code: 
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  snake_case				:	Optional[int]							   =						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  @dataclass
  class    _snake_case      (  snake_case       ):
  				UpperCamelCase__					   =					42
  
  
  
  
  
  
  
  class    _snake_case      (  snake_case       ,		snake_case       ):
  				UpperCamelCase__					   =					1
  
  
  
  
  
  
  
  				@register_to_config
  				def __init__(       self		,     _a = 40		,     _a = 0.0_02		,     _a = 80.0		,     _a = 0.5		,     _a = 1.0		,     _a = 7.0		,     _a = True		,     ):
  					# standard deviation of the initial noise distribution
  					__magic_name__	:	str			     =  sigma_max
  
  					__magic_name__	:	Any			     =  np.linspace(0		,     1		,     _a	)
  					__magic_name__	:	Dict			     =  self._convert_to_karras(_a	)
  					__magic_name__	:	Dict			     =  self.sigma_to_t(_a	)
  
  					# setable values
  					__magic_name__	:	Dict			     =  None
  					__magic_name__	:	Optional[Any]			     =  torch.from_numpy(_a	)
  					__magic_name__	:	Any			     =  torch.from_numpy(_a	)
  					__magic_name__	:	Union[str, Any]			     =  False
  					__magic_name__	:	int			     =  False
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=None	):
  					if schedule_timesteps is None:
  						__magic_name__	:	Optional[Any]			     =  self.timesteps
  
  					__magic_name__	:	Optional[int]			     =  (schedule_timesteps == timestep).nonzero()
  					return indices.item()
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  					# Get sigma corresponding to timestep
  					if isinstance(_a		,     torch.Tensor	):
  						__magic_name__	:	Any			     =  timestep.to(self.timesteps.device	)
  					__magic_name__	:	str			     =  self.index_for_timestep(_a	)
  					__magic_name__	:	List[str]			     =  self.sigmas[step_idx]
  
  					__magic_name__	:	Optional[Any]			     =  sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
  					__magic_name__	:	Dict			     =  True
  					return sample
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  					if not isinstance(_a		,     np.ndarray	):
  						__magic_name__	:	str			     =  np.array(_a		,     dtype=np.floataa	)
  
  					__magic_name__	:	Tuple			     =  1_000 * 0.25 * np.log(sigmas + 1e-44	)
  
  					return timesteps
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a = None		,     _a = None		,     _a = None		,     ):
  					if num_inference_steps is None and timesteps is None:
  						raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."	)
  
  					if num_inference_steps is not None and timesteps is not None:
  						raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."	)
  
  					# Follow DDPMScheduler custom timesteps logic
  					if timesteps is not None:
  						for i in range(1		,     len(_a	)	):
  							if timesteps[i] >= timesteps[i - 1]:
  								raise ValueError("`timesteps` must be in descending order."	)
  
  						if timesteps[0] >= self.config.num_train_timesteps:
  							raise ValueError(
  							    f'''`timesteps` must start before `self.config.train_timesteps`:'''
  							    f''' {self.config.num_train_timesteps}.'''	)
  
  						__magic_name__	:	Optional[int]			     =  np.array(_a		,     dtype=np.intaa	)
  						__magic_name__	:	Optional[int]			     =  True
  					else:
  						if num_inference_steps > self.config.num_train_timesteps:
  							raise ValueError(
  							    f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
  							    f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
  							    f''' maximal {self.config.num_train_timesteps} timesteps.'''	)
  
  						__magic_name__	:	Tuple			     =  num_inference_steps
  
  						__magic_name__	:	Optional[Any]			     =  self.config.num_train_timesteps // self.num_inference_steps
  						__magic_name__	:	Optional[Any]			     =  (np.arange(0		,     _a	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
  						__magic_name__	:	Dict			     =  False
  
  					# Map timesteps to Karras sigmas directly for multistep sampling
  					# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
  					__magic_name__	:	Optional[int]			     =  self.config.num_train_timesteps
  					__magic_name__	:	Optional[int]			     =  timesteps[::-1].copy()
  					__magic_name__	:	str			     =  ramp / (num_train_timesteps - 1)
  					__magic_name__	:	Union[str, Any]			     =  self._convert_to_karras(_a	)
  					__magic_name__	:	List[str]			     =  self.sigma_to_t(_a	)
  
