




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Any]    ,				_lowerCAmelCase				: List[Any]=0.999    ,				_lowerCAmelCase				: Tuple="cosine"    ,				):




 """simple docstring"""







 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_lowerCAmelCase				: Optional[Any]						):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

 elif alpha_transform_type == "exp":

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

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

 UpperCAmelCase__							=    []
 for i in range(_lowerCAmelCase						):
  UpperCAmelCase__							=    i / num_diffusion_timesteps
  UpperCAmelCase__							=    (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase						) / alpha_bar_fn(_lowerCAmelCase						)    ,				_lowerCAmelCase						)						)
 return torch.tensor(_lowerCAmelCase    ,				dtype=torch.floataa						)


class       _UpperCamelCase   (					lowerCAmelCase						,							lowerCAmelCase		):
      UpperCAmelCase_          =		[e.name for e in KarrasDiffusionSchedulers]
      UpperCAmelCase_          =		2
      @register_to_config
      def __init__(      self :List[str]			,   lowerCamelCase :int = 1000			,   lowerCamelCase :float = 0.0_00_85			,   lowerCamelCase :float = 0.0_12			,   lowerCamelCase :str = "linear"			,   lowerCamelCase :Optional[Union[np.ndarray, List[float]]] = None			,   lowerCamelCase :str = "epsilon"			,   lowerCamelCase :str = "linspace"			,   lowerCamelCase :int = 0			,   )  ->      Any:
       if trained_betas is not None:
        UpperCAmelCase__							=    torch.tensor(lowerCamelCase			,   dtype=torch.floataa   )
       elif beta_schedule == "linear":
        UpperCAmelCase__							=    torch.linspace(lowerCamelCase			,   lowerCamelCase			,   lowerCamelCase			,   dtype=torch.floataa   )
       elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        UpperCAmelCase__							=    (
            torch.linspace(beta_start**0.5			,   beta_end**0.5			,   lowerCamelCase			,   dtype=torch.floataa   ) ** 2
        )
       elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        UpperCAmelCase__							=    betas_for_alpha_bar(lowerCamelCase   )
       else:
        raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

       UpperCAmelCase__							=    1.0 - self.betas
       UpperCAmelCase__							=    torch.cumprod(self.alphas			,   dim=0   )

       #  set all values
       self.set_timesteps(lowerCamelCase			,   lowerCamelCase			,   lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :int			,   lowerCamelCase :List[Any]=None   )  ->      List[str]:
       if schedule_timesteps is None:
        UpperCAmelCase__							=    self.timesteps

       UpperCAmelCase__							=    (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter   ) == 0:
        UpperCAmelCase__							=    1 if len(lowerCamelCase   ) > 1 else 0
       else:
        UpperCAmelCase__							=    timestep.cpu().item() if torch.is_tensor(lowerCamelCase   ) else timestep
        UpperCAmelCase__							=    self._index_counter[timestep_int]

       return indices[pos].item()
      @property
      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      List[Any]:
       # 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 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :torch.FloatTensor			,   lowerCamelCase :Union[float, torch.FloatTensor]			,   )  ->      torch.FloatTensor:
       UpperCAmelCase__							=    self.index_for_timestep(lowerCamelCase   )

       if self.state_in_first_order:
        UpperCAmelCase__							=    self.sigmas[step_index]
       else:
        UpperCAmelCase__							=    self.sigmas_interpol[step_index - 1]

       UpperCAmelCase__							=    sample / ((sigma**2 + 1) ** 0.5)
       return sample
      def 						UpperCAmelCase_    (      self :List[Any]			,   lowerCamelCase :int			,   lowerCamelCase :Union[str, torch.device] = None			,   lowerCamelCase :Optional[int] = None			,   )  ->      Union[str, Any]:
       UpperCAmelCase__							=    num_inference_steps

       UpperCAmelCase__							=    num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
        UpperCAmelCase__							=    np.linspace(0			,   num_train_timesteps - 1			,   lowerCamelCase			,   dtype=lowerCamelCase   )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
        UpperCAmelCase__							=    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
        UpperCAmelCase__							=    (np.arange(0			,   lowerCamelCase   ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase   )
        timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
        UpperCAmelCase__							=    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
        UpperCAmelCase__							=    (np.arange(lowerCamelCase			,   0			,   -step_ratio   )).round().copy().astype(lowerCamelCase   )
        timesteps -= 1
       else:
        raise ValueError(
            f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''   )

       UpperCAmelCase__							=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
       UpperCAmelCase__							=    torch.from_numpy(np.log(lowerCamelCase   )   ).to(lowerCamelCase   )

