'''simple docstring'''

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

import numpy as np
import torch
from scipy import integrate

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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						42
   __UpperCAmelCase           =						None




def  _lowerCAmelCase				(				__a			,      __a=0.9_99			,      __a="cosine"			,      )  ->      List[Any]:
     '''simple docstring'''







     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  :int       =[]
     for i in range(__a      ):
          _UpperCamelCase  :Tuple       =i / num_diffusion_timesteps
          _UpperCamelCase  :List[Any]       =(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    lowerCamelCase__  (   __snake_case       ,				__snake_case     ):
   __UpperCAmelCase           =						[e.name for e in KarrasDiffusionSchedulers]
   __UpperCAmelCase           =						1



   @register_to_config
   def __init__(    self					,      lowerCAmelCase__ = 1_000					,      lowerCAmelCase__ = 0.0001					,      lowerCAmelCase__ = 0.02					,      lowerCAmelCase__ = "linear"					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = False					,      lowerCAmelCase__ = "epsilon"					,      lowerCAmelCase__ = "linspace"					,      lowerCAmelCase__ = 0					,      )				->     Dict:
        """simple docstring"""





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

        _UpperCamelCase  :List[Any]       =1.0 - self.betas
        _UpperCamelCase  :Tuple       =torch.cumprod(self.alphas					,      dim=0     )

        _UpperCamelCase  :Optional[int]       =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
        _UpperCamelCase  :Tuple       =np.concatenate([sigmas[::-1], [0.0]]     ).astype(np.floataa     )
        _UpperCamelCase  :int       =torch.from_numpy(lowerCAmelCase__     )

        # setable values
        _UpperCamelCase  :int       =None
        _UpperCamelCase  :str       =use_karras_sigmas
        self.set_timesteps(lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =[]
        _UpperCamelCase  :str       =False



   @property
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





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

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



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     torch.FloatTensor:
        """simple docstring"""





        if isinstance(lowerCAmelCase__					,      torch.Tensor     ):
             _UpperCamelCase  :Union[str, Any]       =timestep.to(self.timesteps.device     )
        _UpperCamelCase  :str       =(self.timesteps == timestep).nonzero().item()
        _UpperCamelCase  :List[str]       =self.sigmas[step_index]
        _UpperCamelCase  :str       =sample / ((sigma**2 + 1) ** 0.5)
        _UpperCamelCase  :Tuple       =True
        return sample



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        def lms_derivative(lowerCAmelCase__     ):
             _UpperCamelCase  :Dict       =1.0
             for k in range(lowerCAmelCase__     ):
                  if current_order == k:
                       continue
                  prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
             return prod

        _UpperCamelCase  :Union[str, Any]       =integrate.quad(lowerCAmelCase__					,      self.sigmas[t]					,      self.sigmas[t + 1]					,      epsrel=1e-4     )[0]

        return integrated_coeff



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = None     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :str       =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":
             _UpperCamelCase  :str       =np.linspace(0					,      self.config.num_train_timesteps - 1					,      lowerCAmelCase__					,      dtype=lowerCAmelCase__     )[
                 ::-1
             ].copy()
        elif self.config.timestep_spacing == "leading":
             _UpperCamelCase  :Tuple       =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  :Optional[int]       =(np.arange(0					,      lowerCAmelCase__     ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__     )
             timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
             _UpperCamelCase  :Tuple       =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  :str       =(np.arange(self.config.num_train_timesteps					,      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\'.'''     )

        _UpperCamelCase  :Any       =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
        _UpperCamelCase  :Optional[Any]       =np.log(lowerCAmelCase__     )
        _UpperCamelCase  :Dict       =np.interp(lowerCAmelCase__					,      np.arange(0					,      len(lowerCAmelCase__     )     )					,      lowerCAmelCase__     )

        if self.use_karras_sigmas:
             _UpperCamelCase  :Optional[Any]       =self._convert_to_karras(in_sigmas=lowerCAmelCase__     )
             _UpperCamelCase  :Optional[int]       =np.array([self._sigma_to_t(lowerCAmelCase__					,      lowerCAmelCase__     ) for sigma in sigmas]     )

        _UpperCamelCase  :List[Any]       =np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )

