






"""simple docstring"""







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			:    Union[str, Any]				 =		8





def 						lowercase__(A ,		A=BITS						)    ->Optional[int]:




						"""simple docstring"""



						lowercase__    :				int=	x.device

						lowercase__    :				Union[str, Any]=	(x * 255).int().clamp(0 ,		255						)

						lowercase__    :				List[Any]=	2 ** torch.arange(bits - 1 ,		-1 ,		-1 ,		device=A						)
						lowercase__    :				Optional[Any]=	rearrange(A ,		"d -> d 1 1"						)
						lowercase__    :				Optional[int]=	rearrange(A ,		"b c h w -> b c 1 h w"						)

						lowercase__    :				Dict=	((x & mask) != 0).float()
						lowercase__    :				Dict=	rearrange(A ,		"b c d h w -> b (c d) h w"						)
						lowercase__    :				Optional[int]=	bits * 2 - 1
						return bits





def 						lowercase__(A ,		A=BITS						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	x.device

						lowercase__    :				Optional[Any]=	(x > 0).int()
						lowercase__    :				Union[str, Any]=	2 ** torch.arange(bits - 1 ,		-1 ,		-1 ,		device=A ,		dtype=torch.intaa						)

						lowercase__    :				List[Any]=	rearrange(A ,		"d -> d 1 1"						)
						lowercase__    :				Tuple=	rearrange(A ,		"b (c d) h w -> b c d h w" ,		d=8						)
						lowercase__    :				Any=	reduce(x * mask ,		"b c d h w -> b c h w" ,		"sum"						)
						return (dec / 255).clamp(0.0 ,		1.0						)





def 						lowercase__(self ,		A ,		A ,		A ,		A = 0.0 ,		A = True ,		A=None ,		A = True ,		)    ->Union[DDIMSchedulerOutput, Tuple]:




						"""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__    :				List[Any]=	timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						lowercase__    :				Optional[Any]=	self.alphas_cumprod[timestep]
						lowercase__    :				Dict=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

						lowercase__    :				Optional[int]=	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__    :				str=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						lowercase__    :				List[Any]=	self.bit_scale
						if self.config.clip_sample:
												lowercase__    :				str=	torch.clamp(A ,		-scale ,		A						)

						# 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__    :				Tuple=	self._get_variance(A ,		A						)
						lowercase__    :				Tuple=	eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												lowercase__    :				Any=	(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__    :				Any=	(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__    :				Union[str, Any]=	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__    :				Any=	model_output.device if torch.is_tensor(A						) else "cpu"
												lowercase__    :				Dict=	torch.randn(model_output.shape ,		dtype=model_output.dtype ,		generator=A						).to(A						)
												lowercase__    :				Optional[int]=	self._get_variance(A ,		A						) ** 0.5 * eta * noise

												lowercase__    :				Optional[Any]=	prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=A ,		pred_original_sample=A						)





def 						lowercase__(self ,		A ,		A ,		A ,		A="epsilon" ,		A=None ,		A = True ,		)    ->Union[DDPMSchedulerOutput, Tuple]:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	timestep

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

						# 1. compute alphas, betas
						lowercase__    :				Tuple=	self.alphas_cumprod[t]
						lowercase__    :				int=	self.alphas_cumprod[t - 1] if t > 0 else self.one
						lowercase__    :				Optional[int]=	1 - alpha_prod_t
						lowercase__    :				Optional[Any]=	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__    :				Tuple=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												lowercase__    :				Dict=	model_output
						else:
												raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''						)

						# 3. Clip "predicted x_0"
						lowercase__    :				Tuple=	self.bit_scale
						if self.config.clip_sample:
												lowercase__    :				Optional[Any]=	torch.clamp(A ,		-scale ,		A						)

						# 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__    :				str=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						lowercase__    :				Optional[int]=	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__    :				str=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

						lowercase__    :				List[Any]=	pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=A ,		pred_original_sample=A						)







class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__ = 1.0	,  ):
												'''simple docstring'''
												super().__init__()
												lowercase__    :				List[str]=	bit_scale
												lowercase__    :				str=	(
												    ddim_bit_scheduler_step if isinstance(snake_case__	,  snake_case__      ) else ddpm_bit_scheduler_step
												)