       UpperCAmelCase__							=    np.interp(lowerCamelCase			,   np.arange(0			,   len(lowerCamelCase   )   )			,   lowerCamelCase   )
       UpperCAmelCase__							=    np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
       UpperCAmelCase__							=    torch.from_numpy(lowerCamelCase   ).to(device=lowerCamelCase   )

       # compute up and down sigmas
       UpperCAmelCase__							=    sigmas.roll(-1   )
       UpperCAmelCase__							=    0.0
       UpperCAmelCase__							=    (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       UpperCAmelCase__							=    (sigmas_next**2 - sigmas_up**2) ** 0.5
       UpperCAmelCase__							=    0.0

       # compute interpolated sigmas
       UpperCAmelCase__							=    sigmas.log().lerp(sigmas_down.log()			,   0.5   ).exp()
       UpperCAmelCase__							=    0.0

       # set sigmas
       UpperCAmelCase__							=    torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
       UpperCAmelCase__							=    torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )
       UpperCAmelCase__							=    torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2   ), sigmas_up[-1:]]   )
       UpperCAmelCase__							=    torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2   ), sigmas_down[-1:]]   )

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

       UpperCAmelCase__							=    self.sigma_to_t(lowerCamelCase   ).to(lowerCamelCase			,   dtype=timesteps.dtype   )
       UpperCAmelCase__							=    torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])			,   dim=-1   ).flatten()

       UpperCAmelCase__							=    torch.cat([timesteps[:1], interleaved_timesteps]   )

       UpperCAmelCase__							=    None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       UpperCAmelCase__							=    defaultdict(lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :int   )  ->      Optional[int]:
       # get log sigma
       UpperCAmelCase__							=    sigma.log()

       # get distribution
       UpperCAmelCase__							=    log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       UpperCAmelCase__							=    dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
       UpperCAmelCase__							=    low_idx + 1

       UpperCAmelCase__							=    self.log_sigmas[low_idx]
       UpperCAmelCase__							=    self.log_sigmas[high_idx]

       # interpolate sigmas
       UpperCAmelCase__							=    (low - log_sigma) / (low - high)
       UpperCAmelCase__							=    w.clamp(0			,   1   )

       # transform interpolation to time range
       UpperCAmelCase__							=    (1 - w) * low_idx + w * high_idx
       UpperCAmelCase__							=    t.view(sigma.shape   )
       return t
      @property
      def 						UpperCAmelCase_    (      self :Optional[Any]   )  ->      Optional[Any]:
       return self.sample is None
      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :Union[torch.FloatTensor, np.ndarray]			,   lowerCamelCase :Union[float, torch.FloatTensor]			,   lowerCamelCase :Union[torch.FloatTensor, np.ndarray]			,   lowerCamelCase :Optional[torch.Generator] = None			,   lowerCamelCase :bool = True			,   )  ->      Union[SchedulerOutput, Tuple]:
       UpperCAmelCase__							=    self.index_for_timestep(lowerCamelCase   )

       # advance index counter by 1
       UpperCAmelCase__							=    timestep.cpu().item() if torch.is_tensor(lowerCamelCase   ) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
        UpperCAmelCase__							=    self.sigmas[step_index]
        UpperCAmelCase__							=    self.sigmas_interpol[step_index]
        UpperCAmelCase__							=    self.sigmas_up[step_index]
        UpperCAmelCase__							=    self.sigmas_down[step_index - 1]
       else:
        # 2nd order / KPDM2's method
        UpperCAmelCase__							=    self.sigmas[step_index - 1]
        UpperCAmelCase__							=    self.sigmas_interpol[step_index - 1]
        UpperCAmelCase__							=    self.sigmas_up[step_index - 1]
        UpperCAmelCase__							=    self.sigmas_down[step_index - 1]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       UpperCAmelCase__							=    0
       UpperCAmelCase__							=    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       UpperCAmelCase__							=    model_output.device
       UpperCAmelCase__							=    randn_tensor(model_output.shape			,   dtype=model_output.dtype			,   device=lowerCamelCase			,   generator=lowerCamelCase   )

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
        UpperCAmelCase__							=    sigma_hat if self.state_in_first_order else sigma_interpol
        UpperCAmelCase__							=    sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
        UpperCAmelCase__							=    sigma_hat if self.state_in_first_order else sigma_interpol
        UpperCAmelCase__							=    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
            sample / (sigma_input**2 + 1)
        )
       elif self.config.prediction_type == "sample":
        raise NotImplementedError("prediction_type not implemented yet: sample"   )
       else:
        raise ValueError(
            f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

       if self.state_in_first_order:
        # 2. Convert to an ODE derivative for 1st order
        UpperCAmelCase__							=    (sample - pred_original_sample) / sigma_hat
        # 3. delta timestep
        UpperCAmelCase__							=    sigma_interpol - sigma_hat

