





# 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 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      42
      UpperCAmelCase__						=      None




def    __UpperCAmelCase(      lowercase_ ,    lowercase_=0.9_9_9 ,    lowercase_="cosine" ,    ):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowercase_      ):
                     return math.cos((t + 0.0_0_8) / 1.0_0_8 * 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}"""      )

       _lowerCamelCase							:     Union[str, Any]	=		[]
       for i in range(lowercase_      ):
              _lowerCamelCase							:     List[Any]	=		i / num_diffusion_timesteps
              _lowerCamelCase							:     Optional[Any]	=		(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 			__A       (      lowerCamelCase__ ,lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      1


      @register_to_config
      def __init__(		self      ,							a__ = 1000      ,							a__ = 0.0001      ,							a__ = 0.02      ,							a__ = "linear"      ,							a__ = None      ,							a__ = True      ,							a__ = True      ,							a__ = 0      ,							a__ = "epsilon"      ,							a__ = 1.0      ,							**a__      ,							):



             """simple docstring"""





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

             _lowerCamelCase							:     Optional[Any]	=		1.0 - self.betas
             _lowerCamelCase							:     Optional[int]	=		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.
             _lowerCamelCase							:     Tuple	=		torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

             # standard deviation of the initial noise distribution
             _lowerCamelCase							:     Any	=		1.0

             # setable values
             _lowerCamelCase							:     Union[str, Any]	=		None
             _lowerCamelCase							:     Dict	=		torch.from_numpy(np.arange(0      ,							a__).copy().astype(np.intaa))


      def 				__snake_case			(		self      ,							a__      ,							a__ = None):



             """simple docstring"""





             return sample


      def 				__snake_case			(		self      ,							a__      ,							a__ = None):



             """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.""")

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


      def 				__snake_case			(		self      ,							a__      ,							a__      ,							a__      ,							a__ = 0.0      ,							a__ = False      ,							a__ = None      ,							a__ = True      ,							):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		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
             _lowerCamelCase							:     Tuple	=		self.alphas_cumprod[timestep]
             _lowerCamelCase							:     str	=		(
                 self.alphas_cumprod[prev_timestep]
                 if prev_timestep < self.config.num_train_timesteps
                 else self.final_alpha_cumprod
             )

             _lowerCamelCase							:     Optional[int]	=		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":
                    _lowerCamelCase							:     Optional[int]	=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                    _lowerCamelCase							:     int	=		model_output
             elif self.config.prediction_type == "sample":
                    _lowerCamelCase							:     Union[str, Any]	=		model_output
                    _lowerCamelCase							:     str	=		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
             elif self.config.prediction_type == "v_prediction":
                    _lowerCamelCase							:     str	=		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                    _lowerCamelCase							:     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:
                    _lowerCamelCase							:     int	=		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
             _lowerCamelCase							:     str	=		(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
             _lowerCamelCase							:     Union[str, 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):



             """simple docstring"""





             return self.config.num_train_timesteps










import pytest

import datasets


# Import fixture modules as plugins
_lowerCamelCase			       =      ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
       for item in items:
              if any(marker in item.keywords for marker in ['''integration''', '''unit''']      ):
                     continue
              item.add_marker(pytest.mark.unit      )




def    __UpperCAmelCase(      lowercase_      ):
       config.addinivalue_line('''markers''' ,    '''torchaudio_latest: mark test to run with torchaudio>=0.12'''      )




@pytest.fixture(autouse=lowercase_      )
def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
       _lowerCamelCase							:     Optional[Any]	=		tmp_path_factory.getbasetemp() / '''cache'''
       _lowerCamelCase							:     Optional[Any]	=		test_hf_cache_home / '''datasets'''
       _lowerCamelCase							:     Union[str, Any]	=		test_hf_cache_home / '''metrics'''
       _lowerCamelCase							:     Dict	=		test_hf_cache_home / '''modules'''
       monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE''' ,    str(lowercase_      )      )
       monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE''' ,    str(lowercase_      )      )
       monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE''' ,    str(lowercase_      )      )
       _lowerCamelCase							:     str	=		test_hf_datasets_cache / '''downloads'''
       monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH''' ,    str(lowercase_      )      )
       _lowerCamelCase							:     Optional[int]	=		test_hf_datasets_cache / '''downloads''' / '''extracted'''
       monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH''' ,    str(lowercase_      )      )




@pytest.fixture(autouse=lowercase_ ,    scope='''session'''      )
def    __UpperCAmelCase(      ):
       datasets.disable_progress_bar()




@pytest.fixture(autouse=lowercase_      )
def    __UpperCAmelCase(      lowercase_      ):
       # don't take tests into account when counting downloads
       monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS''' ,    lowercase_      )




@pytest.fixture
def    __UpperCAmelCase(      lowercase_      ):
       # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
       # To be removed once SQLAlchemy 2.0 supported
       monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING''' ,    lowercase_      )

