





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    __A       (       __A					):

			'''simple docstring'''







			def __init__(       self			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase			,    __lowerCAmelCase = None			,    ):



				'''simple docstring'''







				super().__init__()
				self.register_modules(transformer=lowerCAmelCase_			,    vae=lowerCAmelCase_			,    scheduler=lowerCAmelCase_				)

				# create a imagenet -> id dictionary for easier use
				lowerCamelCase__														=						{}
				if idalabel is not None:
					for key, value in idalabel.items():
						for label in value.split(''','''				):
							lowerCamelCase__														=						int(lowerCAmelCase_				)
					lowerCamelCase__														=						dict(sorted(self.labels.items()				)				)





			def 						__lowerCamelCase							(       self			,    __lowerCAmelCase				):



				'''simple docstring'''







				if not isinstance(lowerCAmelCase_			,    lowerCAmelCase_				):
					lowerCamelCase__														=						list(lowerCAmelCase_				)

				for l in label:
					if l not in self.labels:
						raise ValueError(
						    F'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'				)

				return [self.labels[l] for l in label]





			@torch.no_grad()
			def __call__(       self			,    __lowerCAmelCase			,    __lowerCAmelCase = 4.0			,    __lowerCAmelCase = None			,    __lowerCAmelCase = 5_0			,    __lowerCAmelCase = "pil"			,    __lowerCAmelCase = True			,    ):



				'''simple docstring'''







				lowerCamelCase__														=						len(lowerCAmelCase_				)
				lowerCamelCase__														=						self.transformer.config.sample_size
				lowerCamelCase__														=						self.transformer.config.in_channels

				lowerCamelCase__														=						randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size)			,    generator=lowerCAmelCase_			,    device=self.device			,    dtype=self.transformer.dtype			,    )
				lowerCamelCase__														=						torch.cat([latents] * 2				) if guidance_scale > 1 else latents

				lowerCamelCase__														=						torch.tensor(lowerCAmelCase_			,    device=self.device				).reshape(-1				)
				lowerCamelCase__														=						torch.tensor([1_0_0_0] * batch_size			,    device=self.device				)
				lowerCamelCase__														=						torch.cat([class_labels, class_null]			,    0				) if guidance_scale > 1 else class_labels

				# set step values
				self.scheduler.set_timesteps(lowerCAmelCase_				)

				for t in self.progress_bar(self.scheduler.timesteps				):
					if guidance_scale > 1:
						lowerCamelCase__														=						latent_model_input[: len(lowerCAmelCase_				) // 2]
						lowerCamelCase__														=						torch.cat([half, half]			,    dim=0				)
					lowerCamelCase__														=						self.scheduler.scale_model_input(lowerCAmelCase_			,    lowerCAmelCase_				)

					lowerCamelCase__														=						t
					if not torch.is_tensor(lowerCAmelCase_				):
						# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
						# This would be a good case for the `match` statement (Python 3.10+)
						lowerCamelCase__														=						latent_model_input.device.type == "mps"
						if isinstance(lowerCAmelCase_			,    lowerCAmelCase_				):
							lowerCamelCase__														=						torch.floataa if is_mps else torch.floataa
						else:
							lowerCamelCase__														=						torch.intaa if is_mps else torch.intaa
						lowerCamelCase__														=						torch.tensor([timesteps]			,    dtype=lowerCAmelCase_			,    device=latent_model_input.device				)
					elif len(timesteps.shape				) == 0:
						lowerCamelCase__														=						timesteps[None].to(latent_model_input.device				)
					# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
					lowerCamelCase__														=						timesteps.expand(latent_model_input.shape[0]				)
					# predict noise model_output
					lowerCamelCase__														=						self.transformer(
					    lowerCAmelCase_			,    timestep=lowerCAmelCase_			,    class_labels=lowerCAmelCase_				).sample