        # store for 2nd order step
        UpperCAmelCase__							=    sample
        UpperCAmelCase__							=    dt
        UpperCAmelCase__							=    sample + derivative * dt
       else:
        # DPM-Solver-2
        # 2. Convert to an ODE derivative for 2nd order
        UpperCAmelCase__							=    (sample - pred_original_sample) / sigma_interpol
        # 3. delta timestep
        UpperCAmelCase__							=    sigma_down - sigma_hat

        UpperCAmelCase__							=    self.sample
        UpperCAmelCase__							=    None

        UpperCAmelCase__							=    sample + derivative * dt
        UpperCAmelCase__							=    prev_sample + noise * sigma_up

       if not return_dict:
        return (prev_sample,)

       return SchedulerOutput(prev_sample=lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :torch.FloatTensor			,   lowerCamelCase :torch.FloatTensor			,   lowerCamelCase :torch.FloatTensor			,   )  ->      torch.FloatTensor:
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCAmelCase__							=    self.sigmas.to(device=original_samples.device			,   dtype=original_samples.dtype   )
       if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase   ):
        # mps does not support float64
        UpperCAmelCase__							=    self.timesteps.to(original_samples.device			,   dtype=torch.floataa   )
        UpperCAmelCase__							=    timesteps.to(original_samples.device			,   dtype=torch.floataa   )
       else:
        UpperCAmelCase__							=    self.timesteps.to(original_samples.device   )
        UpperCAmelCase__							=    timesteps.to(original_samples.device   )

       UpperCAmelCase__							=    [self.index_for_timestep(lowerCamelCase			,   lowerCamelCase   ) for t in timesteps]

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

       UpperCAmelCase__							=    original_samples + noise * sigma
       return noisy_samples







      def __len__(      self :str   )  ->      List[str]:
       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       _UpperCamelCase   (					tf.keras.layers.Layer		):
      def __init__(      self :int			,   lowerCamelCase :List			,   lowerCamelCase :bool			,   lowerCamelCase :int = None			,   lowerCamelCase :int = None			,   lowerCamelCase :int = None			,   lowerCamelCase :str = "longest"			,   lowerCamelCase :bool = True			,   lowerCamelCase :int = 512			,   lowerCamelCase :int = None			,   lowerCamelCase :bool = True			,   lowerCamelCase :bool = True			,   lowerCamelCase :bool = True			,   **lowerCamelCase :Optional[Any]			,   )  ->      List[str]:
       super().__init__()
       if use_fast_bert_tokenizer:
        UpperCAmelCase__							=    FastBertTokenizer(
            lowerCamelCase			,   token_out_type=tf.intaa			,   lower_case_nfd_strip_accents=lowerCamelCase			,   **lowerCamelCase   )
       else:
        UpperCAmelCase__							=    tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=lowerCamelCase			,   key_dtype=tf.string			,   values=tf.range(tf.size(lowerCamelCase			,   out_type=tf.intaa   )			,   dtype=tf.intaa   )			,   value_dtype=tf.intaa			,   )			,   num_oov_buckets=1			,   )
        UpperCAmelCase__							=    BertTokenizerLayer(
            lowerCamelCase			,   token_out_type=tf.intaa			,   lower_case=lowerCamelCase			,   **lowerCamelCase   )

       UpperCAmelCase__							=    vocab_list
       UpperCAmelCase__							=    do_lower_case
       UpperCAmelCase__							=    cls_token_id or vocab_list.index("[CLS]"   )
       UpperCAmelCase__							=    sep_token_id or vocab_list.index("[SEP]"   )
       UpperCAmelCase__							=    pad_token_id or vocab_list.index("[PAD]"   )
       UpperCAmelCase__							=    ShrinkLongestTrimmer(max_length - 3			,   axis=1   )  # Allow room for special tokens
       UpperCAmelCase__							=    max_length
       UpperCAmelCase__							=    padding
       UpperCAmelCase__							=    truncation
       UpperCAmelCase__							=    pad_to_multiple_of
       UpperCAmelCase__							=    return_token_type_ids
       UpperCAmelCase__							=    return_attention_mask
      @classmethod
      def 						UpperCAmelCase_    (      cls :List[str]			,   lowerCamelCase :"PreTrainedTokenizerBase"			,   **lowerCamelCase :Dict   )  ->      Dict:  # noqa: F821
       UpperCAmelCase__							=    kwargs.pop("do_lower_case"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
       UpperCAmelCase__							=    kwargs.pop("cls_token_id"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
       UpperCAmelCase__							=    kwargs.pop("sep_token_id"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
       UpperCAmelCase__							=    kwargs.pop("pad_token_id"			,   lowerCamelCase   )
       UpperCAmelCase__							=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

