






'''simple docstring'''







import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class _lowercase      (							_UpperCAmelCase						):
							a							 =			42
							a							 =			None





def 					SCREAMING_SNAKE_CASE_			(UpperCamelCase							,     UpperCamelCase=0.999							,     UpperCamelCase="cosine"							,     ) ->			Optional[Any]:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCamelCase ):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(UpperCamelCase ):
																					return math.exp(t * -12.0 )

							else:
														raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )

							lowerCamelCase__   :      int			    =			[]
							for i in range(UpperCAmelCase__ ):
														lowerCamelCase__   :      Tuple			    =			i / num_diffusion_timesteps
														lowerCamelCase__   :      Union[str, Any]			    =			(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(UpperCAmelCase__ ) / alpha_bar_fn(UpperCAmelCase__ )							,     UpperCAmelCase__ ) )
							return torch.tensor(UpperCAmelCase__							,     dtype=torch.floataa )



class _lowercase      (							_UpperCAmelCase							, _UpperCAmelCase						):
							a							 =			[e.name for e in KarrasDiffusionSchedulers]
							a							 =			1




							@register_to_config
							def __init__(				self:							List[str]     ,				UpperCamelCase__:							Union[str, Any] = 1_000     ,				UpperCamelCase__:							int = 0.0_001     ,				UpperCamelCase__:							List[Any] = 0.02     ,				UpperCamelCase__:							Optional[int] = "linear"     ,				UpperCamelCase__:							Optional[int] = None     ,				UpperCamelCase__:							Optional[int] = False     ,				UpperCamelCase__:							Tuple = "epsilon"     ,				UpperCamelCase__:							str = "linspace"     ,				UpperCamelCase__:							Optional[Any] = 0     ,				):
														if trained_betas is not None:
																					lowerCamelCase__   :      Tuple			    =			torch.tensor(__UpperCAmelCase     ,				dtype=torch.floataa	)
														elif beta_schedule == "linear":
																					lowerCamelCase__   :      Union[str, Any]			    =			torch.linspace(__UpperCAmelCase     ,				__UpperCAmelCase     ,				__UpperCAmelCase     ,				dtype=torch.floataa	)
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					lowerCamelCase__   :      str			    =			(
																					    torch.linspace(beta_start**0.5     ,				beta_end**0.5     ,				__UpperCAmelCase     ,				dtype=torch.floataa	) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					lowerCamelCase__   :      Optional[int]			    =			betas_for_alpha_bar(__UpperCAmelCase	)
														else:
																					raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

														lowerCamelCase__   :      Tuple			    =			1.0 - self.betas
														lowerCamelCase__   :      List[str]			    =			torch.cumprod(self.alphas     ,				dim=0	)

														lowerCamelCase__   :      List[Any]			    =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
														lowerCamelCase__   :      Any			    =			np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
														lowerCamelCase__   :      Any			    =			torch.from_numpy(__UpperCAmelCase	)

														# setable values
														lowerCamelCase__   :      int			    =			None
														lowerCamelCase__   :      Optional[Any]			    =			use_karras_sigmas
														self.set_timesteps(__UpperCAmelCase     ,				__UpperCAmelCase	)
														lowerCamelCase__   :      Any			    =			[]
														lowerCamelCase__   :      Union[str, Any]			    =			False




							@property
							def 						lowerCamelCase_							(				self:							Dict	):
														# standard deviation of the initial noise distribution
														if self.config.timestep_spacing in ["linspace", "trailing"]:
																					return self.sigmas.max()

														return (self.sigmas.max() ** 2 + 1) ** 0.5




							def 						lowerCamelCase_							(				self:							Tuple     ,				UpperCamelCase__:							List[Any]     ,				UpperCamelCase__:							int	):
														if isinstance(__UpperCAmelCase     ,				torch.Tensor	):
																					lowerCamelCase__   :      int			    =			timestep.to(self.timesteps.device	)
														lowerCamelCase__   :      Union[str, Any]			    =			(self.timesteps == timestep).nonzero().item()
														lowerCamelCase__   :      List[str]			    =			self.sigmas[step_index]
														lowerCamelCase__   :      Tuple			    =			sample / ((sigma**2 + 1) ** 0.5)
														lowerCamelCase__   :      Dict			    =			True
														return sample