        _UpperCamelCase  :Optional[Any]       =torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )
        if str(lowerCAmelCase__     ).startswith("""mps"""     ):
             # mps does not support float64
             _UpperCamelCase  :str       =torch.from_numpy(lowerCAmelCase__     ).to(lowerCAmelCase__					,      dtype=torch.floataa     )
        else:
             _UpperCamelCase  :str       =torch.from_numpy(lowerCAmelCase__     ).to(device=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =[]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     List[Any]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =np.log(lowerCAmelCase__     )

        # get distribution
        _UpperCamelCase  :Optional[int]       =log_sigma - log_sigmas[:, np.newaxis]

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

        _UpperCamelCase  :str       =log_sigmas[low_idx]
        _UpperCamelCase  :Union[str, Any]       =log_sigmas[high_idx]

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

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



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__     )				->     torch.FloatTensor:
        """simple docstring"""





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

        _UpperCamelCase  :Union[str, Any]       =7.0  # 7.0 is the value used in the paper
        _UpperCamelCase  :str       =np.linspace(0					,      1					,      self.num_inference_steps     )
        _UpperCamelCase  :Union[str, Any]       =sigma_min ** (1 / rho)
        _UpperCamelCase  :List[str]       =sigma_max ** (1 / rho)
        _UpperCamelCase  :List[str]       =(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = 4					,      lowerCAmelCase__ = True					,      )				->     Union[LMSDiscreteSchedulerOutput, Tuple]:
        """simple docstring"""





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

        if isinstance(lowerCAmelCase__					,      torch.Tensor     ):
             _UpperCamelCase  :Tuple       =timestep.to(self.timesteps.device     )
        _UpperCamelCase  :int       =(self.timesteps == timestep).nonzero().item()
        _UpperCamelCase  :int       =self.sigmas[step_index]

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

        # 2. Convert to an ODE derivative
        _UpperCamelCase  :List[str]       =(sample - pred_original_sample) / sigma
        self.derivatives.append(lowerCAmelCase__     )
        if len(self.derivatives     ) > order:
             self.derivatives.pop(0     )

        # 3. Compute linear multistep coefficients
        _UpperCamelCase  :Optional[Any]       =min(step_index + 1					,      lowerCAmelCase__     )
        _UpperCamelCase  :Any       =[self.get_lms_coefficient(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     ) for curr_order in range(lowerCAmelCase__     )]

        # 4. Compute previous sample based on the derivatives path
        _UpperCamelCase  :List[str]       =sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCAmelCase__					,      reversed(self.derivatives     )     )     )

        if not return_dict:
             return (prev_sample,)

        return LMSDiscreteSchedulerOutput(prev_sample=lowerCAmelCase__					,      pred_original_sample=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      )				->     torch.FloatTensor:
        """simple docstring"""





        _UpperCamelCase  :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
             _UpperCamelCase  :Tuple       =self.timesteps.to(original_samples.device					,      dtype=torch.floataa     )
             _UpperCamelCase  :Union[str, Any]       =timesteps.to(original_samples.device					,      dtype=torch.floataa     )
        else:
             _UpperCamelCase  :Optional[Any]       =self.timesteps.to(original_samples.device     )
             _UpperCamelCase  :Any       =timesteps.to(original_samples.device     )

        _UpperCamelCase  :Optional[int]       =[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

        _UpperCamelCase  :List[Any]       =original_samples + noise * sigma
        return noisy_samples




   def __len__(    self     )				->     Optional[int]:
        """simple docstring"""





        return self.config.num_train_timesteps

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    require_torch_gpu,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						Optional[Any]				   =      StableDiffusionParadigmsPipeline
						__A:						Optional[int]				   =      TEXT_TO_IMAGE_PARAMS
						__A:						Dict				   =      TEXT_TO_IMAGE_BATCH_PARAMS
						__A:						Any				   =      TEXT_TO_IMAGE_IMAGE_PARAMS
						__A:						List[Any]				   =      TEXT_TO_IMAGE_IMAGE_PARAMS

