"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=0.999		,					_SCREAMING_SNAKE_CASE="cosine"		,					)     ->Dict:
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		a__:					List[str]      							=     []
		for i in range(__SCREAMING_SNAKE_CASE       ):
				a__:					Tuple      							=     i / num_diffusion_timesteps
				a__:					Dict      							=     (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        __snake_case	(					__UpperCamelCase	,				__UpperCamelCase    ):
						a__				=		[e.name for e in KarrasDiffusionSchedulers]
						a__				=		2



						@register_to_config
						def __init__(						self   ,      lowercase = 10_00   ,      lowercase = 0.00085   ,      lowercase = 0.012   ,      lowercase = "linear"   ,      lowercase = None   ,      lowercase = "epsilon"   ,      lowercase = False   ,      lowercase = False   ,      lowercase = 1.0   ,      lowercase = "linspace"   ,      lowercase = 0   ,      )     ->      Tuple:





								'''simple docstring'''





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

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

								#  set all values
								self.set_timesteps(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase)
								a__:					Optional[Any]      							=     use_karras_sigmas



						def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=None)     ->      Optional[int]:





								'''simple docstring'''





								if schedule_timesteps is None:
										a__:					List[str]      							=     self.timesteps

								a__:					str      							=     (schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter) == 0:
										a__:					Union[str, Any]      							=     1 if len(_lowerCAmelCase) > 1 else 0
								else:
										a__:					List[Any]      							=     timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase) else timestep
										a__:					int      							=     self._index_counter[timestep_int]

								return indices[pos].item()



						@property
						def   lowerCamelCase_       (						self)     ->      List[Any]:





								'''simple docstring'''





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

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



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





								'''simple docstring'''





								a__:					Dict      							=     self.index_for_timestep(_lowerCAmelCase)

								a__:					Optional[Any]      							=     self.sigmas[step_index]
								a__:					str      							=     sample / ((sigma**2 + 1) ** 0.5)
								return sample



						def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None   ,      lowercase = None   ,      )     ->      str:





								'''simple docstring'''





								a__:					int      							=     num_inference_steps

								a__:					Dict      							=     num_train_timesteps or self.config.num_train_timesteps

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

								a__:					str      							=     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								a__:					Optional[int]      							=     np.log(_lowerCAmelCase)
								a__:					Tuple      							=     np.interp(_lowerCAmelCase   ,      np.arange(0   ,      len(_lowerCAmelCase))   ,      _lowerCAmelCase)

								if self.config.use_karras_sigmas:
										a__:					List[str]      							=     self._convert_to_karras(in_sigmas=_lowerCAmelCase   ,      num_inference_steps=self.num_inference_steps)
										a__:					Dict      							=     np.array([self._sigma_to_t(_lowerCAmelCase   ,      _lowerCAmelCase) for sigma in sigmas])

								a__:					Dict      							=     np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								a__:					str      							=     torch.from_numpy(_lowerCAmelCase).to(device=_lowerCAmelCase)
								a__:					Tuple      							=     torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]])

								a__:					int      							=     torch.from_numpy(_lowerCAmelCase)
								a__:					int      							=     torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)])

								if str(_lowerCAmelCase).startswith('mps'):
										# mps does not support float64
										a__:					Union[str, Any]      							=     timesteps.to(_lowerCAmelCase   ,      dtype=torch.floataa)
								else:
										a__:					List[Any]      							=     timesteps.to(device=_lowerCAmelCase)

								# empty dt and derivative
								a__:					Tuple      							=     None
								a__:					List[str]      							=     None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								a__:					Optional[int]      							=     defaultdict(_lowerCAmelCase)



						def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      Any:





								'''simple docstring'''





								a__:					Optional[Any]      							=     np.log(_lowerCAmelCase)

								# get distribution
								a__:					str      							=     log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								a__:					Union[str, Any]      							=     np.cumsum((dists >= 0)   ,      axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
								a__:					Optional[Any]      							=     low_idx + 1

								a__:					int      							=     log_sigmas[low_idx]
								a__:					int      							=     log_sigmas[high_idx]

								# interpolate sigmas
								a__:					Union[str, Any]      							=     (low - log_sigma) / (low - high)
								a__:					Any      							=     np.clip(_lowerCAmelCase   ,      0   ,      1)

								# transform interpolation to time range
								a__:					List[str]      							=     (1 - w) * low_idx + w * high_idx
								a__:					int      							=     t.reshape(sigma.shape)
								return t



						def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      Dict:





								'''simple docstring'''





								a__:					float      							=     in_sigmas[-1].item()
								a__:					float      							=     in_sigmas[0].item()

								a__:					Optional[int]      							=     7.0  # 7.0 is the value used in the paper
								a__:					int      							=     np.linspace(0   ,      1   ,      _lowerCAmelCase)
								a__:					Dict      							=     sigma_min ** (1 / rho)
								a__:					Any      							=     sigma_max ** (1 / rho)
								a__:					Tuple      							=     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas



						@property
						def   lowerCamelCase_       (						self)     ->      str:





								'''simple docstring'''





								return self.dt is None



						def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase = True   ,      )     ->      List[Any]:





								'''simple docstring'''





								a__:					List[str]      							=     self.index_for_timestep(_lowerCAmelCase)

								# advance index counter by 1
								a__:					Optional[int]      							=     timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
										a__:					Any      							=     self.sigmas[step_index]
										a__:					Tuple      							=     self.sigmas[step_index + 1]
								else:
										# 2nd order / Heun's method
										a__:					Any      							=     self.sigmas[step_index - 1]
										a__:					int      							=     self.sigmas[step_index]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								a__:					List[Any]      							=     0
								a__:					Optional[Any]      							=     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
										a__:					Optional[Any]      							=     sigma_hat if self.state_in_first_order else sigma_next
										a__:					List[str]      							=     sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
										a__:					List[str]      							=     sigma_hat if self.state_in_first_order else sigma_next
										a__:					int      							=     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
										    sample / (sigma_input**2 + 1)
										)
								elif self.config.prediction_type == "sample":
										a__:					Dict      							=     model_output
								else:
										raise ValueError(
										    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`')

								if self.config.clip_sample:
										a__:					List[Any]      							=     pred_original_sample.clamp(
										    -self.config.clip_sample_range   ,      self.config.clip_sample_range)

								if self.state_in_first_order:
										# 2. Convert to an ODE derivative for 1st order
										a__:					Any      							=     (sample - pred_original_sample) / sigma_hat
										# 3. delta timestep
										a__:					Dict      							=     sigma_next - sigma_hat

										# store for 2nd order step
										a__:					int      							=     derivative
										a__:					Optional[int]      							=     dt
										a__:					List[Any]      							=     sample
								else:
										# 2. 2nd order / Heun's method
										a__:					str      							=     (sample - pred_original_sample) / sigma_next
										a__:					str      							=     (self.prev_derivative + derivative) / 2

										# 3. take prev timestep & sample
										a__:					int      							=     self.dt
										a__:					List[Any]      							=     self.sample

										# free dt and derivative
										# Note, this puts the scheduler in "first order mode"
										a__:					int      							=     None
										a__:					Union[str, Any]      							=     None
										a__:					int      							=     None

								a__:					List[str]      							=     sample + derivative * dt

								if not return_dict:
										return (prev_sample,)

								return SchedulerOutput(prev_sample=_lowerCAmelCase)



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





								'''simple docstring'''





								a__:					List[str]      							=     self.sigmas.to(device=original_samples.device   ,      dtype=original_samples.dtype)
								if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase):
										# mps does not support float64
										a__:					Any      							=     self.timesteps.to(original_samples.device   ,      dtype=torch.floataa)
										a__:					int      							=     timesteps.to(original_samples.device   ,      dtype=torch.floataa)
								else:
										a__:					Any      							=     self.timesteps.to(original_samples.device)
										a__:					Tuple      							=     timesteps.to(original_samples.device)

								a__:					List[str]      							=     [self.index_for_timestep(_lowerCAmelCase   ,      _lowerCAmelCase) for t in timesteps]

								a__:					Optional[int]      							=     sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
										a__:					List[Any]      							=     sigma.unsqueeze(-1)

								a__:					Dict      							=     original_samples + noise * sigma
								return noisy_samples



						def __len__(						self)     ->      str:





								'''simple docstring'''





								return self.config.num_train_timesteps



"""simple docstring"""
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        __snake_case	(					unittest.TestCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					str      							=     UNetaDModel(
            block_out_channels=(32, 64)   ,      layers_per_block=2   ,      sample_size=32   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					List[Any]      							=     VQModel(
            block_out_channels=[32, 64]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=3   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					Optional[int]      							=     CLIPTextConfig(
            bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=32   ,      intermediate_size=37   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=10_00   ,      )
        return CLIPTextModel(lowercase)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.dummy_uncond_unet
        a__:					Optional[int]      							=     DDIMScheduler()
        a__:					Optional[int]      							=     self.dummy_vq_model

        a__:					Union[str, Any]      							=     LDMPipeline(unet=lowercase   ,      vqvae=lowercase   ,      scheduler=lowercase)
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					str      							=     torch.manual_seed(0)
        a__:					Dict      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy').images

        a__:					Union[str, Any]      							=     torch.manual_seed(0)
        a__:					int      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy'   ,      return_dict=lowercase)[0]

        a__:					Union[str, Any]      							=     image[0, -3:, -3:, -1]
        a__:					int      							=     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__:					int      							=     np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
        a__:					Optional[Any]      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance







@slow
@require_torch
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					List[str]      							=     torch.manual_seed(0)
        a__:					Optional[int]      							=     ldm(generator=lowercase   ,      num_inference_steps=5   ,      output_type='numpy').images

        a__:					Optional[int]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        a__:					int      							=     np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
        a__:					Any      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance


