
"""simple docstring"""







# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




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




def       A_      (       lowercase    ,			lowercase=0.999    ,			lowercase="cosine"    ,			)  ->				Any:


     """simple docstring"""







     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

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

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



class 		UpperCAmelCase_						(lowerCamelCase_	,			lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  List[Any]					 =     1




 @register_to_config
 def __init__(			self							:			Dict  , a_							:			int = 10_00  , a_							:			float = 0.0001  , a_							:			float = 0.02  , a_							:			str = "linear"  , a_							:			Optional[Union[np.ndarray, List[float]]] = None  , a_							:			bool = True  , a_							:			bool = True  , a_							:			int = 0  , a_							:			str = "epsilon"  , a_							:			float = 1.0  , **a_							:			Optional[Any]  , )->       str:



      """simple docstring"""

      if kwargs.get("""set_alpha_to_one"""  , a_			) is not None:
           UpperCAmelCase_    :     Optional[int]            =	(
               """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
           )
           deprecate("""set_alpha_to_one"""  , """1.0.0"""  , a_  , standard_warn=a_			)
           UpperCAmelCase_    :     Dict            =	kwargs["""set_alpha_to_one"""]
      if trained_betas is not None:
           UpperCAmelCase_    :     List[Any]            =	torch.tensor(a_  , dtype=torch.floataa			)
      elif beta_schedule == "linear":
           UpperCAmelCase_    :     Union[str, Any]            =	torch.linspace(a_  , a_  , a_  , dtype=torch.floataa			)
      elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           UpperCAmelCase_    :     List[str]            =	(
               torch.linspace(beta_start**0.5  , beta_end**0.5  , a_  , dtype=torch.floataa			) ** 2
           )
      elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           UpperCAmelCase_    :     int            =	betas_for_alpha_bar(a_			)
      else:
           raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

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

      # At every step in inverted ddim, we are looking into the next alphas_cumprod
      # For the final step, there is no next alphas_cumprod, and the index is out of bounds
      # `set_alpha_to_zero` decides whether we set this parameter simply to zero
      # in this case, self.step() just output the predicted noise
      # or whether we use the final alpha of the "non-previous" one.
      UpperCAmelCase_    :     int            =	torch.tensor(0.0			) if set_alpha_to_zero else self.alphas_cumprod[-1]

      # standard deviation of the initial noise distribution
      UpperCAmelCase_    :     Optional[Any]            =	1.0

      # setable values
      UpperCAmelCase_    :     Optional[int]            =	None
      UpperCAmelCase_    :     str            =	torch.from_numpy(np.arange(0  , a_			).copy().astype(np.intaa			)			)




 def 	a   (			self							:			Any  , a_							:			torch.FloatTensor  , a_							:			Optional[int] = None			)->       torch.FloatTensor:



      """simple docstring"""

      return sample




 def 	a   (			self							:			int  , a_							:			int  , a_							:			Union[str, torch.device] = None			)->       Optional[Any]:



      """simple docstring"""

      if num_inference_steps > self.config.num_train_timesteps:
           raise ValueError(
               f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
               f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
               f''' maximal {self.config.num_train_timesteps} timesteps.'''			)

      UpperCAmelCase_    :     int            =	num_inference_steps
      UpperCAmelCase_    :     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
      UpperCAmelCase_    :     Union[str, Any]            =	(np.arange(0  , a_			) * step_ratio).round().copy().astype(np.intaa			)
      UpperCAmelCase_    :     int            =	torch.from_numpy(a_			).to(a_			)
      self.timesteps += self.config.steps_offset




 def 	a   (			self							:			Tuple  , a_							:			torch.FloatTensor  , a_							:			int  , a_							:			torch.FloatTensor  , a_							:			float = 0.0  , a_							:			bool = False  , a_							:			Optional[torch.FloatTensor] = None  , a_							:			bool = True  , )->       Union[DDIMSchedulerOutput, Tuple]:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	timestep + self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      # change original implementation to exactly match noise levels for analogous forward process
      UpperCAmelCase_    :     Dict            =	self.alphas_cumprod[timestep]
      UpperCAmelCase_    :     Union[str, Any]            =	(
          self.alphas_cumprod[prev_timestep]
          if prev_timestep < self.config.num_train_timesteps
          else self.final_alpha_cumprod
      )

      UpperCAmelCase_    :     List[str]            =	1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      if self.config.prediction_type == "epsilon":
           UpperCAmelCase_    :     Optional[int]            =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           UpperCAmelCase_    :     Any            =	model_output
      elif self.config.prediction_type == "sample":
           UpperCAmelCase_    :     Union[str, Any]            =	model_output
           UpperCAmelCase_    :     List[str]            =	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
           UpperCAmelCase_    :     Any            =	(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           UpperCAmelCase_    :     List[Any]            =	(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
      else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
               """ `v_prediction`"""			)

      # 4. Clip or threshold "predicted x_0"
      if self.config.clip_sample:
           UpperCAmelCase_    :     Tuple            =	pred_original_sample.clamp(
               -self.config.clip_sample_range  , self.config.clip_sample_range			)

      # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      UpperCAmelCase_    :     int            =	(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

      # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      UpperCAmelCase_    :     Any            =	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if not return_dict:
           return (prev_sample, pred_original_sample)
      return DDIMSchedulerOutput(prev_sample=a_  , pred_original_sample=a_			)






 def __len__(			self							:			Optional[Any]			)->       Optional[int]:



      """simple docstring"""

      return self.config.num_train_timesteps



"""simple docstring"""







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
       import torch

       from transformers.generation import DisjunctiveConstraint



@require_torch
class 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




 def 	a   (			self							:			Any			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	[[1, 2, 4], [1, 2, 3, 4]]
      UpperCAmelCase_    :     Union[str, Any]            =	DisjunctiveConstraint(a_			)
      self.assertTrue(isinstance(dc.token_ids  , a_			)			)

      with self.assertRaises(a_			):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]			)			)

      with self.assertRaises(a_			):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4]			), torch.LongTensor([1, 2, 3, 4, 5]			)]			)




 def 	a   (			self							:			List[str]			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	[[1, 2], [1, 2, 3, 4]]

      with self.assertRaises(a_			):
           DisjunctiveConstraint(a_			)  # fails here




 def 	a   (			self							:			Dict			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	[[1, 2, 3], [1, 2, 4]]

      UpperCAmelCase_    :     Dict            =	DisjunctiveConstraint(a_			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     int            =	dc.update(1			)
      UpperCAmelCase_    :     str            =	stepped is True and completed is False and reset is False
      self.assertTrue(a_			)
      self.assertTrue(not dc.completed			)
      self.assertTrue(dc.current_seq == [1]			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     Optional[int]            =	dc.update(2			)
      UpperCAmelCase_    :     Tuple            =	stepped is True and completed is False and reset is False
      self.assertTrue(a_			)
      self.assertTrue(not dc.completed			)
      self.assertTrue(dc.current_seq == [1, 2]			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     Union[str, Any]            =	dc.update(3			)
      UpperCAmelCase_    :     str            =	stepped is True and completed is True and reset is False
      self.assertTrue(a_			)
      self.assertTrue(dc.completed			)  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 3]			)






 def 	a   (			self							:			List[Any]			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

      UpperCAmelCase_    :     Tuple            =	DisjunctiveConstraint(a_			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     List[Any]            =	dc.update(1			)
      self.assertTrue(not dc.completed			)
      self.assertTrue(dc.current_seq == [1]			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     Tuple            =	dc.update(2			)
      self.assertTrue(not dc.completed			)
      self.assertTrue(dc.current_seq == [1, 2]			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     List[str]            =	dc.update(4			)
      self.assertTrue(not dc.completed			)
      self.assertTrue(dc.current_seq == [1, 2, 4]			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     str            =	dc.update(5			)
      self.assertTrue(dc.completed			)  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 4, 5]			)

      dc.reset()

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     Any            =	dc.update(1			)
      self.assertTrue(not dc.completed			)
      self.assertTrue(dc.remaining() == 3			)
      self.assertTrue(dc.current_seq == [1]			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     Any            =	dc.update(2			)
      self.assertTrue(not dc.completed			)
      self.assertTrue(dc.remaining() == 2			)
      self.assertTrue(dc.current_seq == [1, 2]			)

      UpperCAmelCase_       ,UpperCAmelCase_       ,UpperCAmelCase_    :     List[str]            =	dc.update(5			)
      self.assertTrue(dc.completed			)  # Completed!
      self.assertTrue(dc.remaining() == 0			)
      self.assertTrue(dc.current_seq == [1, 2, 5]			)