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							List[Any]     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							Dict	):

														def lms_derivative(UpperCamelCase__:							str	):
																					lowerCamelCase__   :      Dict			    =			1.0
																					for k in range(__UpperCAmelCase	):
																												if current_order == k:
																																			continue
																												prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																					return prod

														lowerCamelCase__   :      Union[str, Any]			    =			integrate.quad(__UpperCAmelCase     ,				self.sigmas[t]     ,				self.sigmas[t + 1]     ,				epsrel=1e-4	)[0]

														return integrated_coeff




							def 						lowerCamelCase_							(				self:							Dict     ,				UpperCamelCase__:							Tuple     ,				UpperCamelCase__:							str = None	):
														lowerCamelCase__   :      List[str]			    =			num_inference_steps

														# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
														if self.config.timestep_spacing == "linspace":
																					lowerCamelCase__   :      Union[str, Any]			    =			np.linspace(0     ,				self.config.num_train_timesteps - 1     ,				__UpperCAmelCase     ,				dtype=__UpperCAmelCase	)[
																					    ::-1
																					].copy()
														elif self.config.timestep_spacing == "leading":
																					lowerCamelCase__   :      str			    =			self.config.num_train_timesteps // self.num_inference_steps
																					# creates integer timesteps by multiplying by ratio
																					# casting to int to avoid issues when num_inference_step is power of 3
																					lowerCamelCase__   :      Dict			    =			(np.arange(0     ,				__UpperCAmelCase	) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase	)
																					timesteps += self.config.steps_offset
														elif self.config.timestep_spacing == "trailing":
																					lowerCamelCase__   :      Any			    =			self.config.num_train_timesteps / self.num_inference_steps
																					# creates integer timesteps by multiplying by ratio
																					# casting to int to avoid issues when num_inference_step is power of 3
																					lowerCamelCase__   :      Tuple			    =			(np.arange(self.config.num_train_timesteps     ,				0     ,				-step_ratio	)).round().copy().astype(__UpperCAmelCase	)
																					timesteps -= 1
														else:
																					raise ValueError(
																					    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

														lowerCamelCase__   :      Union[str, Any]			    =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
														lowerCamelCase__   :      Tuple			    =			np.log(__UpperCAmelCase	)
														lowerCamelCase__   :      Union[str, Any]			    =			np.interp(__UpperCAmelCase     ,				np.arange(0     ,				len(__UpperCAmelCase	)	)     ,				__UpperCAmelCase	)

														if self.use_karras_sigmas:
																					lowerCamelCase__   :      Union[str, Any]			    =			self._convert_to_karras(in_sigmas=__UpperCAmelCase	)
																					lowerCamelCase__   :      Tuple			    =			np.array([self._sigma_to_t(__UpperCAmelCase     ,				__UpperCAmelCase	) for sigma in sigmas]	)

														lowerCamelCase__   :      List[Any]			    =			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)

														lowerCamelCase__   :      Dict			    =			torch.from_numpy(__UpperCAmelCase	).to(device=__UpperCAmelCase	)
														if str(__UpperCAmelCase	).startswith("""mps"""	):
																					# mps does not support float64
																					lowerCamelCase__   :      Dict			    =			torch.from_numpy(__UpperCAmelCase	).to(__UpperCAmelCase     ,				dtype=torch.floataa	)
														else:
																					lowerCamelCase__   :      Dict			    =			torch.from_numpy(__UpperCAmelCase	).to(device=__UpperCAmelCase	)

														lowerCamelCase__   :      List[Any]			    =			[]




							def 						lowerCamelCase_							(				self:							Dict     ,				UpperCamelCase__:							Tuple     ,				UpperCamelCase__:							Optional[int]	):
														# get log sigma
														lowerCamelCase__   :      str			    =			np.log(__UpperCAmelCase	)

														# get distribution
														lowerCamelCase__   :      List[str]			    =			log_sigma - log_sigmas[:, np.newaxis]

														# get sigmas range
														lowerCamelCase__   :      Union[str, Any]			    =			np.cumsum((dists >= 0)     ,				axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
														lowerCamelCase__   :      Any			    =			low_idx + 1

														lowerCamelCase__   :      Tuple			    =			log_sigmas[low_idx]
														lowerCamelCase__   :      int			    =			log_sigmas[high_idx]

