from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__a  :     Tuple       =  8





def 				UpperCAmelCase						(    lowercase							,			lowercase=BITS							):







				"""simple docstring"""



				__lowercase 			=							x.device

				__lowercase 			=							(x * 255).int().clamp(0							,			255							)

				__lowercase 			=							2 ** torch.arange(bits - 1							,			-1							,			-1							,			device=lowercase							)
				__lowercase 			=							rearrange(lowercase							,			'''d -> d 1 1'''							)
				__lowercase 			=							rearrange(lowercase							,			'''b c h w -> b c 1 h w'''							)

				__lowercase 			=							((x & mask) != 0).float()
				__lowercase 			=							rearrange(lowercase							,			'''b c d h w -> b (c d) h w'''							)
				__lowercase 			=							bits * 2 - 1
				return bits





def 				UpperCAmelCase						(    lowercase							,			lowercase=BITS							):







				"""simple docstring"""



				__lowercase 			=							x.device

				__lowercase 			=							(x > 0).int()
				__lowercase 			=							2 ** torch.arange(bits - 1							,			-1							,			-1							,			device=lowercase							,			dtype=torch.intaa							)

				__lowercase 			=							rearrange(lowercase							,			'''d -> d 1 1'''							)
				__lowercase 			=							rearrange(lowercase							,			'''b (c d) h w -> b c d h w'''							,			d=8							)
				__lowercase 			=							reduce(x * mask							,			'''b c d h w -> b c h w'''							,			'''sum'''							)
				return (dec / 255).clamp(0.0							,			1.0							)





def 				UpperCAmelCase						(    self							,			lowercase							,			lowercase							,			lowercase							,			lowercase = 0.0							,			lowercase = True							,			lowercase=None							,			lowercase = True							,			):







				"""simple docstring"""



				if self.num_inference_steps is None:
								raise ValueError(
								    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''							)

				# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
				# Ideally, read DDIM paper in-detail understanding

				# Notation (<variable name> -> <name in paper>
				# - pred_noise_t -> e_theta(x_t, t)
				# - pred_original_sample -> f_theta(x_t, t) or x_0
				# - std_dev_t -> sigma_t
				# - eta -> η
				# - pred_sample_direction -> "direction pointing to x_t"
				# - pred_prev_sample -> "x_t-1"

				# 1. get previous step value (=t-1)
				__lowercase 			=							timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 2. compute alphas, betas
				__lowercase 			=							self.alphas_cumprod[timestep]
				__lowercase 			=							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

				__lowercase 			=							1 - alpha_prod_t

				# 3. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__lowercase 			=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

				# 4. Clip "predicted x_0"
				__lowercase 			=							self.bit_scale
				if self.config.clip_sample:
								__lowercase 			=							torch.clamp(lowercase							,			-scale							,			lowercase							)

				# 5. compute variance: "sigma_t(η)" -> see formula (16)
				# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
				__lowercase 			=							self._get_variance(lowercase							,			lowercase							)
				__lowercase 			=							eta * variance ** 0.5

				if use_clipped_model_output:
								# the model_output is always re-derived from the clipped x_0 in Glide
								__lowercase 			=							(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

				# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__lowercase 			=							(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				__lowercase 			=							alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

				if eta > 0:
								# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
								__lowercase 			=							model_output.device if torch.is_tensor(lowercase							) else '''cpu'''
								__lowercase 			=							torch.randn(model_output.shape							,			dtype=model_output.dtype							,			generator=lowercase							).to(lowercase							)
								__lowercase 			=							self._get_variance(lowercase							,			lowercase							) ** 0.5 * eta * noise

								__lowercase 			=							prev_sample + variance

				if not return_dict:
								return (prev_sample,)

				return DDIMSchedulerOutput(prev_sample=lowercase							,			pred_original_sample=lowercase							)





def 				UpperCAmelCase						(    self							,			lowercase							,			lowercase							,			lowercase							,			lowercase="epsilon"							,			lowercase=None							,			lowercase = True							,			):







