





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						42
			lowerCAmelCase_							     =						42





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				super().__init__()
				self.register_modules(unet=__lowerCAmelCase       ,  scheduler=__lowerCAmelCase							)





			@torch.no_grad()
			def __call__(    self       ,  __lowerCAmelCase = 1       ,  __lowerCAmelCase = 5_0       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = "pil"       ,  __lowerCAmelCase = True       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		self.unet.config.sample_size
				lowerCamelCase__								=		(batch_size, 3, img_size, img_size)

				lowerCamelCase__								=		self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				lowerCamelCase__								=		randn_tensor(__lowerCAmelCase       ,  generator=__lowerCAmelCase       ,  device=self.device							) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(__lowerCAmelCase							)

				for t in self.progress_bar(self.scheduler.timesteps							):
					# here sigma_t == t_i from the paper
					lowerCamelCase__								=		self.scheduler.schedule[t]
					lowerCamelCase__								=		self.scheduler.schedule[t - 1] if t > 0 else 0

					# 1. Select temporarily increased noise level sigma_hat
					# 2. Add new noise to move from sample_i to sample_hat
					lowerCamelCase__				,	lowerCamelCase__								=		self.scheduler.add_noise_to_input(__lowerCAmelCase       ,  __lowerCAmelCase       ,  generator=__lowerCAmelCase							)

					# 3. Predict the noise residual given the noise magnitude `sigma_hat`
					# The model inputs and output are adjusted by following eq. (213) in [1].
					lowerCamelCase__								=		(sigma_hat / 2) * model((sample_hat + 1) / 2       ,  sigma_hat / 2							).sample

					# 4. Evaluate dx/dt at sigma_hat
					# 5. Take Euler step from sigma to sigma_prev
					lowerCamelCase__								=		self.scheduler.step(__lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							)

					if sigma_prev != 0:
						# 6. Apply 2nd order correction
						# The model inputs and output are adjusted by following eq. (213) in [1].
						lowerCamelCase__								=		(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,  sigma_prev / 2							).sample
						lowerCamelCase__								=		self.scheduler.step_correct(
						    __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  step_output.prev_sample       ,  step_output['''derivative''']       ,  )
					lowerCamelCase__								=		step_output.prev_sample

				lowerCamelCase__								=		(sample / 2 + 0.5).clamp(0       ,  1							)
				lowerCamelCase__								=		sample.cpu().permute(0       ,  2       ,  3       ,  1							).numpy()
				if output_type == "pil":
					lowerCamelCase__								=		self.numpy_to_pil(__lowerCAmelCase							)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=__lowerCAmelCase							)










import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)


class   A			(unittest.TestCase    ):
			'''simple docstring'''




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







					"""simple docstring"""






					A__        =   ["""a""", """b""", """c"""]

					# Defaults to last layer if both are None
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[2]   )

					# Out indices set to match out features
					A__      ,			A__        =   get_aligned_output_features_output_indices(["""a""", """c"""]							,	__lowerCAmelCase							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[0, 2]   )

					# Out features set to match out indices
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	[0, 2]							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[0, 2]   )

					# Out features selected from negative indices
					A__      ,			A__        =   get_aligned_output_features_output_indices(__lowerCAmelCase							,	[-3, -1]							,	__lowerCAmelCase   )
					self.assertEqual(__lowerCAmelCase							,	["""a""", """c"""]   )
					self.assertEqual(__lowerCAmelCase							,	[-3, -1]   )






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







					"""simple docstring"""






					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 1)							,	__lowerCAmelCase   )

					# Out features must be a list
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(("""a""", """b""")							,	(0, 1)							,	["""a""", """b"""]   )

					# Out features must be a subset of stage names
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 1)							,	["""a"""]   )

					# Out indices must be a list or tuple
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(__lowerCAmelCase							,	0							,	["""a""", """b"""]   )

					# Out indices must be a subset of stage names
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(__lowerCAmelCase							,	(0, 1)							,	["""a"""]   )

					# Out features and out indices must be the same length
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0,)							,	["""a""", """b""", """c"""]   )

					# Out features should match out indices
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""a""", """b"""]							,	(0, 2)							,	["""a""", """b""", """c"""]   )

					# Out features and out indices should be in order
					with self.assertRaises(__lowerCAmelCase   ):
							verify_out_features_out_indices(["""b""", """a"""]							,	(0, 1)							,	["""a""", """b"""]   )

					# Check passes with valid inputs
					verify_out_features_out_indices(["""a""", """b""", """d"""]							,	(0, 1, -1)							,	["""a""", """b""", """c""", """d"""]   )







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







					"""simple docstring"""






					A__        =   BackboneMixin()

					A__        =   ["""a""", """b""", """c"""]
					A__        =   ["""a""", """c"""]
					A__        =   [0, 2]

					# Check that the output features and indices are set correctly
					self.assertEqual(backbone.out_features							,	["""a""", """c"""]   )
					self.assertEqual(backbone.out_indices							,	[0, 2]   )

					# Check out features and indices are updated correctly
					A__        =   ["""a""", """b"""]
					self.assertEqual(backbone.out_features							,	["""a""", """b"""]   )
					self.assertEqual(backbone.out_indices							,	[0, 1]   )

					A__        =   [-3, -1]
					self.assertEqual(backbone.out_features							,	["""a""", """c"""]   )
					self.assertEqual(backbone.out_indices							,	[-3, -1]   )



