



"""simple docstring"""


import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput







@dataclass
class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				42
						__UpperCamelCase													=				None
						__UpperCamelCase													=				None







class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[Any]:
												UpperCAmelCase			 =					ModelOutputTest(a=30   )
												self.assertEqual(x.a  ,    30   )
												self.assertIsNone(x.b   )
												self.assertIsNone(x.c   )
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x.d






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Any:
												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    b=10   )
												self.assertEqual(x[0]  ,    30   )
												self.assertEqual(x[1]  ,    10   )
												self.assertEqual(x[:2]  ,    (30, 10)   )
												self.assertEqual(x[:]  ,    (30, 10)   )

												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    c=10   )
												self.assertEqual(x[0]  ,    30   )
												self.assertEqual(x[1]  ,    10   )
												self.assertEqual(x[:2]  ,    (30, 10)   )
												self.assertEqual(x[:]  ,    (30, 10)   )






						def       UpperCAmelCase__	(   self					:Dict   )							->     int:
												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    b=10   )
												self.assertEqual(x['a']  ,    30   )
												self.assertEqual(x['b']  ,    10   )
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x['c']

												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    c=10   )
												self.assertEqual(x['a']  ,    30   )
												self.assertEqual(x['c']  ,    10   )
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x['b']






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Union[str, Any]:
												UpperCAmelCase			 =					ModelOutputTest(a=30   )
												self.assertEqual(list(x.keys()   )  ,    ['a']   )
												self.assertEqual(list(x.values()   )  ,    [30]   )
												self.assertEqual(list(x.items()   )  ,    [('a', 30)]   )
												self.assertEqual(list(lowercase_   )  ,    ['a']   )

												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    b=10   )
												self.assertEqual(list(x.keys()   )  ,    ['a', 'b']   )
												self.assertEqual(list(x.values()   )  ,    [30, 10]   )
												self.assertEqual(list(x.items()   )  ,    [('a', 30), ('b', 10)]   )
												self.assertEqual(list(lowercase_   )  ,    ['a', 'b']   )

												UpperCAmelCase			 =					ModelOutputTest(a=30  ,    c=10   )
												self.assertEqual(list(x.keys()   )  ,    ['a', 'c']   )
												self.assertEqual(list(x.values()   )  ,    [30, 10]   )
												self.assertEqual(list(x.items()   )  ,    [('a', 30), ('c', 10)]   )
												self.assertEqual(list(lowercase_   )  ,    ['a', 'c']   )

												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x.update({'d': 20}   )
												with self.assertRaises(lowercase_   ):
																		del x["a"]
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x.pop('a'   )
												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					x.setdefault('d'  ,    32   )






						def       UpperCAmelCase__	(   self					:Any   )							->     Union[str, Any]:
												UpperCAmelCase			 =					ModelOutputTest(a=30   )
												UpperCAmelCase			 =					10
												self.assertEqual(x.a  ,    10   )
												self.assertEqual(x['a']  ,    10   )






						def       UpperCAmelCase__	(   self					:Any   )							->     int:
												UpperCAmelCase			 =					ModelOutputTest(a=30   )
												UpperCAmelCase			 =					10
												self.assertEqual(x.a  ,    10   )
												self.assertEqual(x['a']  ,    10   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     List[str]:
												UpperCAmelCase			 =					ModelOutputTest({'a': 30, 'b': 10}   )
												self.assertEqual(list(x.keys()   )  ,    ['a', 'b']   )
												self.assertEqual(x.a  ,    30   )
												self.assertEqual(x.b  ,    10   )






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Optional[int]:
												UpperCAmelCase			 =					ModelOutputTest([('a', 30), ('b', 10)]   )
												self.assertEqual(list(x.keys()   )  ,    ['a', 'b']   )
												self.assertEqual(x.a  ,    30   )
												self.assertEqual(x.b  ,    10   )

												with self.assertRaises(lowercase_   ):
																		UpperCAmelCase			 =					ModelOutputTest([('a', 30), (10, 10)]   )

												UpperCAmelCase			 =					ModelOutputTest(a=(30, 30)   )
												self.assertEqual(list(x.keys()   )  ,    ['a']   )
												self.assertEqual(x.a  ,    (30, 30)   )










"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

												# set step values
												self.scheduler.set_timesteps(lowercase_   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																		# 1. predict noise model_output
																		UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_   ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).prev_sample

												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   ).numpy()
												if output_type == "pil":
																		UpperCAmelCase			 =					self.numpy_to_pil(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





