

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     a__    (				UpperCAmelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	torch.FloatTensor
  UpperCAmelCase__ :	Optional[torch.FloatTensor]         =None






def    __lowercase (       _A  ,      _A=0.999  ,      _A="cosine"  ,      )       ->  str:
      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

      else:
            raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"     )

      SCREAMING_SNAKE_CASE	: List[Any]					       =				[]
      for i in range(_A     ):
            SCREAMING_SNAKE_CASE	: int					       =				i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE	: Any					       =				(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_A     ) / alpha_bar_fn(_A     )  ,      _A     )     )
      return torch.tensor(_A  ,      dtype=torch.floataa     )







class     a__    (				UpperCAmelCase		,     UpperCAmelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	str         =[e.name for e in KarrasDiffusionSchedulers]
  UpperCAmelCase__ :	List[Any]         =1


  @register_to_config
  def __init__(     self      :	Optional[int]			,			UpperCAmelCase__      :	int = 1_0_0_0			,			UpperCAmelCase__      :	float = 0.00_01			,			UpperCAmelCase__      :	float = 0.02			,			UpperCAmelCase__      :	str = "linear"			,			UpperCAmelCase__      :	Optional[Union[np.ndarray, List[float]]] = None			,			UpperCAmelCase__      :	Optional[bool] = False			,			UpperCAmelCase__      :	str = "epsilon"			,			UpperCAmelCase__      :	str = "linspace"			,			UpperCAmelCase__      :	int = 0			,			)   ->List[str]:
        """simple docstring"""







        if trained_betas is not None:
              SCREAMING_SNAKE_CASE	: Dict					       =				torch.tensor(UpperCAmelCase__			,			dtype=torch.floataa						)
        elif beta_schedule == "linear":
              SCREAMING_SNAKE_CASE	: List[str]					       =				torch.linspace(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			dtype=torch.floataa						)
        elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              SCREAMING_SNAKE_CASE	: Tuple					       =				(
                  torch.linspace(beta_start**0.5			,			beta_end**0.5			,			UpperCAmelCase__			,			dtype=torch.floataa						) ** 2
              )
        elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              SCREAMING_SNAKE_CASE	: Any					       =				betas_for_alpha_bar(UpperCAmelCase__						)
        else:
              raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"						)

        SCREAMING_SNAKE_CASE	: Any					       =				1.0 - self.betas
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				torch.cumprod(self.alphas			,			dim=0						)

        SCREAMING_SNAKE_CASE	: str					       =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
        SCREAMING_SNAKE_CASE	: str					       =				torch.from_numpy(UpperCAmelCase__						)

        # setable values
        SCREAMING_SNAKE_CASE	: str					       =				None
        SCREAMING_SNAKE_CASE	: Any					       =				use_karras_sigmas
        self.set_timesteps(UpperCAmelCase__			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				[]
        SCREAMING_SNAKE_CASE	: List[Any]					       =				False


  @property
  def 	_lowercase (     self      :	Union[str, Any]						)   ->str:
        """simple docstring"""







        if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

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


  def 	_lowercase (     self      :	List[Any]			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	Union[float, torch.FloatTensor]						)   ->torch.FloatTensor:
        """simple docstring"""







        if isinstance(UpperCAmelCase__			,			torch.Tensor						):
              SCREAMING_SNAKE_CASE	: List[Any]					       =				timestep.to(self.timesteps.device						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				(self.timesteps == timestep).nonzero().item()
        SCREAMING_SNAKE_CASE	: List[Any]					       =				self.sigmas[step_index]
        SCREAMING_SNAKE_CASE	: str					       =				sample / ((sigma**2 + 1) ** 0.5)
        SCREAMING_SNAKE_CASE	: Any					       =				True
        return sample


  def 	_lowercase (     self      :	str			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	Tuple						)   ->List[Any]:
        """simple docstring"""







        def lms_derivative(UpperCAmelCase__      :	Tuple						):
              SCREAMING_SNAKE_CASE	: str					       =				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