  					__magic_name__	:	int			     =  np.concatenate([sigmas, [self.sigma_min]]	).astype(np.floataa	)
  					__magic_name__	:	Union[str, Any]			     =  torch.from_numpy(_a	).to(device=_a	)
  
  					if str(_a	).startswith("mps"	):
  						# mps does not support float64
  						__magic_name__	:	str			     =  torch.from_numpy(_a	).to(_a		,     dtype=torch.floataa	)
  					else:
  						__magic_name__	:	Dict			     =  torch.from_numpy(_a	).to(device=_a	)
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  					__magic_name__	:	float			     =  self.config.sigma_min
  					__magic_name__	:	float			     =  self.config.sigma_max
  
  					__magic_name__	:	int			     =  self.config.rho
  					__magic_name__	:	List[Any]			     =  sigma_min ** (1 / rho)
  					__magic_name__	:	List[str]			     =  sigma_max ** (1 / rho)
  					__magic_name__	:	List[Any]			     =  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
  					return sigmas
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  					__magic_name__	:	Tuple			     =  self.config.sigma_data
  
  					__magic_name__	:	List[Any]			     =  sigma_data**2 / (sigma**2 + sigma_data**2)
  					__magic_name__	:	Optional[int]			     =  sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  					return c_skip, c_out
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  					__magic_name__	:	Tuple			     =  self.config.sigma_min
  					__magic_name__	:	List[Any]			     =  self.config.sigma_data
  
  					__magic_name__	:	Union[str, Any]			     =  sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
  					__magic_name__	:	str			     =  (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
  					return c_skip, c_out
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a = None		,     _a = True		,     ):
  
  					if (
  					    isinstance(_a		,     _a	)
  					    or isinstance(_a		,     torch.IntTensor	)
  					    or isinstance(_a		,     torch.LongTensor	)
  					):
  						raise ValueError(
  						    (
  						        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
  						        f''' `{self.__class__}.step()` is not supported. Make sure to pass'''
  						        " one of the `scheduler.timesteps` as a timestep."
  						    )		,     )
  
  					if not self.is_scale_input_called:
  						logger.warning(
  						    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
  						    "See `StableDiffusionPipeline` for a usage example."	)
  
  					if isinstance(_a		,     torch.Tensor	):
  						__magic_name__	:	Tuple			     =  timestep.to(self.timesteps.device	)
  
  					__magic_name__	:	Dict			     =  self.config.sigma_min
  					__magic_name__	:	Union[str, Any]			     =  self.config.sigma_max
  
  					__magic_name__	:	str			     =  self.index_for_timestep(_a	)
  
  					# sigma_next corresponds to next_t in original implementation
  					__magic_name__	:	List[Any]			     =  self.sigmas[step_index]
  					if step_index + 1 < self.config.num_train_timesteps:
  						__magic_name__	:	Tuple			     =  self.sigmas[step_index + 1]
  					else:
  						# Set sigma_next to sigma_min
  						__magic_name__	:	Dict			     =  self.sigmas[-1]
  
  					# Get scalings for boundary conditions
  					__magic_name__	:	List[Any]			     =  self.get_scalings_for_boundary_condition(_a	)
  
  					# 1. Denoise model output using boundary conditions
  					__magic_name__	:	Optional[int]			     =  c_out * model_output + c_skip * sample
  					if self.config.clip_denoised:
  						__magic_name__	:	Optional[int]			     =  denoised.clamp(-1		,     1	)
  
  					# 2. Sample z ~ N(0, s_noise^2 * I)
  					# Noise is not used for onestep sampling.
  					if len(self.timesteps	) > 1:
  						__magic_name__	:	Union[str, Any]			     =  randn_tensor(
  						    model_output.shape		,     dtype=model_output.dtype		,     device=model_output.device		,     generator=_a	)
  					else:
  						__magic_name__	:	Dict			     =  torch.zeros_like(_a	)
  					__magic_name__	:	Union[str, Any]			     =  noise * self.config.s_noise
  
