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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


a							=logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        __UpperCAmelCase						(     lowerCAmelCase__     ):
				A__				:       torch.FloatTensor
				A__				:       Optional[torch.FloatTensor]  							=	None





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase=0.999       ,   __lowerCAmelCase="cosine"       ,   )							-> List[str]:




						'''simple docstring'''



						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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






class        __UpperCAmelCase						(     lowerCAmelCase__				,				lowerCAmelCase__     ):
				A__				:       str  							=	[e.name for e in KarrasDiffusionSchedulers]
				A__				:       Union[str, Any]  							=	1

				@register_to_config
				def __init__(				self     ,			_lowerCamelCase = 1000     ,			_lowerCamelCase = 0.0_0_0_1     ,			_lowerCamelCase = 0.0_2     ,			_lowerCamelCase = "linear"     ,			_lowerCamelCase = None     ,			_lowerCamelCase = "epsilon"     ,			_lowerCamelCase = "linspace"     ,			_lowerCamelCase = 0     ,			):


										if trained_betas is not None:
																lowerCamelCase__ =torch.tensor(_UpperCAmelCase     ,			dtype=torch.floataa				)
										elif beta_schedule == "linear":
																lowerCamelCase__ =torch.linspace(_UpperCAmelCase     ,			_UpperCAmelCase     ,			_UpperCAmelCase     ,			dtype=torch.floataa				)
										elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																lowerCamelCase__ =(
																    torch.linspace(beta_start**0.5     ,			beta_end**0.5     ,			_UpperCAmelCase     ,			dtype=torch.floataa				) ** 2
																)
										elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																lowerCamelCase__ =betas_for_alpha_bar(_UpperCAmelCase				)
										else:
																raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''				)

										lowerCamelCase__ =1.0 - self.betas
										lowerCamelCase__ =torch.cumprod(self.alphas     ,			dim=0				)

										lowerCamelCase__ =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
										lowerCamelCase__ =np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
										lowerCamelCase__ =torch.from_numpy(_UpperCAmelCase				)

										# setable values
										lowerCamelCase__ =None
										lowerCamelCase__ =np.linspace(0     ,			num_train_timesteps - 1     ,			_UpperCAmelCase     ,			dtype=_UpperCAmelCase				)[::-1].copy()
										lowerCamelCase__ =torch.from_numpy(_UpperCAmelCase				)
										lowerCamelCase__ =False

				@property
				def 		_a     (				self				):


										if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):


										if isinstance(_UpperCAmelCase     ,			torch.Tensor				):
																lowerCamelCase__ =timestep.to(self.timesteps.device				)
										lowerCamelCase__ =(self.timesteps == timestep).nonzero().item()
										lowerCamelCase__ =self.sigmas[step_index]
										lowerCamelCase__ =sample / ((sigma**2 + 1) ** 0.5)
										lowerCamelCase__ =True
										return sample

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase = None				):
										lowerCamelCase__ =num_inference_steps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																lowerCamelCase__ =np.linspace(0     ,			self.config.num_train_timesteps - 1     ,			_UpperCAmelCase     ,			dtype=_UpperCAmelCase				)[
																    ::-1
																].copy()
										elif self.config.timestep_spacing == "leading":
																lowerCamelCase__ =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__ =(np.arange(0     ,			_UpperCAmelCase				) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase				)
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																lowerCamelCase__ =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__ =(np.arange(self.config.num_train_timesteps     ,			0     ,			-step_ratio				)).round().copy().astype(_UpperCAmelCase				)
																timesteps -= 1
										else:
																raise ValueError(
																    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''				)

										lowerCamelCase__ =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
										lowerCamelCase__ =np.interp(_UpperCAmelCase     ,			np.arange(0     ,			len(_UpperCAmelCase				)				)     ,			_UpperCAmelCase				)
										lowerCamelCase__ =np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
										lowerCamelCase__ =torch.from_numpy(_UpperCAmelCase				).to(device=_UpperCAmelCase				)
										if str(_UpperCAmelCase				).startswith("mps"				):
																# mps does not support float64
																lowerCamelCase__ =torch.from_numpy(_UpperCAmelCase				).to(_UpperCAmelCase     ,			dtype=torch.floataa				)
										else:
																lowerCamelCase__ =torch.from_numpy(_UpperCAmelCase				).to(device=_UpperCAmelCase				)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			):