       UpperCAmelCase__							=    tokenizer.get_vocab()
       UpperCAmelCase__							=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,   key=lambda lowerCamelCase   : x[1]   )
       UpperCAmelCase__							=    [entry[0] for entry in vocab]
       return cls(
           vocab_list=lowerCamelCase			,   do_lower_case=lowerCamelCase			,   cls_token_id=lowerCamelCase			,   sep_token_id=lowerCamelCase			,   pad_token_id=lowerCamelCase			,   **lowerCamelCase			,   )
      @classmethod
      def 						UpperCAmelCase_    (      cls :Any			,   lowerCamelCase :Union[str, os.PathLike]			,   *lowerCamelCase :str			,   **lowerCamelCase :List[str]   )  ->      Tuple:
       try:
        UpperCAmelCase__							=    BertTokenizer.from_pretrained(lowerCamelCase			,   *lowerCamelCase			,   **lowerCamelCase   )
       except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        UpperCAmelCase__							=    BertTokenizerFast.from_pretrained(lowerCamelCase			,   *lowerCamelCase			,   **lowerCamelCase   )
       return cls.from_tokenizer(lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :int			,   lowerCamelCase :Optional[int]   )  ->      List[str]:
       if self.do_lower_case:
        UpperCAmelCase__							=    case_fold_utfa(lowerCamelCase   )
       UpperCAmelCase__							=    self.tf_tokenizer.tokenize(lowerCamelCase   )
       return tokens.merge_dims(1			,   -1   )
      def 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Tuple			,   lowerCamelCase :str=None			,   lowerCamelCase :Tuple=None			,   lowerCamelCase :Any=None			,   lowerCamelCase :Optional[int]=None			,   lowerCamelCase :Optional[Any]=None			,   lowerCamelCase :Optional[Any]=None			,   lowerCamelCase :Tuple=None			,   )  ->      str:
       if padding is None:
        UpperCAmelCase__							=    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:
        UpperCAmelCase__							=    self.max_length
       if truncation is None:
        UpperCAmelCase__							=    self.truncation
       if pad_to_multiple_of is None:
        UpperCAmelCase__							=    self.pad_to_multiple_of
       if return_token_type_ids is None:
        UpperCAmelCase__							=    self.return_token_type_ids
       if return_attention_mask is None:
        UpperCAmelCase__							=    self.return_attention_mask
       if not isinstance(lowerCamelCase			,   tf.Tensor   ):
        UpperCAmelCase__							=    tf.convert_to_tensor(lowerCamelCase   )
       if text_pair is not None and not isinstance(lowerCamelCase			,   tf.Tensor   ):
        UpperCAmelCase__							=    tf.convert_to_tensor(lowerCamelCase   )
       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:
        UpperCAmelCase__			,			UpperCAmelCase__							=    text[:, 0], text[:, 1]
       UpperCAmelCase__							=    self.unpaired_tokenize(lowerCamelCase   )
       if text_pair is None:  # Unpaired text
        if truncation:
         UpperCAmelCase__							=    text[:, : max_length - 2]  # Allow room for special tokens
        UpperCAmelCase__			,			UpperCAmelCase__							=    combine_segments(
            (text,)			,   start_of_sequence_id=self.cls_token_id			,   end_of_segment_id=self.sep_token_id   )
       else:  # Paired text
        UpperCAmelCase__							=    self.unpaired_tokenize(lowerCamelCase   )
        if truncation:
         UpperCAmelCase__			,			UpperCAmelCase__							=    self.paired_trimmer.trim([text, text_pair]   )
        UpperCAmelCase__			,			UpperCAmelCase__							=    combine_segments(
            (text, text_pair)			,   start_of_sequence_id=self.cls_token_id			,   end_of_segment_id=self.sep_token_id   )
       if padding == "longest":
        UpperCAmelCase__							=    input_ids.bounding_shape(axis=1   )
        if pad_to_multiple_of is not None:
         # No ceiling division in tensorflow, so we negate floordiv instead
         UpperCAmelCase__							=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,   lowerCamelCase   ))
       else:
        UpperCAmelCase__							=    max_length

       UpperCAmelCase__			,			UpperCAmelCase__							=    pad_model_inputs(lowerCamelCase			,   max_seq_length=lowerCamelCase			,   pad_value=self.pad_token_id   )
       UpperCAmelCase__							=    {"input_ids": input_ids}
       if return_attention_mask:
        UpperCAmelCase__							=    attention_mask
       if return_token_type_ids:
        UpperCAmelCase__			,			UpperCAmelCase__							=    pad_model_inputs(
            lowerCamelCase			,   max_seq_length=lowerCamelCase			,   pad_value=self.pad_token_id   )
        UpperCAmelCase__							=    token_type_ids
       return output







      def 						UpperCAmelCase_    (      self :Tuple   )  ->      Tuple:
       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,
       }





