






"""simple docstring"""







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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def _a			(		_snake_case				,	_snake_case=0.999				,	_snake_case="cosine"				,	):







      """simple docstring"""






      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      UpperCAmelCase          =	[]
      for i in range(__UpperCAmelCase							):
            UpperCAmelCase          =	i / num_diffusion_timesteps
            UpperCAmelCase          =	(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  lowerCamelCase__ (	_UpperCAmelCase   ,					_UpperCAmelCase			):
    SCREAMING_SNAKE_CASE		=      [e.name for e in KarrasDiffusionSchedulers]
    SCREAMING_SNAKE_CASE		=      2






    @register_to_config
    def __init__(			self     ,A = 1_000     ,A = 0.00085     ,A = 0.012     ,A = "linear"     ,A = None     ,A = "epsilon"     ,A = "linspace"     ,A = 0     ,):
          if trained_betas is not None:
                UpperCAmelCase          =	torch.tensor(_UpperCAmelCase     ,dtype=torch.floataa )
          elif beta_schedule == "linear":
                UpperCAmelCase          =	torch.linspace(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,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     ,_UpperCAmelCase     ,dtype=torch.floataa ) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                UpperCAmelCase          =	betas_for_alpha_bar(_UpperCAmelCase )
          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(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase )






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          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(_UpperCAmelCase ) > 1 else 0
          else:
                UpperCAmelCase          =	timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase ) else timestep
                UpperCAmelCase          =	self._index_counter[timestep_int]

          return indices[pos].item()






    @property
    def     _UpperCamelCase							(			self ):
          # 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     ,A     ,A     ,):
          UpperCAmelCase          =	self.index_for_timestep(_UpperCAmelCase )

          if self.state_in_first_order:
                UpperCAmelCase          =	self.sigmas[step_index]
          else:
                UpperCAmelCase          =	self.sigmas_interpol[step_index]

          UpperCAmelCase          =	sample / ((sigma**2 + 1) ** 0.5)
          return sample






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,):
          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     ,_UpperCAmelCase     ,dtype=_UpperCAmelCase )[::-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     ,_UpperCAmelCase ) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase )
                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(_UpperCAmelCase     ,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\'.''' )

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

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

          # interpolate sigmas
          UpperCAmelCase          =	sigmas.log().lerp(sigmas.roll(1 ).log()     ,0.5 ).exp()

          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:]] )

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

          # interpolate timesteps
          UpperCAmelCase          =	self.sigma_to_t(_UpperCAmelCase ).to(_UpperCAmelCase     ,dtype=timesteps.dtype )
          UpperCAmelCase          =	torch.stack((timesteps_interpol[1:-1, 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(_UpperCAmelCase )






    def     _UpperCamelCase							(			self     ,A ):
          # 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 ):
          return self.sample is None






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A = True     ,):
          UpperCAmelCase          =	self.index_for_timestep(_UpperCAmelCase )

          # advance index counter by 1
          UpperCAmelCase          =	timestep.cpu().item() if torch.is_tensor(_UpperCAmelCase ) 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 + 1]
                UpperCAmelCase          =	self.sigmas[step_index + 1]
          else:
                # 2nd order / KDPM2's method
                UpperCAmelCase          =	self.sigmas[step_index - 1]
                UpperCAmelCase          =	self.sigmas_interpol[step_index]
                UpperCAmelCase          =	self.sigmas[step_index]

          # 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

          # 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
          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_next - sigma_hat

                UpperCAmelCase          =	self.sample
                UpperCAmelCase          =	None

          UpperCAmelCase          =	sample + derivative * dt

          if not return_dict:
                return (prev_sample,)

          return SchedulerOutput(prev_sample=_UpperCAmelCase )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,):
          # 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(_UpperCAmelCase ):
                # 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(_UpperCAmelCase     ,_UpperCAmelCase ) 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 ):
          return self.config.num_train_timesteps











"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  _UpperCamelCase					      =  None


_UpperCamelCase					      =  logging.get_logger(__name__)


_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase					      =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

_UpperCamelCase					      =  {
    """google/pegasus-xsum""": 512,
}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      PegasusTokenizer
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A=None     ,A=None     ,A="<pad>"     ,A="</s>"     ,A="<unk>"     ,A="<mask_2>"     ,A="<mask_1>"     ,A=None     ,A=103     ,**A     ,):
          UpperCAmelCase          =	offset

          if additional_special_tokens is not None:
                if not isinstance(A     ,A ):
                      raise TypeError(
                          F'''additional_special_tokens should be of type {type(A )}, but is'''
                          F''' {type(A )}''' )

                UpperCAmelCase          =	(
                    ([mask_token_sent] + additional_special_tokens)
                    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                    else additional_special_tokens
                )
                # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                additional_special_tokens_extended += [
                    F'''<unk_{i}>''' for i in range(len(A )     ,self.offset - 1 )
                ]

                if len(set(A ) ) != len(A ):
                      raise ValueError(
                          """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
                          F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
                UpperCAmelCase          =	additional_special_tokens_extended
          else:
                UpperCAmelCase          =	[mask_token_sent] if mask_token_sent is not None else []
                additional_special_tokens += [F'''<unk_{i}>''' for i in range(2     ,self.offset )]

          super().__init__(
              A     ,tokenizer_file=A     ,pad_token=A     ,eos_token=A     ,unk_token=A     ,mask_token=A     ,mask_token_sent=A     ,offset=A     ,additional_special_tokens=A     ,**A     ,)
          UpperCAmelCase          =	vocab_file
          UpperCAmelCase          =	False if not self.vocab_file else True






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	set(self.all_special_ids )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
                raise ValueError(
                    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
                    F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )

          return [1 if x in all_special_ids else 0 for x in seq]






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):
          if already_has_special_tokens:
                return self._special_token_mask(A )
          elif token_ids_a is None:
                return self._special_token_mask(A ) + [1]
          else:
                return self._special_token_mask(token_ids_a + token_ids_a ) + [1]






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          if token_ids_a is None:
                return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                    """tokenizer.""" )

          if not os.path.isdir(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
                copyfile(self.vocab_file     ,A )

          return (out_vocab_file,)