						def 						a__ (					self		:			Optional[Any]			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		UNetaDConditionModel(
													    block_out_channels=(32, 64)	,						layers_per_block=2	,						sample_size=32	,						in_channels=4	,						out_channels=4	,						down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,						up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,						cross_attention_dim=32	,						attention_head_dim=(2, 4)	,						use_linear_projection=_lowerCamelCase	,						)
													_UpperCAmelCase  :			Dict	       =		DDIMParallelScheduler(
													    beta_start=0.0_00_85	,						beta_end=0.0_12	,						beta_schedule="scaled_linear"	,						clip_sample=_lowerCamelCase	,						set_alpha_to_one=_lowerCamelCase	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Tuple	       =		AutoencoderKL(
													    block_out_channels=[32, 64]	,						in_channels=3	,						out_channels=3	,						down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,						up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,						latent_channels=4	,						sample_size=1_28	,						)
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Optional[Any]	       =		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	,						hidden_act="gelu"	,						projection_dim=5_12	,						)
													_UpperCAmelCase  :			Tuple	       =		CLIPTextModel(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

													_UpperCAmelCase  :			Optional[int]	       =		{
													    "unet": unet,
													    "scheduler": scheduler,
													    "vae": vae,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "safety_checker": None,
													    "feature_extractor": None,
													}
													return components

						def 						a__ (					self		:			Dict	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			int=0			):
													if str(_lowerCamelCase			).startswith("mps"			):
																				_UpperCAmelCase  :			Tuple	       =		torch.manual_seed(_lowerCamelCase			)
													else:
																				_UpperCAmelCase  :			Optional[int]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 6.0,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			List[str]	       =		self.get_dummy_components()
													_UpperCAmelCase  :			List[Any]	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			List[Any]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			str	       =		np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			Dict	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Dict	       =		self.get_dummy_components()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			str	       =		DDPMParallelScheduler()
													torch.manual_seed(0			)
													_UpperCAmelCase  :			int	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			Any	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Dict	       =		sd_pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			str	       =		image[0, -3:, -3:, -1]
													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_consistent(batch_sizes=[1, 2]			)

						def 						a__ (					self		:			List[str]			):
													super().test_inference_batch_single_identical(batch_size=2	,						expected_max_diff=3E-3			)






						def 						a__ (					self		:			Optional[Any]			):
													_UpperCAmelCase  :			int	       =		"cpu"  # ensure determinism for the device-dependent torch.Generator
													_UpperCAmelCase  :			Tuple	       =		self.get_dummy_components()
													_UpperCAmelCase  :			Dict	       =		StableDiffusionParadigmsPipeline(**_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		sd_pipe.to(_lowerCamelCase			)
													sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Optional[int]	       =		self.get_dummy_inputs(_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		"french fries"
													_UpperCAmelCase  :			Tuple	       =		sd_pipe(**_lowerCamelCase	,						negative_prompt=_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[Any]	       =		output.images
													_UpperCAmelCase  :			Union[str, Any]	       =		image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Optional[Any]	       =		np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]			)

													assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2







@slow
@require_torch_gpu
class    _UpperCamelCase(   unittest.TestCase				):

						def 						a__ (					self		:			int			):
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			Optional[int]=0			):
													_UpperCAmelCase  :			Union[str, Any]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		{
													    "prompt": "a photograph of an astronaut riding a horse",
													    "generator": generator,
													    "num_inference_steps": 10,
													    "guidance_scale": 7.5,
													    "output_type": "numpy",
													    "parallel": 3,
													    "debug": True,
													}
													return inputs






						def 						a__ (					self		:			Any			):
													_UpperCAmelCase  :			str	       =		"stabilityai/stable-diffusion-2-base"
													_UpperCAmelCase  :			List[str]	       =		DDIMParallelScheduler.from_pretrained(_lowerCamelCase	,						subfolder="scheduler"			)
													_UpperCAmelCase  :			Union[str, Any]	       =		StableDiffusionParadigmsPipeline.from_pretrained(_lowerCamelCase	,						scheduler=_lowerCamelCase	,						safety_checker=_lowerCamelCase			)
													pipe.to(_lowerCamelCase			)
													pipe.set_progress_bar_config(disable=_lowerCamelCase			)
													pipe.enable_attention_slicing()

													_UpperCAmelCase  :			Optional[Any]	       =		self.get_inputs()
													_UpperCAmelCase  :			List[str]	       =		pipe(**_lowerCamelCase			).images
													_UpperCAmelCase  :			Tuple	       =		image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)

													_UpperCAmelCase  :			Union[str, Any]	       =		np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]			)

													assert np.abs(expected_slice - image_slice			).max() < 1E-2




