
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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					torch.FloatTensor
		a							:					Optional[torch.FloatTensor]       =					None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   List[str]=0.999	,			_SCREAMING_SNAKE_CASE		:   Tuple="cosine"	,			):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_SCREAMING_SNAKE_CASE		:   Union[str, 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[Any]  ):
																		return math.exp(t * -12.0  )

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

						_SCREAMING_SNAKE_CASE				= []
						for i in range(_SCREAMING_SNAKE_CASE  ):
												_SCREAMING_SNAKE_CASE				= i / num_diffusion_timesteps
												_SCREAMING_SNAKE_CASE				= (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 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase			):
		a							:					int       =					1




		@register_to_config
		def __init__(				self					,						UpperCamelCase = 1_000					,						UpperCamelCase = 0.00_01					,						UpperCamelCase = 0.02					,						UpperCamelCase = "linear"					,						UpperCamelCase = None					,						UpperCamelCase = True					,						UpperCamelCase = True					,						UpperCamelCase = 0					,						UpperCamelCase = "epsilon"					,						UpperCamelCase = 1.0					,						**UpperCamelCase					,						):
								if kwargs.get("set_alpha_to_one"					,						UpperCamelCase      ) is not None:
														_SCREAMING_SNAKE_CASE				= (
														    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
														)
														deprecate("set_alpha_to_one"					,						"1.0.0"					,						UpperCamelCase					,						standard_warn=UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= kwargs["set_alpha_to_one"]
								if trained_betas is not None:
														_SCREAMING_SNAKE_CASE				= torch.tensor(UpperCamelCase					,						dtype=torch.floataa      )
								elif beta_schedule == "linear":
														_SCREAMING_SNAKE_CASE				= torch.linspace(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						dtype=torch.floataa      )
								elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														_SCREAMING_SNAKE_CASE				= (
														    torch.linspace(beta_start**0.5					,						beta_end**0.5					,						UpperCamelCase					,						dtype=torch.floataa      ) ** 2
														)
								elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														_SCREAMING_SNAKE_CASE				= betas_for_alpha_bar(UpperCamelCase      )
								else:
														raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'      )

								_SCREAMING_SNAKE_CASE				= 1.0 - self.betas
								_SCREAMING_SNAKE_CASE				= 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.
								_SCREAMING_SNAKE_CASE				= torch.tensor(0.0      ) if set_alpha_to_zero else self.alphas_cumprod[-1]

								# standard deviation of the initial noise distribution
								_SCREAMING_SNAKE_CASE				= 1.0

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								return sample




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):

								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.'      )

								_SCREAMING_SNAKE_CASE				= num_inference_steps
								_SCREAMING_SNAKE_CASE				= 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
								_SCREAMING_SNAKE_CASE				= (np.arange(0					,						UpperCamelCase      ) * step_ratio).round().copy().astype(np.intaa      )
								_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      ).to(UpperCamelCase      )
								self.timesteps += self.config.steps_offset




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = 0.0					,						UpperCamelCase = False					,						UpperCamelCase = None					,						UpperCamelCase = True					,						):
								# 1. get previous step value (=t+1)
								_SCREAMING_SNAKE_CASE				= 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
								_SCREAMING_SNAKE_CASE				= self.alphas_cumprod[timestep]
								_SCREAMING_SNAKE_CASE				= (
								    self.alphas_cumprod[prev_timestep]
								    if prev_timestep < self.config.num_train_timesteps
								    else self.final_alpha_cumprod
								)

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




		def __len__(				self      ):
								return self.config.num_train_timesteps

'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import GLPNImageProcessor


class 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=3					,						UpperCamelCase=18					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=32					,						UpperCamelCase=True					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= min_resolution
								_SCREAMING_SNAKE_CASE				= max_resolution
								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size_divisor
								_SCREAMING_SNAKE_CASE				= do_rescale




		def     lowercase       (				self      ):
								return {
								    "do_resize": self.do_resize,
								    "size_divisor": self.size_divisor,
								    "do_rescale": self.do_rescale,
								}


@require_torch
@require_vision
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Dict       =					GLPNImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= GLPNImageProcessingTester(self      )




		@property
		def     lowercase       (				self      ):
								return self.image_processor_tester.prepare_image_processor_dict()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_resize"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"size_divisor"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"resample"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"do_rescale"      )      )




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						Image.Image      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						numpify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						np.ndarray      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




		def     lowercase       (				self      ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase					,						torchify=UpperCamelCase      )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase					,						torch.Tensor      )

								# Test not batched input (GLPNImageProcessor doesn't support batching)
								_SCREAMING_SNAKE_CASE				= image_processing(image_inputs[0]					,						return_tensors="pt"      ).pixel_values
								self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
								self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )