

'''simple docstring'''






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							42
    _A			    =							42






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Tuple  ,  SCREAMING_SNAKE_CASE_     :	int=0.999  ,  SCREAMING_SNAKE_CASE_     :	Any="cosine"  ,  )							->   int:
      """simple docstring"""

      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(SCREAMING_SNAKE_CASE_     :	List[str]    ):
                  return math.exp(t * -12.0    )

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

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





class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase,_UpperCAmelCase      ):
    _A			    =							1




    @register_to_config
    def __init__(					self				,     lowercase__ = 1000				,     lowercase__ = 0.0001				,     lowercase__ = 0.02				,     lowercase__ = "linear"				,     lowercase__ = 0.0				,     lowercase__ = None				,     lowercase__ = True				,     ):






          """simple docstring"""






          if trained_betas is not None:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.from_numpy(lowercase__			)
          elif beta_schedule == "linear":
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       torch.linspace(lowercase__				,     lowercase__				,     lowercase__				,     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				,     lowercase__				,     dtype=torch.floataa			) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                SCREAMING_SNAKE_CASE_      :			str     =       betas_for_alpha_bar(lowercase__			)
          elif beta_schedule == "sigmoid":
                # GeoDiff sigmoid schedule
                SCREAMING_SNAKE_CASE_      :			Dict     =       torch.linspace(-6				,     6				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			str     =       torch.sigmoid(lowercase__			) * (beta_end - beta_start) + beta_start
          else:
                raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       1.0 - self.betas
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.cumprod(self.alphas				,     dim=0			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.tensor(1.0			)

          SCREAMING_SNAKE_CASE_      :			int     =       torch.tensor(1.0			)

          # standard deviation of the initial noise distribution
          SCREAMING_SNAKE_CASE_      :			List[str]     =       1.0

          # setable values
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       None
          SCREAMING_SNAKE_CASE_      :			int     =       torch.from_numpy(np.arange(0				,     lowercase__			)[::-1].copy()			)

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       eta




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






          return sample




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = 10				,     lowercase__ = 10				,     lowercase__ = None				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       min(self.config.num_train_timesteps				,     lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       num_inference_steps

          SCREAMING_SNAKE_CASE_      :			Tuple     =       []

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       {}
          for j in range(0				,     num_inference_steps - jump_length				,     lowercase__			):
                SCREAMING_SNAKE_CASE_      :			int     =       jump_n_sample - 1

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       num_inference_steps
          while t >= 1:
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       t - 1
                timesteps.append(lowercase__			)

                if jumps.get(lowercase__				,     0			) > 0:
                      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       jumps[t] - 1
                      for _ in range(lowercase__			):
                            SCREAMING_SNAKE_CASE_      :			Optional[int]     =       t + 1
                            timesteps.append(lowercase__			)

          SCREAMING_SNAKE_CASE_      :			List[str]     =       np.array(lowercase__			) * (self.config.num_train_timesteps // self.num_inference_steps)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.from_numpy(lowercase__			).to(lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Dict     =       t - self.config.num_train_timesteps // self.num_inference_steps

          SCREAMING_SNAKE_CASE_      :			Any     =       self.alphas_cumprod[t]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       1 - alpha_prod_t
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       1 - alpha_prod_t_prev

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from
          # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
          # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
          # variance to pred_sample
          # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
          # without eta.
          # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
          SCREAMING_SNAKE_CASE_      :			str     =       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

          return variance




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     lowercase__ = True				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       timestep
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       timestep - self.config.num_train_timesteps // self.num_inference_steps

          # 1. compute alphas, betas
          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.alphas_cumprod[t]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
          SCREAMING_SNAKE_CASE_      :			str     =       1 - alpha_prod_t

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       torch.clamp(lowercase__				,     -1				,     1			)

          # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
          # substitute formula (7) in the algorithm coming from DDPM paper
          # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
          # DDIM schedule gives the same results as DDPM with eta = 1.0
          # Noise is being reused in 7. and 8., but no impact on quality has
          # been observed.

          # 5. Add noise
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       model_output.device
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       randn_tensor(model_output.shape				,     generator=lowercase__				,     device=lowercase__				,     dtype=model_output.dtype			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.eta * self._get_variance(lowercase__			) ** 0.5

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       0
          if t > 0 and self.eta > 0:
                SCREAMING_SNAKE_CASE_      :			List[str]     =       std_dev_t * noise

          # 6. compute "direction pointing to x_t" of formula (12)
          # from https://arxiv.org/pdf/2010.02502.pdf
          SCREAMING_SNAKE_CASE_      :			Dict     =       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

          # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

          # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
          SCREAMING_SNAKE_CASE_      :			str     =       (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

          # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       mask * prev_known_part + (1.0 - mask) * prev_unknown_part

          if not return_dict:
                return (
                    pred_prev_sample,
                    pred_original_sample,
                )

          return RePaintSchedulerOutput(prev_sample=lowercase__				,     pred_original_sample=lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__=None			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.config.num_train_timesteps // self.num_inference_steps

          for i in range(lowercase__			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       self.betas[timestep + i]
                if sample.device.type == "mps":
                      # randn does not work reproducibly on mps
                      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       randn_tensor(sample.shape				,     dtype=sample.dtype				,     generator=lowercase__			)
                      SCREAMING_SNAKE_CASE_      :			Any     =       noise.to(sample.device			)
                else:
                      SCREAMING_SNAKE_CASE_      :			List[Any]     =       randn_tensor(sample.shape				,     generator=lowercase__				,     device=sample.device				,     dtype=sample.dtype			)

                # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       (1 - beta) ** 0.5 * sample + beta**0.5 * noise

          return sample




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__				,     ):






          """simple docstring"""






          raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)







    def __len__(					self			):






          """simple docstring"""






          return self.config.num_train_timesteps







'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_				:     Dict     			=						{
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_				:     List[Any]     			=						['''MarkupLMTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_				:     int     			=						[
         '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MarkupLMForQuestionAnswering''',
         '''MarkupLMForSequenceClassification''',
         '''MarkupLMForTokenClassification''',
         '''MarkupLMModel''',
         '''MarkupLMPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
     from .feature_extraction_markuplm import MarkupLMFeatureExtractor
     from .processing_markuplm import MarkupLMProcessor
     from .tokenization_markuplm import MarkupLMTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_markuplm_fast import MarkupLMTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_markuplm import (
              MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarkupLMForQuestionAnswering,
              MarkupLMForSequenceClassification,
              MarkupLMForTokenClassification,
              MarkupLMModel,
              MarkupLMPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_				:     Dict     			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)