  					__magic_name__	:	Dict			     =  sigma_next.clamp(min=_a		,     max=_a	)
  
  					# 3. Return noisy sample
  					# tau = sigma_hat, eps = sigma_min
  					__magic_name__	:	Union[str, Any]			     =  denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
  					if not return_dict:
  						return (prev_sample,)
  
  					return CMStochasticIterativeSchedulerOutput(prev_sample=_a	)
  
  
  
  
  
  
  
  				def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     ):
  					# Make sure sigmas and timesteps have the same device and dtype as original_samples
  					__magic_name__	:	str			     =  self.sigmas.to(device=original_samples.device		,     dtype=original_samples.dtype	)
  					if original_samples.device.type == "mps" and torch.is_floating_point(_a	):
  						# mps does not support float64
  						__magic_name__	:	Any			     =  self.timesteps.to(original_samples.device		,     dtype=torch.floataa	)
  						__magic_name__	:	Dict			     =  timesteps.to(original_samples.device		,     dtype=torch.floataa	)
  					else:
  						__magic_name__	:	Any			     =  self.timesteps.to(original_samples.device	)
  						__magic_name__	:	Optional[int]			     =  timesteps.to(original_samples.device	)
  
  					__magic_name__	:	Optional[Any]			     =  [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
  					__magic_name__	:	str			     =  sigmas[step_indices].flatten()
  					while len(sigma.shape	) < len(original_samples.shape	):
  						__magic_name__	:	int			     =  sigma.unsqueeze(-1	)
  
  					__magic_name__	:	Optional[int]			     =  original_samples + noise * sigma
  					return noisy_samples
  
  
  
  
  
  
  
  				def __len__(       self	):
  					return self.config.num_train_timesteps
  
style_context: 
  import os
  import unittest
  
  from transformers import ElectraTokenizerFast
  from transformers.models.electra.tokenization_electra import (
      VOCAB_FILES_NAMES,
      BasicTokenizer,
      ElectraTokenizer,
      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    _snake_case      (  snake_case       ,		unittest.TestCase       ):
  			UpperCamelCase__					   =					ElectraTokenizer
  			UpperCamelCase__					   =					ElectraTokenizerFast
  			UpperCamelCase__					   =					True
  			UpperCamelCase__					   =					True
  			UpperCamelCase__					   =					filter_non_english
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				super().setUp()
  
  				__magic_name__	:	Optional[Any]			     =  [
  				    "[UNK]",
  				    "[CLS]",
  				    "[SEP]",
  				    "[PAD]",
  				    "[MASK]",
  				    "want",
  				    "##want",
  				    "##ed",
  				    "wa",
  				    "un",
  				    "runn",
  				    "##ing",
  				    ",",
  				    "low",
  				    "lowest",
  				]
  				__magic_name__	:	Dict			     =  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		,     _a	):
  				__magic_name__	:	Dict			     =  "UNwant\u00E9d,running"
  				__magic_name__	:	int			     =  "unwanted, running"
  				return input_text, output_text
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Union[str, Any]			     =  self.tokenizer_class(self.vocab_file	)
  
  				__magic_name__	:	Optional[int]			     =  tokenizer.tokenize("UNwant\u00E9d,running"	)
  				self.assertListEqual(_a		,     ["un", "##want", "##ed", ",", "runn", "##ing"]	)
  				self.assertListEqual(tokenizer.convert_tokens_to_ids(_a	)		,     [9, 6, 7, 12, 10, 11]	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				if not self.test_rust_tokenizer:
  					return
  
  				__magic_name__	:	str			     =  self.get_tokenizer()
  				__magic_name__	:	Optional[int]			     =  self.get_rust_tokenizer()
  
  				__magic_name__	:	Tuple			     =  "UNwant\u00E9d,running"
  
  				__magic_name__	:	List[Any]			     =  tokenizer.tokenize(_a	)
  				__magic_name__	:	str			     =  rust_tokenizer.tokenize(_a	)
  				self.assertListEqual(_a		,     _a	)
  