														# interpolate sigmas
														lowerCamelCase__   :      Optional[int]			    =			(low - log_sigma) / (low - high)
														lowerCamelCase__   :      Union[str, Any]			    =			np.clip(__UpperCAmelCase     ,				0     ,				1	)

														# transform interpolation to time range
														lowerCamelCase__   :      Optional[int]			    =			(1 - w) * low_idx + w * high_idx
														lowerCamelCase__   :      Any			    =			t.reshape(sigma.shape	)
														return t




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				UpperCamelCase__:							Any	):
														lowerCamelCase__   :      float			    =			in_sigmas[-1].item()
														lowerCamelCase__   :      float			    =			in_sigmas[0].item()

														lowerCamelCase__   :      Tuple			    =			7.0  # 7.0 is the value used in the paper
														lowerCamelCase__   :      Union[str, Any]			    =			np.linspace(0     ,				1     ,				self.num_inference_steps	)
														lowerCamelCase__   :      Optional[Any]			    =			sigma_min ** (1 / rho)
														lowerCamelCase__   :      Optional[int]			    =			sigma_max ** (1 / rho)
														lowerCamelCase__   :      Tuple			    =			(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
														return sigmas




							def 						lowerCamelCase_							(				self:							Tuple     ,				UpperCamelCase__:							List[Any]     ,				UpperCamelCase__:							int     ,				UpperCamelCase__:							Tuple     ,				UpperCamelCase__:							str = 4     ,				UpperCamelCase__:							Optional[Any] = True     ,				):
														if not self.is_scale_input_called:
																					warnings.warn(
																					    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
																					    """See `StableDiffusionPipeline` for a usage example."""	)

														if isinstance(__UpperCAmelCase     ,				torch.Tensor	):
																					lowerCamelCase__   :      List[Any]			    =			timestep.to(self.timesteps.device	)
														lowerCamelCase__   :      List[str]			    =			(self.timesteps == timestep).nonzero().item()
														lowerCamelCase__   :      Optional[Any]			    =			self.sigmas[step_index]

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														if self.config.prediction_type == "epsilon":
																					lowerCamelCase__   :      Union[str, Any]			    =			sample - sigma * model_output
														elif self.config.prediction_type == "v_prediction":
																					# * c_out + input * c_skip
																					lowerCamelCase__   :      Optional[Any]			    =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
														elif self.config.prediction_type == "sample":
																					lowerCamelCase__   :      List[Any]			    =			model_output
														else:
																					raise ValueError(
																					    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

														# 2. Convert to an ODE derivative
														lowerCamelCase__   :      Union[str, Any]			    =			(sample - pred_original_sample) / sigma
														self.derivatives.append(__UpperCAmelCase	)
														if len(self.derivatives	) > order:
																					self.derivatives.pop(0	)

														# 3. Compute linear multistep coefficients
														lowerCamelCase__   :      Optional[Any]			    =			min(step_index + 1     ,				__UpperCAmelCase	)
														lowerCamelCase__   :      Dict			    =			[self.get_lms_coefficient(__UpperCAmelCase     ,				__UpperCAmelCase     ,				__UpperCAmelCase	) for curr_order in range(__UpperCAmelCase	)]

														# 4. Compute previous sample based on the derivatives path
														lowerCamelCase__   :      Optional[Any]			    =			sample + sum(
														    coeff * derivative for coeff, derivative in zip(__UpperCAmelCase     ,				reversed(self.derivatives	)	)	)

														if not return_dict:
																					return (prev_sample,)

														return LMSDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase     ,				pred_original_sample=__UpperCAmelCase	)




