"""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      :		Tuple           =   8





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Tuple=BITS	)       ->      List[str]:
  _lowerCAmelCase						:       Optional[Any]  			=     x.device

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

  _lowerCAmelCase						:       Union[str, Any]  			=     2 ** torch.arange(bits - 1			,-1			,-1			,device=__lowerCAmelCase	)
  _lowerCAmelCase						:       Tuple  			=     rearrange(__lowerCAmelCase			,"""d -> d 1 1"""	)
  _lowerCAmelCase						:       List[str]  			=     rearrange(__lowerCAmelCase			,"""b c h w -> b c 1 h w"""	)

  _lowerCAmelCase						:       List[Any]  			=     ((x & mask) != 0).float()
  _lowerCAmelCase						:       Union[str, Any]  			=     rearrange(__lowerCAmelCase			,"""b c d h w -> b (c d) h w"""	)
  _lowerCAmelCase						:       Dict  			=     bits * 2 - 1
  return bits





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       List[str]=BITS	)       ->      Dict:
  _lowerCAmelCase						:       Union[str, Any]  			=     x.device

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

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





def 							SCREAMING_SNAKE_CASE     (			self						:       str			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       List[str] = 0.0			,_lowerCamelCase						:       List[Any] = True			,_lowerCamelCase						:       int=None			,_lowerCamelCase						:       Dict = True			,)       ->      Union[DDIMSchedulerOutput, Tuple]:

  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)
  _lowerCAmelCase						:       Tuple  			=     timestep - self.config.num_train_timesteps // self.num_inference_steps

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

  _lowerCAmelCase						:       Dict  			=     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
  _lowerCAmelCase						:       Optional[Any]  			=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  _lowerCAmelCase						:       int  			=     self.bit_scale
  if self.config.clip_sample:
    _lowerCAmelCase						:       str  			=     torch.clamp(__lowerCAmelCase			,-scale			,__lowerCAmelCase	)

  # 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)
  _lowerCAmelCase						:       Any  			=     self._get_variance(__lowerCAmelCase			,__lowerCAmelCase	)
  _lowerCAmelCase						:       Optional[int]  			=     eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    _lowerCAmelCase						:       str  			=     (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
  _lowerCAmelCase						:       List[str]  			=     (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
  _lowerCAmelCase						:       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
    _lowerCAmelCase						:       List[str]  			=     model_output.device if torch.is_tensor(__lowerCAmelCase	) else '''cpu'''
    _lowerCAmelCase						:       Tuple  			=     torch.randn(model_output.shape			,dtype=model_output.dtype			,generator=__lowerCAmelCase	).to(__lowerCAmelCase	)
    _lowerCAmelCase						:       Any  			=     self._get_variance(__lowerCAmelCase			,__lowerCAmelCase	) ** 0.5 * eta * noise

    _lowerCAmelCase						:       int  			=     prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase			,pred_original_sample=__lowerCAmelCase	)





def 							SCREAMING_SNAKE_CASE     (			self						:       str			,_lowerCamelCase						:       str			,_lowerCamelCase						:       str			,_lowerCamelCase						:       Optional[int]			,_lowerCamelCase						:       Tuple="epsilon"			,_lowerCamelCase						:       List[Any]=None			,_lowerCamelCase						:       str = True			,)       ->      Union[DDPMSchedulerOutput, Tuple]:
  _lowerCAmelCase						:       Dict  			=     timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    _lowerCAmelCase						:       List[Any]  			=     torch.split(__lowerCAmelCase			,sample.shape[1]			,dim=1	)
  else:
    _lowerCAmelCase						:       Optional[int]  			=     None

  # 1. compute alphas, betas
  _lowerCAmelCase						:       Any  			=     self.alphas_cumprod[t]
  _lowerCAmelCase						:       Dict  			=     self.alphas_cumprod[t - 1] if t > 0 else self.one
  _lowerCAmelCase						:       str  			=     1 - alpha_prod_t
  _lowerCAmelCase						:       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":
    _lowerCAmelCase						:       int  			=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    _lowerCAmelCase						:       Dict  			=     model_output
  else:
    raise ValueError(f"Unsupported prediction_type {prediction_type}."	)

  # 3. Clip "predicted x_0"
  _lowerCAmelCase						:       Tuple  			=     self.bit_scale
  if self.config.clip_sample:
    _lowerCAmelCase						:       List[str]  			=     torch.clamp(__lowerCAmelCase			,-scale			,__lowerCAmelCase	)

  # 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
  _lowerCAmelCase						:       Tuple  			=     (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  _lowerCAmelCase						:       Tuple  			=     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
  _lowerCAmelCase						:       int  			=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

  _lowerCAmelCase						:       Optional[Any]  			=     pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=__lowerCAmelCase			,pred_original_sample=__lowerCAmelCase	)






class 						__A							( __lowerCamelCase		):






     def __init__(		self				,     a__				,     a__				,     a__ = 1.0				,     ):
       super().__init__()
       _lowerCAmelCase						:       List[str]  			=     bit_scale
       _lowerCAmelCase						:       str  			=     (
           ddim_bit_scheduler_step if isinstance(__lowercase				,     __lowercase  ) else ddpm_bit_scheduler_step
       )

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






     @torch.no_grad()
     def __call__(		self				,     a__ = 256				,     a__ = 256				,     a__ = 50				,     a__ = None				,     a__ = 1				,     a__ = "pil"				,     a__ = True				,     **a__				,     ):
       _lowerCAmelCase						:       Optional[int]  			=     torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)				,     generator=__lowercase				,     )
       _lowerCAmelCase						:       List[str]  			=     decimal_to_bits(__lowercase  ) * self.bit_scale
       _lowerCAmelCase						:       str  			=     latents.to(self.device  )

       self.scheduler.set_timesteps(__lowercase  )

       for t in self.progress_bar(self.scheduler.timesteps  ):
         # predict the noise residual
         _lowerCAmelCase						:       Optional[Any]  			=     self.unet(__lowercase				,     __lowercase  ).sample

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

       _lowerCAmelCase						:       Optional[Any]  			=     bits_to_decimal(__lowercase  )

       if output_type == "pil":
         _lowerCAmelCase						:       Optional[int]  			=     self.numpy_to_pil(__lowercase  )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=__lowercase  )









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       a							(			__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    int											=					BertTokenizer
		__lowerCAmelCase	:    int											=					BertTokenizerFast
		__lowerCAmelCase	:    int											=					True
		__lowerCAmelCase	:    List[Any]											=					True
		__lowerCAmelCase	:    Any											=					filter_non_english



		def 	__lowerCamelCase			(     self     :str  ):
						super().setUp()

						snake_case__	:      str     		=	[
						    '''[UNK]''',
						    '''[CLS]''',
						    '''[SEP]''',
						    '''[PAD]''',
						    '''[MASK]''',
						    '''want''',
						    '''##want''',
						    '''##ed''',
						    '''wa''',
						    '''un''',
						    '''runn''',
						    '''##ing''',
						    ''',''',
						    '''low''',
						    '''lowest''',
						]
						snake_case__	:      str     		=	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 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :str  ):
						snake_case__	:      List[Any]     		=	'''UNwant\u00E9d,running'''
						snake_case__	:      str     		=	'''unwanted, running'''
						return input_text, output_text