				"""simple docstring"""



				__lowercase 			=							timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
								__lowercase			,     __lowercase 			=							torch.split(lowercase							,			sample.shape[1]							,			dim=1							)
				else:
								__lowercase 			=							None

				# 1. compute alphas, betas
				__lowercase 			=							self.alphas_cumprod[t]
				__lowercase 			=							self.alphas_cumprod[t - 1] if t > 0 else self.one
				__lowercase 			=							1 - alpha_prod_t
				__lowercase 			=							1 - alpha_prod_t_prev

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				if prediction_type == "epsilon":
								__lowercase 			=							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif prediction_type == "sample":
								__lowercase 			=							model_output
				else:
								raise ValueError(F"Unsupported prediction_type {prediction_type}."							)

				# 3. Clip "predicted x_0"
				__lowercase 			=							self.bit_scale
				if self.config.clip_sample:
								__lowercase 			=							torch.clamp(lowercase							,			-scale							,			lowercase							)

				# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__lowercase 			=							(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
				__lowercase 			=							self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

				# 5. Compute predicted previous sample µ_t
				# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
				__lowercase 			=							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				__lowercase 			=							0
				if t > 0:
								__lowercase 			=							torch.randn(
								    model_output.size()							,			dtype=model_output.dtype							,			layout=model_output.layout							,			generator=lowercase							).to(model_output.device							)
								__lowercase 			=							(self._get_variance(lowercase							,			predicted_variance=lowercase							) ** 0.5) * noise

				__lowercase 			=							pred_prev_sample + variance

				if not return_dict:
								return (pred_prev_sample,)

				return DDPMSchedulerOutput(prev_sample=lowercase							,			pred_original_sample=lowercase							)




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = 1.0      ,   )     ->					Optional[int]:

							'''simple docstring'''


							super().__init__()
							__lowercase 			=							bit_scale
							__lowercase 			=							(
							    ddim_bit_scheduler_step if isinstance(lowerCAmelCase__      ,   lowerCAmelCase__						) else ddpm_bit_scheduler_step
							)

							self.register_modules(unet=lowerCAmelCase__      ,   scheduler=lowerCAmelCase__						)





			@torch.no_grad()
			def __call__(   self      ,   lowerCAmelCase__ = 2_56      ,   lowerCAmelCase__ = 2_56      ,   lowerCAmelCase__ = 50      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = 1      ,   lowerCAmelCase__ = "pil"      ,   lowerCAmelCase__ = True      ,   **lowerCAmelCase__      ,   )     ->					Union[Tuple, ImagePipelineOutput]:

							'''simple docstring'''


							__lowercase 			=							torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)      ,   generator=lowerCAmelCase__      ,   )
							__lowercase 			=							decimal_to_bits(lowerCAmelCase__						) * self.bit_scale
							__lowercase 			=							latents.to(self.device						)

							self.scheduler.set_timesteps(lowerCAmelCase__						)

							for t in self.progress_bar(self.scheduler.timesteps						):
											# predict the noise residual
											__lowercase 			=							self.unet(lowerCAmelCase__      ,   lowerCAmelCase__						).sample

											# compute the previous noisy sample x_t -> x_t-1
											__lowercase 			=							self.scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						).prev_sample

							__lowercase 			=							bits_to_decimal(lowerCAmelCase__						)

							if output_type == "pil":
											__lowercase 			=							self.numpy_to_pil(lowerCAmelCase__						)

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=lowerCAmelCase__						)
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  :     Tuple       =  """pt"""
elif is_tf_available():
		__a  :     int       =  """tf"""
else:
		__a  :     Tuple       =  """jax"""




class    _UpperCamelCase      (						_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : List[Any]			 =						ByTaTokenizer
			__a  : str			 =						False





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

							'''simple docstring'''