										if (
										    isinstance(_UpperCAmelCase     ,			_UpperCAmelCase				)
										    or isinstance(_UpperCAmelCase     ,			torch.IntTensor				)
										    or isinstance(_UpperCAmelCase     ,			torch.LongTensor				)
										):
																raise ValueError(
																    (
																        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
																        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
																        " one of the `scheduler.timesteps` as a timestep."
																    )     ,			)

										if not self.is_scale_input_called:
																logger.warning(
																    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
																    "See `StableDiffusionPipeline` for a usage example."				)

										if isinstance(_UpperCAmelCase     ,			torch.Tensor				):
																lowerCamelCase__ =timestep.to(self.timesteps.device				)

										lowerCamelCase__ =(self.timesteps == timestep).nonzero().item()
										lowerCamelCase__ =self.sigmas[step_index]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
																lowerCamelCase__ =sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																lowerCamelCase__ =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										elif self.config.prediction_type == "sample":
																raise NotImplementedError("prediction_type not implemented yet: sample"				)
										else:
																raise ValueError(
																    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''				)

										lowerCamelCase__ =self.sigmas[step_index]
										lowerCamelCase__ =self.sigmas[step_index + 1]
										lowerCamelCase__ =(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
										lowerCamelCase__ =(sigma_to**2 - sigma_up**2) ** 0.5

										# 2. Convert to an ODE derivative
										lowerCamelCase__ =(sample - pred_original_sample) / sigma

										lowerCamelCase__ =sigma_down - sigma

										lowerCamelCase__ =sample + derivative * dt

										lowerCamelCase__ =model_output.device
										lowerCamelCase__ =randn_tensor(model_output.shape     ,			dtype=model_output.dtype     ,			device=_UpperCAmelCase     ,			generator=_UpperCAmelCase				)

										lowerCamelCase__ =prev_sample + noise * sigma_up

										if not return_dict:
																return (prev_sample,)

										return EulerAncestralDiscreteSchedulerOutput(
										    prev_sample=_UpperCAmelCase     ,			pred_original_sample=_UpperCAmelCase				)

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
										lowerCamelCase__ =self.sigmas.to(device=original_samples.device     ,			dtype=original_samples.dtype				)
										if original_samples.device.type == "mps" and torch.is_floating_point(_UpperCAmelCase				):
																# mps does not support float64
																lowerCamelCase__ =self.timesteps.to(original_samples.device     ,			dtype=torch.floataa				)
																lowerCamelCase__ =timesteps.to(original_samples.device     ,			dtype=torch.floataa				)
										else:
																lowerCamelCase__ =self.timesteps.to(original_samples.device				)
																lowerCamelCase__ =timesteps.to(original_samples.device				)

										lowerCamelCase__ =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

										lowerCamelCase__ =sigmas[step_indices].flatten()
										while len(sigma.shape				) < len(original_samples.shape				):
																lowerCamelCase__ =sigma.unsqueeze(-1				)

										lowerCamelCase__ =original_samples + noise * sigma
										return noisy_samples






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



"""simple docstring"""


import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class        __UpperCAmelCase						(     __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase     ):
				A__				:       Any  							=	IFSuperResolutionPipeline
				A__				:       Tuple  							=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
				A__				:       Optional[int]  							=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				A__				:       List[Any]  							=	PipelineTesterMixin.required_optional_params - {'''latents'''}

				def 		_a     (				self				):
										return self._get_superresolution_dummy_components()

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase=0				):
										if str(_lowerCamelCase				).startswith("mps"				):
																lowerCamelCase__ =torch.manual_seed(_lowerCamelCase				)
										else:
																lowerCamelCase__ =torch.Generator(device=_lowerCamelCase				).manual_seed(_lowerCamelCase				)

										lowerCamelCase__ =floats_tensor((1, 3, 32, 32)     ,			rng=random.Random(_lowerCamelCase				)				).to(_lowerCamelCase				)

										lowerCamelCase__ ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs

				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()     ,			reason="XFormers attention is only available with CUDA and `xformers` installed"     ,			)
				def 		_a     (				self				):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

				def 		_a     (				self				):
										self._test_save_load_optional_components()

				@unittest.skipIf(torch_device != "cuda"     ,			reason="float16 requires CUDA"				)
				def 		_a     (				self				):
										# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
										super().test_save_load_floataa(expected_max_diff=1E-1				)

				def 		_a     (				self				):
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)

				def 		_a     (				self				):
										self._test_save_load_local()






				def 		_a     (				self				):
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2     ,			)