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						snake_case__	:      str     		=	self.tokenizer_class(self.vocab_file  )

						snake_case__	:      str     		=	tokenizer.tokenize('''UNwant\u00E9d,running'''  )
						self.assertListEqual(__lowercase   ,['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase  )   ,[9, 6, 7, 1_2, 1_0, 1_1]  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						if not self.test_rust_tokenizer:
										return

						snake_case__	:      str     		=	self.get_tokenizer()
						snake_case__	:      Dict     		=	self.get_rust_tokenizer()

						snake_case__	:      List[Any]     		=	'''UNwant\u00E9d,running'''

						snake_case__	:      Dict     		=	tokenizer.tokenize(__lowercase  )
						snake_case__	:      int     		=	rust_tokenizer.tokenize(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      List[str]     		=	tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						snake_case__	:      List[str]     		=	rust_tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      Optional[Any]     		=	self.get_rust_tokenizer()
						snake_case__	:      List[Any]     		=	tokenizer.encode(__lowercase  )
						snake_case__	:      Dict     		=	rust_tokenizer.encode(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						# With lower casing
						snake_case__	:      List[Any]     		=	self.get_tokenizer(do_lower_case=__lowercase  )
						snake_case__	:      Optional[int]     		=	self.get_rust_tokenizer(do_lower_case=__lowercase  )

						snake_case__	:      int     		=	'''UNwant\u00E9d,running'''

						snake_case__	:      List[str]     		=	tokenizer.tokenize(__lowercase  )
						snake_case__	:      Union[str, Any]     		=	rust_tokenizer.tokenize(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      int     		=	tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						snake_case__	:      List[str]     		=	rust_tokenizer.encode(__lowercase   ,add_special_tokens=__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )

						snake_case__	:      Tuple     		=	self.get_rust_tokenizer()
						snake_case__	:      int     		=	tokenizer.encode(__lowercase  )
						snake_case__	:      Dict     		=	rust_tokenizer.encode(__lowercase  )
						self.assertListEqual(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      Optional[Any]     		=	BasicTokenizer()

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



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Dict     		=	BasicTokenizer(do_lower_case=__lowercase  )

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



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      Optional[int]     		=	BasicTokenizer(do_lower_case=__lowercase   ,strip_accents=__lowercase  )

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



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Union[str, Any]     		=	BasicTokenizer(do_lower_case=__lowercase   ,strip_accents=__lowercase  )

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



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      Union[str, Any]     		=	BasicTokenizer(do_lower_case=__lowercase  )

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



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      str     		=	BasicTokenizer(do_lower_case=__lowercase  )

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



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      Any     		=	BasicTokenizer(do_lower_case=__lowercase   ,strip_accents=__lowercase  )

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



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      str     		=	BasicTokenizer(do_lower_case=__lowercase   ,strip_accents=__lowercase  )

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



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      List[Any]     		=	BasicTokenizer(do_lower_case=__lowercase   ,never_split=['''[UNK]''']  )

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



		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__	:      List[str]     		=	BasicTokenizer()
						snake_case__	:      Any     		=	'''a\n\'ll !!to?\'d of, can\'t.'''
						snake_case__	:      Optional[int]     		=	['''a''', '''\'''', '''ll''', '''!''', '''!''', '''to''', '''?''', '''\'''', '''d''', '''of''', ''',''', '''can''', '''\'''', '''t''', '''.''']
						self.assertListEqual(tokenizer.tokenize(__lowercase  )   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      Any     		=	['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

						snake_case__	:      Optional[Any]     		=	{}
						for i, token in enumerate(__lowercase  ):
										snake_case__	:      Dict     		=	i
						snake_case__	:      int     		=	WordpieceTokenizer(vocab=__lowercase   ,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 	__lowerCamelCase			(     self     :List[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 	__lowerCamelCase			(     self     :Union[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 	__lowerCamelCase			(     self     :str  ):
						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 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      str     		=	self.get_tokenizer()
						snake_case__	:      List[str]     		=	self.get_rust_tokenizer()

						# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
						self.assertListEqual([tokenizer.tokenize(__lowercase  ) for t in ['''Test''', '''\xad''', '''test''']]   ,[['''[UNK]'''], [], ['''[UNK]''']]  )

						self.assertListEqual(
						    [rust_tokenizer.tokenize(__lowercase  ) for t in ['''Test''', '''\xad''', '''test''']]   ,[['''[UNK]'''], [], ['''[UNK]''']]  )