  				__magic_name__	:	int			     =  tokenizer.encode(_a		,     add_special_tokens=_a	)
  				__magic_name__	:	Union[str, Any]			     =  rust_tokenizer.encode(_a		,     add_special_tokens=_a	)
  				self.assertListEqual(_a		,     _a	)
  
  				__magic_name__	:	Tuple			     =  self.get_rust_tokenizer()
  				__magic_name__	:	Union[str, Any]			     =  tokenizer.encode(_a	)
  				__magic_name__	:	Any			     =  rust_tokenizer.encode(_a	)
  				self.assertListEqual(_a		,     _a	)
  
  				# With lower casing
  				__magic_name__	:	Optional[Any]			     =  self.get_tokenizer(do_lower_case=_a	)
  				__magic_name__	:	Union[str, Any]			     =  self.get_rust_tokenizer(do_lower_case=_a	)
  
  				__magic_name__	:	Tuple			     =  "UNwant\u00E9d,running"
  
  				__magic_name__	:	Optional[Any]			     =  tokenizer.tokenize(_a	)
  				__magic_name__	:	List[Any]			     =  rust_tokenizer.tokenize(_a	)
  				self.assertListEqual(_a		,     _a	)
  
  				__magic_name__	:	Any			     =  tokenizer.encode(_a		,     add_special_tokens=_a	)
  				__magic_name__	:	List[str]			     =  rust_tokenizer.encode(_a		,     add_special_tokens=_a	)
  				self.assertListEqual(_a		,     _a	)
  
  				__magic_name__	:	Dict			     =  self.get_rust_tokenizer()
  				__magic_name__	:	Optional[int]			     =  tokenizer.encode(_a	)
  				__magic_name__	:	Any			     =  rust_tokenizer.encode(_a	)
  				self.assertListEqual(_a		,     _a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Tuple			     =  BasicTokenizer()
  
  				self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"	)		,     ["ah", "\u535A", "\u63A8", "zz"]	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Optional[Any]			     =  BasicTokenizer(do_lower_case=_a	)
  
  				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	):
  				__magic_name__	:	Union[str, Any]			     =  BasicTokenizer(do_lower_case=_a		,     strip_accents=_a	)
  
  				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	):
  				__magic_name__	:	str			     =  BasicTokenizer(do_lower_case=_a		,     strip_accents=_a	)
  
  				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	):
  				__magic_name__	:	Optional[int]			     =  BasicTokenizer(do_lower_case=_a	)
  
  				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	):
  				__magic_name__	:	Union[str, Any]			     =  BasicTokenizer(do_lower_case=_a	)
  
  				self.assertListEqual(
  				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "	)		,     ["HeLLo", "!", "how", "Are", "yoU", "?"]	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Dict			     =  BasicTokenizer(do_lower_case=_a		,     strip_accents=_a	)
  
  				self.assertListEqual(
  				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)		,     ["HäLLo", "!", "how", "Are", "yoU", "?"]	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Any			     =  BasicTokenizer(do_lower_case=_a		,     strip_accents=_a	)
  
  				self.assertListEqual(
  				    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "	)		,     ["HaLLo", "!", "how", "Are", "yoU", "?"]	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Any			     =  BasicTokenizer(do_lower_case=_a		,     never_split=["[UNK]"]	)
  
  				self.assertListEqual(
  				    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"	)		,     ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	Dict			     =  ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
  
  				__magic_name__	:	Optional[int]			     =  {}
  				for i, token in enumerate(_a	):
  					__magic_name__	:	Tuple			     =  i
  				__magic_name__	:	Tuple			     =  WordpieceTokenizer(vocab=_a		,     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	):
  				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	):
  				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	):
  				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	):
  				__magic_name__	:	List[Any]			     =  self.get_tokenizer()
  				__magic_name__	:	List[str]			     =  self.get_rust_tokenizer()
  