        SCREAMING_SNAKE_CASE	: int					       =				integrate.quad(UpperCAmelCase__			,			self.sigmas[t]			,			self.sigmas[t + 1]			,			epsrel=1e-4						)[0]

        return integrated_coeff


  def 	_lowercase (     self      :	int			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	Union[str, torch.device] = None						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: 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":
              SCREAMING_SNAKE_CASE	: List[Any]					       =				np.linspace(0			,			self.config.num_train_timesteps - 1			,			UpperCAmelCase__			,			dtype=UpperCAmelCase__						)[
                  ::-1
              ].copy()
        elif self.config.timestep_spacing == "leading":
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				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
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				(np.arange(0			,			UpperCAmelCase__						) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase__						)
              timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
              SCREAMING_SNAKE_CASE	: Tuple					       =				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
              SCREAMING_SNAKE_CASE	: Dict					       =				(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'."						)

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

        if self.use_karras_sigmas:
              SCREAMING_SNAKE_CASE	: int					       =				self._convert_to_karras(in_sigmas=UpperCAmelCase__						)
              SCREAMING_SNAKE_CASE	: str					       =				np.array([self._sigma_to_t(UpperCAmelCase__			,			UpperCAmelCase__						) for sigma in sigmas]						)

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

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

        SCREAMING_SNAKE_CASE	: List[Any]					       =				[]


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	Optional[int]			,			UpperCAmelCase__      :	Tuple						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				np.log(UpperCAmelCase__						)

        # get distribution
        SCREAMING_SNAKE_CASE	: List[Any]					       =				log_sigma - log_sigmas[:, np.newaxis]

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

        SCREAMING_SNAKE_CASE	: Tuple					       =				log_sigmas[low_idx]
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				log_sigmas[high_idx]

        # interpolate sigmas
        SCREAMING_SNAKE_CASE	: Any					       =				(low - log_sigma) / (low - high)
        SCREAMING_SNAKE_CASE	: str					       =				np.clip(UpperCAmelCase__			,			0			,			1						)

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


  def 	_lowercase (     self      :	List[str]			,			UpperCAmelCase__      :	torch.FloatTensor						)   ->torch.FloatTensor:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: float					       =				in_sigmas[-1].item()
        SCREAMING_SNAKE_CASE	: float					       =				in_sigmas[0].item()

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


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	Union[float, torch.FloatTensor]			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	int = 4			,			UpperCAmelCase__      :	bool = True			,			)   ->Union[LMSDiscreteSchedulerOutput, Tuple]:
        """simple docstring"""







        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						):
              SCREAMING_SNAKE_CASE	: str					       =				timestep.to(self.timesteps.device						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				(self.timesteps == timestep).nonzero().item()
        SCREAMING_SNAKE_CASE	: int					       =				self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
              SCREAMING_SNAKE_CASE	: List[Any]					       =				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              SCREAMING_SNAKE_CASE	: int					       =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				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
        SCREAMING_SNAKE_CASE	: str					       =				(sample - pred_original_sample) / sigma
        self.derivatives.append(UpperCAmelCase__						)
        if len(self.derivatives						) > order:
              self.derivatives.pop(0						)

        # 3. Compute linear multistep coefficients
        SCREAMING_SNAKE_CASE	: List[Any]					       =				min(step_index + 1			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				[self.get_lms_coefficient(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						) for curr_order in range(UpperCAmelCase__						)]

        # 4. Compute previous sample based on the derivatives path
        SCREAMING_SNAKE_CASE	: str					       =				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 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	torch.FloatTensor			,			)   ->torch.FloatTensor:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: 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
              SCREAMING_SNAKE_CASE	: Dict					       =				self.timesteps.to(original_samples.device			,			dtype=torch.floataa						)
              SCREAMING_SNAKE_CASE	: List[Any]					       =				timesteps.to(original_samples.device			,			dtype=torch.floataa						)
        else:
              SCREAMING_SNAKE_CASE	: Dict					       =				self.timesteps.to(original_samples.device						)
              SCREAMING_SNAKE_CASE	: str					       =				timesteps.to(original_samples.device						)

        SCREAMING_SNAKE_CASE	: List[str]					       =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				sigmas[step_indices].flatten()
        while len(sigma.shape						) < len(original_samples.shape						):
              SCREAMING_SNAKE_CASE	: int					       =				sigma.unsqueeze(-1						)