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




						@torch.no_grad()
						def __call__(       self	,  snake_case__ = 256	,  snake_case__ = 256	,  snake_case__ = 50	,  snake_case__ = None	,  snake_case__ = 1	,  snake_case__ = "pil"	,  snake_case__ = True	,  **snake_case__	,  ):
												'''simple docstring'''
												lowercase__    :				Tuple=	torch.randn(
												    (batch_size, self.unet.config.in_channels, height, width)	,  generator=snake_case__	,  )
												lowercase__    :				Union[str, Any]=	decimal_to_bits(snake_case__      ) * self.bit_scale
												lowercase__    :				Any=	latents.to(self.device      )

												self.scheduler.set_timesteps(snake_case__      )

												for t in self.progress_bar(self.scheduler.timesteps      ):
																		# predict the noise residual
																		lowercase__    :				Union[str, Any]=	self.unet(snake_case__	,  snake_case__      ).sample

																		# compute the previous noisy sample x_t -> x_t-1
																		lowercase__    :				List[str]=	self.scheduler.step(snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

												lowercase__    :				Optional[int]=	bits_to_decimal(snake_case__      )

												if output_type == "pil":
																		lowercase__    :				Union[str, Any]=	self.numpy_to_pil(snake_case__      )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=snake_case__      )














"""simple docstring"""







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

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

from ...test_tokenization_common import TokenizerTesterMixin


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


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= PerceiverTokenizer
						__lowerCamelCase    	= False




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												super().setUp()
												lowercase__    :				str=	PerceiverTokenizer()
												tokenizer.save_pretrained(self.tmpdirname      )




						@cached_property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"      )




						def 		UpperCAmelCase_	(       self	,  **snake_case__      ):
												'''simple docstring'''
												return self.tokenizer_class.from_pretrained(self.tmpdirname	,  **snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__=False	,  snake_case__=20	,  snake_case__=5      ):
												'''simple docstring'''
												# XXX The default common tokenizer tests assume that every ID is decodable on its own.
												# This assumption is invalid for Perceiver 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.

												lowercase__    :				Union[str, Any]=	[]
												for i in range(len(snake_case__      )      ):
																		try:
																								lowercase__    :				Any=	tokenizer.decode([i]	,  clean_up_tokenization_spaces=snake_case__      )
																		except UnicodeDecodeError:
																								pass
																		toks.append((i, tok)      )

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

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




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	self.perceiver_tokenizer
												lowercase__    :				Union[str, Any]=	"Unicode €."
												lowercase__    :				Tuple=	tokenizer(snake_case__      )
												lowercase__    :				Dict=	[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
												self.assertEqual(encoded["input_ids"]	,  snake_case__      )

												# decoding
												lowercase__    :				List[str]=	tokenizer.decode(snake_case__      )
												self.assertEqual(snake_case__	,  "[CLS]Unicode €.[SEP]"      )

												lowercase__    :				List[str]=	tokenizer("e è é ê ë"      )
												lowercase__    :				int=	[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
												self.assertEqual(encoded["input_ids"]	,  snake_case__      )
												# decoding
												lowercase__    :				Any=	tokenizer.decode(snake_case__      )
												self.assertEqual(snake_case__	,  "[CLS]e è é ê ë[SEP]"      )

												# encode/decode, but with `encode` instead of `__call__`
												self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"      )      )	,  "[CLS]e è é ê ë[SEP]"      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	self.perceiver_tokenizer
												lowercase__    :				Tuple=	["A long paragraph for summarization.", "Another paragraph for summarization."]
												# fmt: off
												lowercase__    :				List[Any]=	[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
												# fmt: on
												lowercase__    :				Dict=	tokenizer(snake_case__	,  padding=snake_case__	,  return_tensors=snake_case__      )
												self.assertIsInstance(snake_case__	,  snake_case__      )

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

												self.assertListEqual(snake_case__	,  snake_case__      )