  				# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
  				self.assertListEqual([tokenizer.tokenize(_a	) for t in ["Test", "\xad", "test"]]		,     [["[UNK]"], [], ["[UNK]"]]	)
  
  				self.assertListEqual(
  				    [rust_tokenizer.tokenize(_a	) for t in ["Test", "\xad", "test"]]		,     [["[UNK]"], [], ["[UNK]"]]	)
  
  
  
  
  
  
  
  			@slow
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				__magic_name__	:	int			     =  self.tokenizer_class.from_pretrained("google/electra-base-discriminator"	)
  
  				__magic_name__	:	List[Any]			     =  tokenizer.encode("sequence builders"		,     add_special_tokens=_a	)
  				__magic_name__	:	List[Any]			     =  tokenizer.encode("multi-sequence build"		,     add_special_tokens=_a	)
  
  				__magic_name__	:	Optional[int]			     =  tokenizer.build_inputs_with_special_tokens(_a	)
  				__magic_name__	:	Tuple			     =  tokenizer.build_inputs_with_special_tokens(_a		,     _a	)
  
  				assert encoded_sentence == [101] + text + [102]
  				assert encoded_pair == [101] + text + [102] + text_a + [102]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  					with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
  						__magic_name__	:	str			     =  self.rust_tokenizer_class.from_pretrained(_a		,     **_a	)
  
  						__magic_name__	:	Optional[Any]			     =  f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
  						__magic_name__	:	Any			     =  tokenizer_r.encode_plus(
  						    _a		,     return_attention_mask=_a		,     return_token_type_ids=_a		,     return_offsets_mapping=_a		,     add_special_tokens=_a		,     )
  
  						__magic_name__	:	Any			     =  tokenizer_r.do_lower_case if hasattr(_a		,     "do_lower_case"	) else False
  						__magic_name__	:	int			     =  (
  						    [
  						        ((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	):
  				__magic_name__	:	str			     =  ["的", "人", "有"]
  				__magic_name__	:	List[str]			     =  "".join(_a	)
  				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  					with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
  						__magic_name__	:	Optional[int]			     =  True
  						__magic_name__	:	Optional[int]			     =  self.tokenizer_class.from_pretrained(_a		,     **_a	)
  						__magic_name__	:	Union[str, Any]			     =  self.rust_tokenizer_class.from_pretrained(_a		,     **_a	)
  
  						__magic_name__	:	Tuple			     =  tokenizer_p.encode(_a		,     add_special_tokens=_a	)
  						__magic_name__	:	List[Any]			     =  tokenizer_r.encode(_a		,     add_special_tokens=_a	)
  
  						__magic_name__	:	List[str]			     =  tokenizer_r.convert_ids_to_tokens(_a	)
  						__magic_name__	:	List[Any]			     =  tokenizer_p.convert_ids_to_tokens(_a	)
  
  						# it is expected that each Chinese character is not preceded by "##"
  						self.assertListEqual(_a		,     _a	)
  						self.assertListEqual(_a		,     _a	)
  
  						__magic_name__	:	Tuple			     =  False
  						__magic_name__	:	Tuple			     =  self.rust_tokenizer_class.from_pretrained(_a		,     **_a	)
  						__magic_name__	:	str			     =  self.tokenizer_class.from_pretrained(_a		,     **_a	)
  
  						__magic_name__	:	Dict			     =  tokenizer_r.encode(_a		,     add_special_tokens=_a	)
  						__magic_name__	:	Dict			     =  tokenizer_p.encode(_a		,     add_special_tokens=_a	)
  
  						__magic_name__	:	List[str]			     =  tokenizer_r.convert_ids_to_tokens(_a	)
  						__magic_name__	:	List[Any]			     =  tokenizer_p.convert_ids_to_tokens(_a	)
  
  						# it is expected that only the first Chinese character is not preceded by "##".
  						__magic_name__	:	Tuple			     =  [
  						    f'''##{token}''' if idx != 0 else token for idx, token in enumerate(_a	)
  						]
  						self.assertListEqual(_a		,     _a	)
  						self.assertListEqual(_a		,     _a	)
  