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





			@cached_property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					ByTaTokenizer:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=20      ,   lowerCAmelCase__=5						)     ->					Tuple[str, list]:

							'''simple docstring'''


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

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

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							'''Unicode €.'''
							__lowercase 			=							tokenizer(lowerCAmelCase__						)
							__lowercase 			=							[88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
							self.assertEqual(encoded['''input_ids''']      ,   lowerCAmelCase__						)

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

							__lowercase 			=							tokenizer('''e è é ê ë'''						)
							__lowercase 			=							[1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
							self.assertEqual(encoded['''input_ids''']      ,   lowerCAmelCase__						)
							# decoding
							__lowercase 			=							tokenizer.decode(lowerCAmelCase__						)
							self.assertEqual(lowerCAmelCase__      ,   '''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        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							# fmt: off
							__lowercase 			=							[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
							# fmt: on
							__lowercase 			=							tokenizer(lowerCAmelCase__      ,   padding=lowerCAmelCase__      ,   return_tensors=lowerCAmelCase__						)
							self.assertIsInstance(lowerCAmelCase__      ,   lowerCAmelCase__						)

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

							self.assertListEqual(lowerCAmelCase__      ,   lowerCAmelCase__						)

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							self.ta_base_tokenizer
							__lowercase 			=							['''A long paragraph for summarization. </s>''']
							__lowercase 			=							['''Summary of the text. </s>''']
							# fmt: off
							__lowercase 			=							[68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
							__lowercase 			=							[86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
							# fmt: on

							__lowercase 			=							tokenizer(lowerCAmelCase__      ,   text_target=lowerCAmelCase__						)

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							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 			=							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 			=							tempfile.mkdtemp()

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

															__lowercase 			=							tokenizer.__class__.from_pretrained(lowerCAmelCase__						)
															__lowercase 			=							after_tokenizer.encode(lowerCAmelCase__      ,   add_special_tokens=lowerCAmelCase__						)
															self.assertListEqual(lowerCAmelCase__      ,   lowerCAmelCase__						)

															shutil.rmtree(lowerCAmelCase__						)

							__lowercase 			=							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 			=							tempfile.mkdtemp()

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

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

															__lowercase 			=							tokenizer.__class__.from_pretrained(lowerCAmelCase__      ,   model_max_length=43						)
															self.assertEqual(tokenizer.model_max_length      ,   43						)

															shutil.rmtree(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							__lowercase 			=							[]
							if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

							if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

							for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer_utils.save_pretrained(lowerCAmelCase__						)

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

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

															__lowercase 			=							[F"<extra_id_{i}>" for i in range(1_25						)]

															__lowercase 			=							added_tokens_extra_ids + [
															    '''an_additional_special_token'''
															]
															__lowercase 			=							added_tokens_extra_ids + [
															    '''an_additional_special_token'''
															]

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

															# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
															# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
															# "special_tokens_map.json" files
															__lowercase 			=							tokenizer_class.from_pretrained(
															    lowerCAmelCase__      ,   )
															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 			=							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''      ,   lstrip=lowerCAmelCase__						)]
															__lowercase 			=							tokenizer_class.from_pretrained(
															    lowerCAmelCase__      ,   additional_special_tokens=lowerCAmelCase__      ,   )

															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        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							__lowercase 			=							[]
							if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())						)

							if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())						)

							for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer_utils.save_pretrained(lowerCAmelCase__						)

															__lowercase 			=							tokenizer_class.from_pretrained(lowerCAmelCase__						)

															self.assertTrue(tokenizer.decode([2_55]						) == ''''''						)





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

							'''simple docstring'''


							pass





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

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							pass





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

							'''simple docstring'''


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

															self.assertIsInstance(lowerCAmelCase__      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


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

															__lowercase 			=							0
															__lowercase 			=							tokenizer.convert_ids_to_tokens(
															    lowerCAmelCase__      ,   skip_special_tokens=lowerCAmelCase__						)

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

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

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

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