        SCREAMING_SNAKE_CASE	: Any					       =				original_samples + noise * sigma
        return noisy_samples




  def __len__(     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        return self.config.num_train_timesteps







import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class     a__    (				tf.keras.layers.Layer    ):







  """simple docstring"""




  def __init__(     self      :	str			,			UpperCAmelCase__      :	List			,			UpperCAmelCase__      :	bool			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	str = "longest"			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	int = 5_1_2			,			UpperCAmelCase__      :	int = None			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	bool = True			,			UpperCAmelCase__      :	bool = True			,			**UpperCAmelCase__      :	Optional[int]			,			)   ->Union[str, Any]:
        """simple docstring"""







        super().__init__()
        if use_fast_bert_tokenizer:
              SCREAMING_SNAKE_CASE	: Any					       =				FastBertTokenizer(
                  UpperCAmelCase__			,			token_out_type=tf.intaa			,			lower_case_nfd_strip_accents=UpperCAmelCase__			,			**UpperCAmelCase__						)
        else:
              SCREAMING_SNAKE_CASE	: Any					       =				tf.lookup.StaticVocabularyTable(
                  tf.lookup.KeyValueTensorInitializer(
                      keys=UpperCAmelCase__			,			key_dtype=tf.string			,			values=tf.range(tf.size(UpperCAmelCase__			,			out_type=tf.intaa						)			,			dtype=tf.intaa						)			,			value_dtype=tf.intaa			,			)			,			num_oov_buckets=1			,			)
              SCREAMING_SNAKE_CASE	: Dict					       =				BertTokenizerLayer(
                  UpperCAmelCase__			,			token_out_type=tf.intaa			,			lower_case=UpperCAmelCase__			,			**UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: Any					       =				vocab_list
        SCREAMING_SNAKE_CASE	: int					       =				do_lower_case
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				cls_token_id or vocab_list.index("""[CLS]"""						)
        SCREAMING_SNAKE_CASE	: int					       =				sep_token_id or vocab_list.index("""[SEP]"""						)
        SCREAMING_SNAKE_CASE	: int					       =				pad_token_id or vocab_list.index("""[PAD]"""						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				ShrinkLongestTrimmer(max_length - 3			,			axis=1						)  # Allow room for special tokens
        SCREAMING_SNAKE_CASE	: Tuple					       =				max_length
        SCREAMING_SNAKE_CASE	: List[str]					       =				padding
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				truncation
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				pad_to_multiple_of
        SCREAMING_SNAKE_CASE	: Dict					       =				return_token_type_ids
        SCREAMING_SNAKE_CASE	: str					       =				return_attention_mask


  @classmethod
  def 	_lowercase (     cls      :	Optional[int]			,			UpperCAmelCase__      :	"PreTrainedTokenizerBase"			,			**UpperCAmelCase__      :	Tuple						)   ->str:  # noqa: F821
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				kwargs.pop("""do_lower_case"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        SCREAMING_SNAKE_CASE	: Any					       =				kwargs.pop("""cls_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				kwargs.pop("""sep_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        SCREAMING_SNAKE_CASE	: int					       =				kwargs.pop("""pad_token_id"""			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        SCREAMING_SNAKE_CASE	: List[str]					       =				tokenizer.get_vocab()
        SCREAMING_SNAKE_CASE	: List[str]					       =				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,			key=lambda UpperCAmelCase__						: x[1]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				[entry[0] for entry in vocab]
        return cls(
            vocab_list=UpperCAmelCase__			,			do_lower_case=UpperCAmelCase__			,			cls_token_id=UpperCAmelCase__			,			sep_token_id=UpperCAmelCase__			,			pad_token_id=UpperCAmelCase__			,			**UpperCAmelCase__			,			)


  @classmethod
  def 	_lowercase (     cls      :	str			,			UpperCAmelCase__      :	Union[str, os.PathLike]			,			*UpperCAmelCase__      :	Dict			,			**UpperCAmelCase__      :	int						)   ->Any:
        """simple docstring"""







        try:
              SCREAMING_SNAKE_CASE	: List[str]					       =				BertTokenizer.from_pretrained(UpperCAmelCase__			,			*UpperCAmelCase__			,			**UpperCAmelCase__						)
        except:  # noqa: E722
              from .tokenization_bert_fast import BertTokenizerFast