							def 						lowerCamelCase_							(				self:							Dict     ,				UpperCamelCase__:							Union[str, Any]     ,				UpperCamelCase__:							Optional[Any]     ,				UpperCamelCase__:							Optional[Any]     ,				):
														# Make sure sigmas and timesteps have the same device and dtype as original_samples
														lowerCamelCase__   :      Dict			    =			self.sigmas.to(device=original_samples.device     ,				dtype=original_samples.dtype	)
														if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase	):
																					# mps does not support float64
																					lowerCamelCase__   :      Tuple			    =			self.timesteps.to(original_samples.device     ,				dtype=torch.floataa	)
																					lowerCamelCase__   :      Any			    =			timesteps.to(original_samples.device     ,				dtype=torch.floataa	)
														else:
																					lowerCamelCase__   :      int			    =			self.timesteps.to(original_samples.device	)
																					lowerCamelCase__   :      Tuple			    =			timesteps.to(original_samples.device	)

														lowerCamelCase__   :      Dict			    =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

														lowerCamelCase__   :      Union[str, Any]			    =			sigmas[step_indices].flatten()
														while len(sigma.shape	) < len(original_samples.shape	):
																					lowerCamelCase__   :      List[Any]			    =			sigma.unsqueeze(-1	)

														lowerCamelCase__   :      Any			    =			original_samples + noise * sigma
														return noisy_samples




							def __len__(				self:							int	):
														return self.config.num_train_timesteps





"""simple docstring"""






import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class 	A_       (     _UpperCAmelCase      ,  unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	Any   =						RealmTokenizer
  lowercase		:	Any   =						RealmTokenizerFast
  lowercase		:	int   =						True
  lowercase		:	Union[str, Any]   =						True
  lowercase		:	int   =						filter_non_english
  def 							lowercase_     (						self						)				->					int:
     super().setUp()

     a   :	str						=    [
         '[UNK]',
         '[CLS]',
         '[SEP]',
         '[PAD]',
         '[MASK]',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     a   :	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 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Optional[int]:
     a   :	List[str]						=    'UNwant\u00E9d,running'
     a   :	Dict						=    'unwanted, running'
     return input_text, output_text
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	int						=    self.tokenizer_class(self.vocab_file						)

     a   :	List[Any]						=    tokenizer.tokenize('UNwant\u00E9d,running'						)
     self.assertListEqual(__UpperCAmelCase  ,					['un', '##want', '##ed', ',', 'runn', '##ing']						)
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase						)  ,					[9, 6, 7, 12, 10, 11]						)
  def 							lowercase_     (						self						)				->					Dict:
     if not self.test_rust_tokenizer:
        return

     a   :	int						=    self.get_tokenizer()
     a   :	List[str]						=    self.get_rust_tokenizer()

     a   :	str						=    'UNwant\u00E9d,running'

     a   :	Optional[int]						=    tokenizer.tokenize(__UpperCAmelCase						)
     a   :	Tuple						=    rust_tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[int]						=    tokenizer.encode(__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     a   :	Tuple						=    rust_tokenizer.encode(__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Tuple						=    self.get_rust_tokenizer()
     a   :	Optional[int]						=    tokenizer.encode(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    rust_tokenizer.encode(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     # With lower casing
     a   :	List[str]						=    self.get_tokenizer(do_lower_case=__UpperCAmelCase						)
     a   :	Any						=    self.get_rust_tokenizer(do_lower_case=__UpperCAmelCase						)

     a   :	Any						=    'UNwant\u00E9d,running'

     a   :	Tuple						=    tokenizer.tokenize(__UpperCAmelCase						)
     a   :	List[Any]						=    rust_tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Any						=    tokenizer.encode(__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     a   :	Any						=    rust_tokenizer.encode(__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[int]						=    self.get_rust_tokenizer()
     a   :	int						=    tokenizer.encode(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    rust_tokenizer.encode(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	Any						=    BasicTokenizer()

