r'^[ a-zA-Z]+$'     ,  t[1]					)     ,  UpperCamelCase_					)					)
						_lowercase					:       List[Any]       =		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:
							_lowercase					:       List[Any]       =		toks[:max_length]
						if min_length is not None and len(UpperCamelCase_					) < min_length and len(UpperCamelCase_					) > 0:
							while len(UpperCamelCase_					) < min_length:
								_lowercase					:       Tuple       =		toks + toks
        # toks_str = [t[1] for t in toks]
						_lowercase					:       Dict       =		[t[0] for t in toks]

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




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       List[str]       =		self.ta_base_tokenizer
						_lowercase					:       Union[str, Any]       =		tokenizer(['hi</s>', 'I went to the gym</s>', '</s>']					)
						_lowercase					:       Tuple       =		tokenizer(['hi', 'I went to the gym', '']					)
						self.assertListEqual(batch_with_eos_added['input_ids']     ,  batch_without_eos_added['input_ids']					)




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		self.ta_base_tokenizer
						_lowercase					:       Tuple       =		'Unicode €.'
						_lowercase					:       List[Any]       =		tokenizer(UpperCamelCase_					)
						_lowercase					:       List[Any]       =		[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
						self.assertEqual(encoded['input_ids']     ,  UpperCamelCase_					)

						# decoding
						_lowercase					:       List[str]       =		tokenizer.decode(UpperCamelCase_					)
						self.assertEqual(UpperCamelCase_     ,  'Unicode €.</s>'					)

						_lowercase					:       Any       =		tokenizer('e è é ê ë'					)
						_lowercase					:       Optional[int]       =		[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
						self.assertEqual(encoded['input_ids']     ,  UpperCamelCase_					)
						# decoding
						_lowercase					:       Tuple       =		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 						__UpperCAmelCase     (  self				:	Tuple					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		self.ta_base_tokenizer
						_lowercase					:       int       =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
						# fmt: off
						_lowercase					:       Any       =		[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
						_lowercase					:       Dict       =		tokenizer(UpperCamelCase_     ,  padding=UpperCamelCase_     ,  return_tensors=UpperCamelCase_					)
						self.assertIsInstance(UpperCamelCase_     ,  UpperCamelCase_					)

						if FRAMEWORK != "jax":
							_lowercase					:       Optional[Any]       =		list(batch.input_ids.numpy()[0]					)
						else:
							_lowercase					:       List[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 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self.ta_base_tokenizer
						_lowercase					:       List[str]       =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
						_lowercase					:       str       =		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 						__UpperCAmelCase     (  self				:	Any					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.ta_base_tokenizer
						_lowercase					:       Optional[Any]       =		[
						    'Summary of the text.',
						    'Another summary.',
						]
						_lowercase					:       str       =		tokenizer(
						    text_target=UpperCamelCase_     ,  max_length=32     ,  padding='max_length'     ,  truncation=UpperCamelCase_     ,  return_tensors=UpperCamelCase_					)
						self.assertEqual(32     ,  targets['input_ids'].shape[1]					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       str       =		self.ta_base_tokenizer
						_lowercase					:       str       =		['A long paragraph for summarization. </s>']
						_lowercase					:       Optional[int]       =		['Summary of the text. </s>']
						# fmt: off
						_lowercase					:       Optional[Any]       =		[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]
						_lowercase					:       Optional[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

						_lowercase					:       Any       =		tokenizer(UpperCamelCase_     ,  text_target=UpperCamelCase_					)

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




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       int:



						'''simple docstring'''





						_lowercase					:       Dict       =		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
						_lowercase					:       List[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
								_lowercase					:       List[Any]       =		tempfile.mkdtemp()

								_lowercase					:       Any       =		' He is very happy, UNwant\u00E9d,running'
								_lowercase					:       Union[str, Any]       =		tokenizer.encode(UpperCamelCase_     ,  add_special_tokens=UpperCamelCase_					)
								tokenizer.save_pretrained(UpperCamelCase_					)

								_lowercase					:       Optional[int]       =		tokenizer.__class__.from_pretrained(UpperCamelCase_					)
								_lowercase					:       Tuple       =		after_tokenizer.encode(UpperCamelCase_     ,  add_special_tokens=UpperCamelCase_					)
								self.assertListEqual(UpperCamelCase_     ,  UpperCamelCase_					)

								shutil.rmtree(UpperCamelCase_					)

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

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

								_lowercase					:       List[str]       =		tokenizer.__class__.from_pretrained(UpperCamelCase_					)
								_lowercase					:       Dict       =		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					)

								_lowercase					:       Dict       =		tokenizer.__class__.from_pretrained(UpperCamelCase_     ,  model_max_length=43					)
								self.assertEqual(tokenizer.model_max_length     ,  43					)

								shutil.rmtree(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Tuple:



						'''simple docstring'''





						_lowercase					:       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:
									_lowercase					:       int       =		json.load(UpperCamelCase_					)

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

								_lowercase					:       List[Any]       =		[F'''<extra_id_{i}>''' for i in range(125					)]

								_lowercase					:       Any       =		added_tokens_extra_ids + [
								    'an_additional_special_token'
								]
								_lowercase					:       int       =		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
								_lowercase					:       Optional[Any]       =		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
								_lowercase					:       List[str]       =		added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'     ,  lstrip=UpperCamelCase_					)]
								_lowercase					:       Tuple       =		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 						__UpperCAmelCase     (  self				:	List[str]					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Union[str, 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_					)

								_lowercase					:       str       =		tokenizer_class.from_pretrained(UpperCamelCase_					)

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




					def 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       int:



						'''simple docstring'''





						pass




					def 						__UpperCAmelCase     (  self				:	str					)  ->       Tuple:



						'''simple docstring'''





						pass




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       List[Any]:



						'''simple docstring'''





						pass




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       int:



						'''simple docstring'''





						pass




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       Optional[Any]:



						'''simple docstring'''





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

								self.assertIsInstance(UpperCamelCase_     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]					)  ->       str:



						'''simple docstring'''





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

								_lowercase					:       Optional[int]       =		0
								_lowercase					:       int       =		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]					)