					# perform guidance
					if guidance_scale > 1:
						lowerCamelCase__														=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
						lowerCamelCase__														=						torch.split(lowerCAmelCase_			,    len(lowerCAmelCase_				) // 2			,    dim=0				)

						lowerCamelCase__														=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
						lowerCamelCase__														=						torch.cat([half_eps, half_eps]			,    dim=0				)

						lowerCamelCase__														=						torch.cat([eps, rest]			,    dim=1				)

					# learned sigma
					if self.transformer.config.out_channels // 2 == latent_channels:
						lowerCamelCase__														=						torch.split(lowerCAmelCase_			,    lowerCAmelCase_			,    dim=1				)
					else:
						lowerCamelCase__														=						noise_pred

					# compute previous image: x_t -> x_t-1
					lowerCamelCase__														=						self.scheduler.step(lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_				).prev_sample

				if guidance_scale > 1:
					lowerCamelCase__														=						latent_model_input.chunk(2			,    dim=0				)
				else:
					lowerCamelCase__														=						latent_model_input

				lowerCamelCase__														=						1 / self.vae.config.scaling_factor * latents
				lowerCamelCase__														=						self.vae.decode(lowerCAmelCase_				).sample

				lowerCamelCase__														=						(samples / 2 + 0.5).clamp(0			,    1				)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				lowerCamelCase__														=						samples.cpu().permute(0			,    2			,    3			,    1				).float().numpy()

				if output_type == "pil":
					lowerCamelCase__														=						self.numpy_to_pil(lowerCAmelCase_				)

				if not return_dict:
					return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase_				)











"""simple docstring"""
import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english







@require_tokenizers
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							BertTokenizer
	__magic_name__       =							BertTokenizerFast
	__magic_name__       =							True
	__magic_name__       =							True
	__magic_name__       =							filter_non_english





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Optional[Any]:
				super().setUp()

				UpperCAmelCase_	:					Optional[int]     	=   [
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				UpperCAmelCase_	:					Optional[Any]     	=   os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["vocab_file"]		)
				with open(self.vocab_file      ,      "w"      ,      encoding="utf-8"		) as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[str]		)		->     List[str]:
				UpperCAmelCase_	:					str     	=   "UNwant\u00E9d,running"
				UpperCAmelCase_	:					Optional[int]     	=   "unwanted, running"
				return input_text, output_text





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Any:
				UpperCAmelCase_	:					Optional[int]     	=   self.tokenizer_class(self.vocab_file		)

				UpperCAmelCase_	:					int     	=   tokenizer.tokenize("UNwant\u00E9d,running"		)
				self.assertListEqual(lowerCAmelCase_      ,      ["un", "##want", "##ed", ",", "runn", "##ing"]		)
				self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)      ,      [9, 6, 7, 12, 10, 11]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[Any]:
				if not self.test_rust_tokenizer:
							return

				UpperCAmelCase_	:					List[Any]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					str     	=   self.get_rust_tokenizer()

				UpperCAmelCase_	:					str     	=   "UNwant\u00E9d,running"

				UpperCAmelCase_	:					Any     	=   tokenizer.tokenize(lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   rust_tokenizer.tokenize(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					List[str]     	=   rust_tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   self.get_rust_tokenizer()
				UpperCAmelCase_	:					int     	=   tokenizer.encode(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[Any]     	=   rust_tokenizer.encode(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				# With lower casing
				UpperCAmelCase_	:					int     	=   self.get_tokenizer(do_lower_case=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   self.get_rust_tokenizer(do_lower_case=lowerCAmelCase_		)

				UpperCAmelCase_	:					Any     	=   "UNwant\u00E9d,running"