              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				BertTokenizerFast.from_pretrained(UpperCAmelCase__			,			*UpperCAmelCase__			,			**UpperCAmelCase__						)
        return cls.from_tokenizer(UpperCAmelCase__			,			**UpperCAmelCase__						)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	List[str]						)   ->List[str]:
        """simple docstring"""







        if self.do_lower_case:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				case_fold_utfa(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: int					       =				self.tf_tokenizer.tokenize(UpperCAmelCase__						)
        return tokens.merge_dims(1			,			-1						)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	Any			,			UpperCAmelCase__      :	Dict=None			,			UpperCAmelCase__      :	Optional[int]=None			,			UpperCAmelCase__      :	Optional[Any]=None			,			UpperCAmelCase__      :	Union[str, Any]=None			,			UpperCAmelCase__      :	Dict=None			,			UpperCAmelCase__      :	int=None			,			UpperCAmelCase__      :	Tuple=None			,			)   ->Any:
        """simple docstring"""







        if padding is None:
              SCREAMING_SNAKE_CASE	: int					       =				self.padding
        if padding not in ("longest", "max_length"):
              raise ValueError("""Padding must be either 'longest' or 'max_length'!"""						)
        if max_length is not None and text_pair is not None:
              # Because we have to instantiate a Trimmer to do it properly
              raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""						)
        if max_length is None:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.max_length
        if truncation is None:
              SCREAMING_SNAKE_CASE	: str					       =				self.truncation
        if pad_to_multiple_of is None:
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.pad_to_multiple_of
        if return_token_type_ids is None:
              SCREAMING_SNAKE_CASE	: Any					       =				self.return_token_type_ids
        if return_attention_mask is None:
              SCREAMING_SNAKE_CASE	: int					       =				self.return_attention_mask
        if not isinstance(UpperCAmelCase__			,			tf.Tensor						):
              SCREAMING_SNAKE_CASE	: str					       =				tf.convert_to_tensor(UpperCAmelCase__						)
        if text_pair is not None and not isinstance(UpperCAmelCase__			,			tf.Tensor						):
              SCREAMING_SNAKE_CASE	: int					       =				tf.convert_to_tensor(UpperCAmelCase__						)
        if text_pair is not None:
              if text.shape.rank > 1:
                    raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""						)
              if text_pair.shape.rank > 1:
                    raise ValueError("""text_pair should not be multidimensional!"""						)
        if text.shape.rank == 2:
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Dict					       =				text[:, 0], text[:, 1]
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.unpaired_tokenize(UpperCAmelCase__						)
        if text_pair is None:  # Unpaired text
              if truncation:
                    SCREAMING_SNAKE_CASE	: int					       =				text[:, : max_length - 2]  # Allow room for special tokens
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[str]					       =				combine_segments(
                  (text,)			,			start_of_sequence_id=self.cls_token_id			,			end_of_segment_id=self.sep_token_id						)
        else:  # Paired text
              SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				self.unpaired_tokenize(UpperCAmelCase__						)
              if truncation:
                    SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Any					       =				self.paired_trimmer.trim([text, text_pair]						)
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: str					       =				combine_segments(
                  (text, text_pair)			,			start_of_sequence_id=self.cls_token_id			,			end_of_segment_id=self.sep_token_id						)
        if padding == "longest":
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				input_ids.bounding_shape(axis=1						)
              if pad_to_multiple_of is not None:
                    # No ceiling division in tensorflow, so we negate floordiv instead
                    SCREAMING_SNAKE_CASE	: Any					       =				pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,			UpperCAmelCase__						))
        else:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				max_length

        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Tuple					       =				pad_model_inputs(UpperCAmelCase__			,			max_seq_length=UpperCAmelCase__			,			pad_value=self.pad_token_id						)
        SCREAMING_SNAKE_CASE	: Any					       =				{"""input_ids""": input_ids}
        if return_attention_mask:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				attention_mask
        if return_token_type_ids:
              SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				pad_model_inputs(
                  UpperCAmelCase__			,			max_seq_length=UpperCAmelCase__			,			pad_value=self.pad_token_id						)
              SCREAMING_SNAKE_CASE	: List[str]					       =				token_type_ids
        return output




  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }




