# 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   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	torch.FloatTensor
  SCREAMING_SNAKE_CASE_	:	Optional[torch.FloatTensor]						=None







def  __UpperCamelCase    (						A ,							A=0.9_99 ,							A="cosine" ,							):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(A							):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

     elif alpha_transform_type == "exp":

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

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

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



class     _A   (    __UpperCamelCase	, __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	int						=1






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





       '''simple docstring'''





       if kwargs.get('''set_alpha_to_one'''   ,				SCREAMING_SNAKE_CASE_	) is not None:
            UpperCamelCase__										=      (
                '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
            )
            deprecate('''set_alpha_to_one'''   ,				'''1.0.0'''   ,				SCREAMING_SNAKE_CASE_   ,				standard_warn=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      kwargs['''set_alpha_to_one''']
       if trained_betas is not None:
            UpperCamelCase__										=      torch.tensor(SCREAMING_SNAKE_CASE_   ,				dtype=torch.floataa	)
       elif beta_schedule == "linear":
            UpperCamelCase__										=      torch.linspace(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				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   ,				SCREAMING_SNAKE_CASE_   ,				dtype=torch.floataa	) ** 2
            )
       elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            UpperCamelCase__										=      betas_for_alpha_bar(SCREAMING_SNAKE_CASE_	)
       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	)

       # 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__										=      torch.tensor(0.0	) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       UpperCamelCase__										=      1.0

       # setable values
       UpperCamelCase__										=      None
       UpperCamelCase__										=      torch.from_numpy(np.arange(0   ,				SCREAMING_SNAKE_CASE_	).copy().astype(np.intaa	)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      torch.FloatTensor:





       '''simple docstring'''





       return sample






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      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__										=      num_inference_steps
       UpperCamelCase__										=      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__										=      (np.arange(0   ,				SCREAMING_SNAKE_CASE_	) * step_ratio).round().copy().astype(np.intaa	)
       UpperCamelCase__										=      torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_	)
       self.timesteps += self.config.steps_offset






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = 0.0   ,				SCREAMING_SNAKE_CASE_ = False   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = True   ,				)		->      Union[DDIMSchedulerOutput, Tuple]:





       '''simple docstring'''





       UpperCamelCase__										=      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__										=      self.alphas_cumprod[timestep]
       UpperCamelCase__										=      (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       UpperCamelCase__										=      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__										=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            UpperCamelCase__										=      model_output
       elif self.config.prediction_type == "sample":
            UpperCamelCase__										=      model_output
            UpperCamelCase__										=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
            UpperCamelCase__										=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            UpperCamelCase__										=      (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__										=      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__										=      (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__										=      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=SCREAMING_SNAKE_CASE_   ,				pred_original_sample=SCREAMING_SNAKE_CASE_	)






  def __len__(self	)		->      Union[str, Any]:





       '''simple docstring'''





       return self.config.num_train_timesteps




__magic_name__								={
    '''Pillow''': '''Pillow<10.0.0''',
    '''accelerate''': '''accelerate>=0.20.3''',
    '''av''': '''av==9.2.0''',
    '''beautifulsoup4''': '''beautifulsoup4''',
    '''black''': '''black~=23.1''',
    '''codecarbon''': '''codecarbon==1.2.0''',
    '''cookiecutter''': '''cookiecutter==1.7.3''',
    '''dataclasses''': '''dataclasses''',
    '''datasets''': '''datasets!=2.5.0''',
    '''decord''': '''decord==0.6.0''',
    '''deepspeed''': '''deepspeed>=0.9.3''',
    '''diffusers''': '''diffusers''',
    '''dill''': '''dill<0.3.5''',
    '''evaluate''': '''evaluate>=0.2.0''',
    '''fairscale''': '''fairscale>0.3''',
    '''faiss-cpu''': '''faiss-cpu''',
    '''fastapi''': '''fastapi''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1,<=0.7.0''',
    '''ftfy''': '''ftfy''',
    '''fugashi''': '''fugashi>=1.0''',
    '''GitPython''': '''GitPython<3.1.19''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''ipadic''': '''ipadic>=1.0.0,<2.0''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
    '''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
    '''jieba''': '''jieba''',
    '''kenlm''': '''kenlm''',
    '''keras-nlp''': '''keras-nlp>=0.3.1''',
    '''librosa''': '''librosa''',
    '''nltk''': '''nltk''',
    '''natten''': '''natten>=0.14.6''',
    '''numpy''': '''numpy>=1.17''',
    '''onnxconverter-common''': '''onnxconverter-common''',
    '''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
    '''onnxruntime''': '''onnxruntime>=1.4.0''',
    '''opencv-python''': '''opencv-python''',
    '''optuna''': '''optuna''',
    '''optax''': '''optax>=0.0.8,<=0.1.4''',
    '''packaging''': '''packaging>=20.0''',
    '''parameterized''': '''parameterized''',
    '''phonemizer''': '''phonemizer''',
    '''protobuf''': '''protobuf''',
    '''psutil''': '''psutil''',
    '''pyyaml''': '''pyyaml>=5.1''',
    '''pydantic''': '''pydantic<2''',
    '''pytest''': '''pytest>=7.2.0''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''python''': '''python>=3.8.0''',
    '''ray[tune]''': '''ray[tune]''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
    '''rjieba''': '''rjieba''',
    '''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
    '''ruff''': '''ruff>=0.0.241,<=0.0.259''',
    '''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
    '''sacremoses''': '''sacremoses''',
    '''safetensors''': '''safetensors>=0.3.1''',
    '''sagemaker''': '''sagemaker>=2.31.0''',
    '''scikit-learn''': '''scikit-learn''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''sigopt''': '''sigopt''',
    '''starlette''': '''starlette''',
    '''sudachipy''': '''sudachipy>=0.6.6''',
    '''sudachidict_core''': '''sudachidict_core>=20220729''',
    '''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
    '''tensorflow''': '''tensorflow>=2.6,<2.14''',
    '''tensorflow-text''': '''tensorflow-text<2.14''',
    '''tf2onnx''': '''tf2onnx''',
    '''timeout-decorator''': '''timeout-decorator''',
    '''timm''': '''timm''',
    '''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
    '''torch''': '''torch>=1.9,!=1.12.0''',
    '''torchaudio''': '''torchaudio''',
    '''torchvision''': '''torchvision''',
    '''pyctcdecode''': '''pyctcdecode>=0.4.0''',
    '''tqdm''': '''tqdm>=4.27''',
    '''unidic''': '''unidic>=1.0.2''',
    '''unidic_lite''': '''unidic_lite>=1.0.7''',
    '''urllib3''': '''urllib3<2.0.0''',
    '''uvicorn''': '''uvicorn''',
}