     self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'						)  ,					['ah', '\u535A', '\u63A8', 'zz']						)
  def 							lowercase_     (						self						)				->					Tuple:
     a   :	Tuple						=    BasicTokenizer(do_lower_case=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '						)  ,					['hello', '!', 'how', 'are', 'you', '?']						)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)  ,					['hello']						)
  def 							lowercase_     (						self						)				->					Dict:
     a   :	Optional[int]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					strip_accents=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['hällo', '!', 'how', 'are', 'you', '?']						)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)  ,					['h\u00E9llo']						)
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	List[Any]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					strip_accents=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['hallo', '!', 'how', 'are', 'you', '?']						)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)  ,					['hello']						)
  def 							lowercase_     (						self						)				->					Dict:
     a   :	Optional[Any]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['hallo', '!', 'how', 'are', 'you', '?']						)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)  ,					['hello']						)
  def 							lowercase_     (						self						)				->					int:
     a   :	Any						=    BasicTokenizer(do_lower_case=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '						)  ,					['HeLLo', '!', 'how', 'Are', 'yoU', '?']						)
  def 							lowercase_     (						self						)				->					Any:
     a   :	List[str]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					strip_accents=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['HäLLo', '!', 'how', 'Are', 'yoU', '?']						)
  def 							lowercase_     (						self						)				->					List[Any]:
     a   :	Optional[int]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					strip_accents=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['HaLLo', '!', 'how', 'Are', 'yoU', '?']						)
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	List[Any]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					never_split=['[UNK]']						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'						)  ,					['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']						)
  def 							lowercase_     (						self						)				->					int:
     a   :	Optional[int]						=    ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

     a   :	str						=    {}
     for i, token in enumerate(__UpperCAmelCase						):
        a   :	str						=    i
     a   :	List[Any]						=    WordpieceTokenizer(vocab=__UpperCAmelCase  ,					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 							lowercase_     (						self						)				->					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 							lowercase_     (						self						)				->					Optional[int]:
     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 							lowercase_     (						self						)				->					Union[str, Any]:
     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 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	List[str]						=    self.get_tokenizer()

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

     if self.test_rust_tokenizer:
        a   :	Tuple						=    self.get_rust_tokenizer()
        self.assertListEqual(
            [rust_tokenizer.tokenize(__UpperCAmelCase						) for t in ['Test', '\xad', 'test']]  ,					[['[UNK]'], [], ['[UNK]']]						)
  @slow
  def 							lowercase_     (						self						)				->					Dict:
     a   :	Optional[int]						=    self.tokenizer_class.from_pretrained('bert-base-uncased'						)

     a   :	Optional[Any]						=    tokenizer.encode('sequence builders'  ,					add_special_tokens=__UpperCAmelCase						)
     a   :	Optional[int]						=    tokenizer.encode('multi-sequence build'  ,					add_special_tokens=__UpperCAmelCase						)

     a   :	str						=    tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase  ,					__UpperCAmelCase						)

     assert encoded_sentence == [1_01] + text + [1_02]
     assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
  def 							lowercase_     (						self						)				->					List[str]:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
           a   :	str						=    self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)

           a   :	Tuple						=    f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
           a   :	Union[str, Any]						=    tokenizer_r.encode_plus(
               __UpperCAmelCase  ,					return_attention_mask=__UpperCAmelCase  ,					return_token_type_ids=__UpperCAmelCase  ,					return_offsets_mapping=__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase  ,					)

           a   :	List[Any]						=    tokenizer_r.do_lower_case if hasattr(__UpperCAmelCase  ,					'do_lower_case'						) else False
           a   :	str						=    (
               [
                   ((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']						)






  @slow
  def 							lowercase_     (						self						)				->					Dict:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
           a   :	str						=    self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
           a   :	str						=    self.tokenizer_class.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
           a   :	Dict						=    [['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

           a   :	Optional[int]						=    tokenizer_r.batch_encode_candidates(__UpperCAmelCase  ,					max_length=10  ,					return_tensors='np'						)
           a   :	str						=    tokenizer_p.batch_encode_candidates(__UpperCAmelCase  ,					max_length=10  ,					return_tensors='np'						)

           a   :	Dict						=    (2, 2, 10)

           self.assertEqual(encoded_sentence_r['input_ids'].shape  ,					__UpperCAmelCase						)
           self.assertEqual(encoded_sentence_r['attention_mask'].shape  ,					__UpperCAmelCase						)
           self.assertEqual(encoded_sentence_r['token_type_ids'].shape  ,					__UpperCAmelCase						)

           self.assertEqual(encoded_sentence_p['input_ids'].shape  ,					__UpperCAmelCase						)
           self.assertEqual(encoded_sentence_p['attention_mask'].shape  ,					__UpperCAmelCase						)
           self.assertEqual(encoded_sentence_p['token_type_ids'].shape  ,					__UpperCAmelCase						)