		@slow
		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      List[str]     		=	self.tokenizer_class.from_pretrained('''bert-base-uncased'''  )

						snake_case__	:      Union[str, Any]     		=	tokenizer.encode('''sequence builders'''   ,add_special_tokens=__lowercase  )
						snake_case__	:      str     		=	tokenizer.encode('''multi-sequence build'''   ,add_special_tokens=__lowercase  )

						snake_case__	:      Tuple     		=	tokenizer.build_inputs_with_special_tokens(__lowercase  )
						snake_case__	:      int     		=	tokenizer.build_inputs_with_special_tokens(__lowercase   ,__lowercase  )

						assert encoded_sentence == [1_0_1] + text + [1_0_2]
						assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
														snake_case__	:      str     		=	self.rust_tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )

														snake_case__	:      List[str]     		=	F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
														snake_case__	:      List[Any]     		=	tokenizer_r.encode_plus(
														    __lowercase   ,return_attention_mask=__lowercase   ,return_token_type_ids=__lowercase   ,return_offsets_mapping=__lowercase   ,add_special_tokens=__lowercase   ,)

														snake_case__	:      List[str]     		=	tokenizer_r.do_lower_case if hasattr(__lowercase   ,'''do_lower_case'''  ) else False
														snake_case__	:      Optional[int]     		=	(
														    [
														        ((0, 0), tokenizer_r.cls_token),
														        ((0, 1), '''A'''),
														        ((1, 2), ''','''),
														        ((3, 5), '''na'''),
														        ((5, 6), '''##ï'''),
														        ((6, 8), '''##ve'''),
														        ((9, 1_5), tokenizer_r.mask_token),
														        ((1_6, 2_1), '''Allen'''),
														        ((2_1, 2_3), '''##NL'''),
														        ((2_3, 2_4), '''##P'''),
														        ((2_5, 3_3), '''sentence'''),
														        ((3_3, 3_4), '''.'''),
														        ((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, 1_5), tokenizer_r.mask_token),
														        ((1_6, 2_1), '''allen'''),
														        ((2_1, 2_3), '''##nl'''),
														        ((2_3, 2_4), '''##p'''),
														        ((2_5, 3_3), '''sentence'''),
														        ((3_3, 3_4), '''.'''),
														        ((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 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      str     		=	['''的''', '''人''', '''有''']
						snake_case__	:      Optional[int]     		=	''''''.join(__lowercase  )
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
														snake_case__	:      Optional[Any]     		=	True
														snake_case__	:      int     		=	self.tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )
														snake_case__	:      Tuple     		=	self.rust_tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )

														snake_case__	:      Union[str, Any]     		=	tokenizer_p.encode(__lowercase   ,add_special_tokens=__lowercase  )
														snake_case__	:      List[str]     		=	tokenizer_r.encode(__lowercase   ,add_special_tokens=__lowercase  )

														snake_case__	:      List[str]     		=	tokenizer_r.convert_ids_to_tokens(__lowercase  )
														snake_case__	:      Optional[Any]     		=	tokenizer_p.convert_ids_to_tokens(__lowercase  )

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

														snake_case__	:      str     		=	False
														snake_case__	:      Dict     		=	self.rust_tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )
														snake_case__	:      int     		=	self.tokenizer_class.from_pretrained(__lowercase   ,**__lowercase  )

														snake_case__	:      int     		=	tokenizer_r.encode(__lowercase   ,add_special_tokens=__lowercase  )
														snake_case__	:      Tuple     		=	tokenizer_p.encode(__lowercase   ,add_special_tokens=__lowercase  )

														snake_case__	:      List[Any]     		=	tokenizer_r.convert_ids_to_tokens(__lowercase  )
														snake_case__	:      Union[str, Any]     		=	tokenizer_p.convert_ids_to_tokens(__lowercase  )

														# it is expected that only the first Chinese character is not preceded by "##".
														snake_case__	:      Optional[int]     		=	[
														    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(__lowercase  )
														]
														self.assertListEqual(__lowercase   ,__lowercase  )
														self.assertListEqual(__lowercase   ,__lowercase  )