				UpperCAmelCase_	:					Dict     	=   tokenizer.tokenize(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   rust_tokenizer.tokenize(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   rust_tokenizer.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   self.get_rust_tokenizer()
				UpperCAmelCase_	:					Dict     	=   tokenizer.encode(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   rust_tokenizer.encode(lowerCAmelCase_		)
				self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     List[str]:
				UpperCAmelCase_	:					Union[str, Any]     	=   BasicTokenizer()

				self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"		)      ,      ["ah", "\u535A", "\u63A8", "zz"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[int]:
				UpperCAmelCase_	:					Union[str, Any]     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "		)      ,      ["hello", "!", "how", "are", "you", "?"]		)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)      ,      ["hello"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      strip_accents=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["hällo", "!", "how", "are", "you", "?"]		)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)      ,      ["h\u00E9llo"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Any:
				UpperCAmelCase_	:					str     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      strip_accents=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["hallo", "!", "how", "are", "you", "?"]		)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)      ,      ["hello"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[str]     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["hallo", "!", "how", "are", "you", "?"]		)
				self.assertListEqual(tokenizer.tokenize("H\u00E9llo"		)      ,      ["hello"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "		)      ,      ["HeLLo", "!", "how", "Are", "yoU", "?"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Union[str, Any]:
				UpperCAmelCase_	:					str     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      strip_accents=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["HäLLo", "!", "how", "Are", "yoU", "?"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     str:
				UpperCAmelCase_	:					Any     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      strip_accents=lowerCAmelCase_		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "		)      ,      ["HaLLo", "!", "how", "Are", "yoU", "?"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Dict:
				UpperCAmelCase_	:					Dict     	=   BasicTokenizer(do_lower_case=lowerCAmelCase_      ,      never_split=["[UNK]"]		)

				self.assertListEqual(
				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"		)      ,      ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Optional[int]     	=   BasicTokenizer()
				UpperCAmelCase_	:					int     	=   "a\n'll !!to?'d of, can't."
				UpperCAmelCase_	:					Union[str, Any]     	=   ["a", "'", "ll", "!", "!", "to", "?", "'", "d", "of", ",", "can", "'", "t", "."]
				self.assertListEqual(tokenizer.tokenize(lowerCAmelCase_		)      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Dict:
				UpperCAmelCase_	:					List[str]     	=   ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

				UpperCAmelCase_	:					int     	=   {}
				for i, token in enumerate(lowerCAmelCase_		):
							UpperCAmelCase_	:					Tuple     	=   i
				UpperCAmelCase_	:					Dict     	=   WordpieceTokenizer(vocab=lowerCAmelCase_      ,      unk_token="[UNK]"		)

				self.assertListEqual(tokenizer.tokenize(""		)      ,      []		)

				self.assertListEqual(tokenizer.tokenize("unwanted running"		)      ,      ["un", "##want", "##ed", "runn", "##ing"]		)

				self.assertListEqual(tokenizer.tokenize("unwantedX running"		)      ,      ["[UNK]", "runn", "##ing"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Any:
				self.assertTrue(_is_whitespace(" "		)		)
				self.assertTrue(_is_whitespace("\t"		)		)
				self.assertTrue(_is_whitespace("\r"		)		)
				self.assertTrue(_is_whitespace("\n"		)		)
				self.assertTrue(_is_whitespace("\u00A0"		)		)

				self.assertFalse(_is_whitespace("A"		)		)
				self.assertFalse(_is_whitespace("-"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     Any:
				self.assertTrue(_is_control("\u0005"		)		)

				self.assertFalse(_is_control("A"		)		)
				self.assertFalse(_is_control(" "		)		)
				self.assertFalse(_is_control("\t"		)		)
				self.assertFalse(_is_control("\r"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				self.assertTrue(_is_punctuation("-"		)		)
				self.assertTrue(_is_punctuation("$"		)		)
				self.assertTrue(_is_punctuation("`"		)		)
				self.assertTrue(_is_punctuation("."		)		)

				self.assertFalse(_is_punctuation("A"		)		)
				self.assertFalse(_is_punctuation(" "		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				UpperCAmelCase_	:					List[Any]     	=   self.get_tokenizer()
				UpperCAmelCase_	:					List[Any]     	=   self.get_rust_tokenizer()

				# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
				self.assertListEqual([tokenizer.tokenize(lowerCAmelCase_		) for t in ["Test", "\xad", "test"]]      ,      [["[UNK]"], [], ["[UNK]"]]		)

				self.assertListEqual(
				    [rust_tokenizer.tokenize(lowerCAmelCase_		) for t in ["Test", "\xad", "test"]]      ,      [["[UNK]"], [], ["[UNK]"]]		)