												self.assertEqual((2, 38)	,  batch.input_ids.shape      )
												self.assertEqual((2, 38)	,  batch.attention_mask.shape      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	self.perceiver_tokenizer
												lowercase__    :				int=	["A long paragraph for summarization.", "Another paragraph for summarization."]
												lowercase__    :				Union[str, Any]=	tokenizer(snake_case__	,  padding=snake_case__	,  return_tensors=snake_case__      )
												# check if input_ids are returned and no decoder_input_ids
												self.assertIn("input_ids"	,  snake_case__      )
												self.assertIn("attention_mask"	,  snake_case__      )
												self.assertNotIn("decoder_input_ids"	,  snake_case__      )
												self.assertNotIn("decoder_attention_mask"	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	self.perceiver_tokenizer
												lowercase__    :				int=	[
												    "Summary of the text.",
												    "Another summary.",
												]
												lowercase__    :				List[Any]=	tokenizer(
												    text_target=snake_case__	,  max_length=32	,  padding="max_length"	,  truncation=snake_case__	,  return_tensors=snake_case__      )
												self.assertEqual(32	,  targets["input_ids"].shape[1]      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# safety check on max_len default value so we are sure the test works
												lowercase__    :				Union[str, 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
												lowercase__    :				List[str]=	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__    :				str=	tempfile.mkdtemp()

																								lowercase__    :				str=	" He is very happy, UNwant\u00E9d,running"
																								lowercase__    :				int=	tokenizer.encode(snake_case__	,  add_special_tokens=snake_case__      )
																								tokenizer.save_pretrained(snake_case__      )

																								lowercase__    :				List[str]=	tokenizer.__class__.from_pretrained(snake_case__      )
																								lowercase__    :				Tuple=	after_tokenizer.encode(snake_case__	,  add_special_tokens=snake_case__      )
																								self.assertListEqual(snake_case__	,  snake_case__      )

																								shutil.rmtree(snake_case__      )

												lowercase__    :				List[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__    :				Tuple=	tempfile.mkdtemp()

																								lowercase__    :				List[Any]=	" He is very happy, UNwant\u00E9d,running"
																								tokenizer.add_tokens(["bim", "bambam"]      )
																								lowercase__    :				Tuple=	tokenizer.additional_special_tokens
																								additional_special_tokens.append("new_additional_special_token"      )
																								tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}      )
																								lowercase__    :				List[str]=	tokenizer.encode(snake_case__	,  add_special_tokens=snake_case__      )
																								tokenizer.save_pretrained(snake_case__      )

																								lowercase__    :				Tuple=	tokenizer.__class__.from_pretrained(snake_case__      )
																								lowercase__    :				Tuple=	after_tokenizer.encode(snake_case__	,  add_special_tokens=snake_case__      )
																								self.assertListEqual(snake_case__	,  snake_case__      )
																								self.assertIn("new_additional_special_token"	,  after_tokenizer.additional_special_tokens      )
																								self.assertEqual(after_tokenizer.model_max_length	,  42      )

																								lowercase__    :				Tuple=	tokenizer.__class__.from_pretrained(snake_case__	,  model_max_length=43      )
																								self.assertEqual(tokenizer.model_max_length	,  43      )

																								shutil.rmtree(snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''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(snake_case__      )

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

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

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

																								lowercase__    :				Optional[int]=	added_tokens_extra_ids + [
																								    "an_additional_special_token"
																								]
																								lowercase__    :				Union[str, Any]=	added_tokens_extra_ids + [
																								    "an_additional_special_token"
																								]

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

																								# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																								# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																								# "special_tokens_map.json" files
																								lowercase__    :				Optional[int]=	tokenizer_class.from_pretrained(
																								    snake_case__	,  )
																								self.assertIn(
																								    "an_additional_special_token"	,  tokenizer_without_change_in_init.additional_special_tokens      )
																								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__    :				int=	added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"	,  lstrip=snake_case__      )]
																								lowercase__    :				int=	tokenizer_class.from_pretrained(
																								    snake_case__	,  additional_special_tokens=snake_case__	,  )

																								self.assertIn("a_new_additional_special_token"	,  tokenizer.additional_special_tokens      )
																								self.assertEqual(
																								    ["a_new_additional_special_token"]	,  tokenizer.convert_ids_to_tokens(
																								        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]      )      )	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	self.perceiver_tokenizer
												self.assertEqual(tokenizer.decode([178]      )	,  "�"      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
												# strings and special added tokens as tokens
												lowercase__    :				Optional[int]=	self.get_tokenizers(fast=snake_case__	,  do_lower_case=snake_case__      )
												for tokenizer in tokenizers:
																		with self.subTest(F'''{tokenizer.__class__.__name__}'''      ):
																								lowercase__    :				Optional[int]=	["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
																								lowercase__    :				Optional[int]=	tokenizer.convert_tokens_to_string(snake_case__      )

																								self.assertIsInstance(snake_case__	,  snake_case__      )