	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     List[Any]:
				UpperCAmelCase_	:					str     	=   self.tokenizer_class.from_pretrained("bert-base-uncased"		)

				UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.encode("sequence builders"      ,      add_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   tokenizer.encode("multi-sequence build"      ,      add_special_tokens=lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_		)
				UpperCAmelCase_	:					Tuple     	=   tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_      ,      lowerCAmelCase_		)

				assert encoded_sentence == [101] + text + [102]
				assert encoded_pair == [101] + text + [102] + text_a + [102]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     List[str]:
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
										UpperCAmelCase_	:					Optional[int]     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					int     	=   f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
										UpperCAmelCase_	:					Dict     	=   tokenizer_r.encode_plus(
										    lowerCAmelCase_      ,      return_attention_mask=lowerCAmelCase_      ,      return_token_type_ids=lowerCAmelCase_      ,      return_offsets_mapping=lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_      ,      )

										UpperCAmelCase_	:					int     	=   tokenizer_r.do_lower_case if hasattr(lowerCAmelCase_      ,      "do_lower_case"		) else False
										UpperCAmelCase_	:					Dict     	=   (
										    [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), "A"),
										        ((1, 2), ","),
										        ((3, 5), "na"),
										        ((5, 6), "##ï"),
										        ((6, 8), "##ve"),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), "Allen"),
										        ((21, 23), "##NL"),
										        ((23, 24), "##P"),
										        ((25, 33), "sentence"),
										        ((33, 34), "."),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										    if not do_lower_case
										    else [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), "a"),
										        ((1, 2), ","),
										        ((3, 8), "naive"),
										        ((9, 15), tokenizer_r.mask_token),
										        ((16, 21), "allen"),
										        ((21, 23), "##nl"),
										        ((23, 24), "##p"),
										        ((25, 33), "sentence"),
										        ((33, 34), "."),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										)

										self.assertEqual(
										    [e[1] for e in expected_results]      ,      tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]		)		)
										self.assertEqual([e[0] for e in expected_results]      ,      tokens["offset_mapping"]		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]		)		->     str:
				UpperCAmelCase_	:					Dict     	=   ["的", "人", "有"]
				UpperCAmelCase_	:					Union[str, Any]     	=   "".join(lowerCAmelCase_		)
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
										UpperCAmelCase_	:					Tuple     	=   True
										UpperCAmelCase_	:					Optional[Any]     	=   self.tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)
										UpperCAmelCase_	:					Dict     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer_p.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
										UpperCAmelCase_	:					Union[str, Any]     	=   tokenizer_r.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)

										UpperCAmelCase_	:					str     	=   tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_		)
										UpperCAmelCase_	:					Optional[Any]     	=   tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_		)

										# it is expected that each Chinese character is not preceded by "##"
										self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)
										self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)

										UpperCAmelCase_	:					int     	=   False
										UpperCAmelCase_	:					str     	=   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)
										UpperCAmelCase_	:					List[str]     	=   self.tokenizer_class.from_pretrained(lowerCAmelCase_      ,      **lowerCAmelCase_		)

										UpperCAmelCase_	:					int     	=   tokenizer_r.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)
										UpperCAmelCase_	:					int     	=   tokenizer_p.encode(lowerCAmelCase_      ,      add_special_tokens=lowerCAmelCase_		)

										UpperCAmelCase_	:					Optional[Any]     	=   tokenizer_r.convert_ids_to_tokens(lowerCAmelCase_		)
										UpperCAmelCase_	:					List[str]     	=   tokenizer_p.convert_ids_to_tokens(lowerCAmelCase_		)

										# it is expected that only the first Chinese character is not preceded by "##".
										UpperCAmelCase_	:					Dict     	=   [
										    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(lowerCAmelCase_		)
										]
										self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)
										self.assertListEqual(lowerCAmelCase_      ,      lowerCAmelCase_		)



