


import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput


@dataclass
class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       42
  _SCREAMING_SNAKE_CASE		    =       None
  _SCREAMING_SNAKE_CASE		    =       None






class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       str  )   -> int:




     """simple docstring"""
     snake_case_					    =					ModelOutputTest(a=3_0  )
     self.assertEqual(x.a    ,				3_0  )
     self.assertIsNone(x.b  )
     self.assertIsNone(x.c  )
     with self.assertRaises(_lowerCAmelCase  ):
        snake_case_					    =					x.d



  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> Tuple:




     """simple docstring"""
     snake_case_					    =					ModelOutputTest(a=3_0    ,				b=1_0  )
     self.assertEqual(x[0]    ,				3_0  )
     self.assertEqual(x[1]    ,				1_0  )
     self.assertEqual(x[:2]    ,				(3_0, 1_0)  )
     self.assertEqual(x[:]    ,				(3_0, 1_0)  )

     snake_case_					    =					ModelOutputTest(a=3_0    ,				c=1_0  )
     self.assertEqual(x[0]    ,				3_0  )
     self.assertEqual(x[1]    ,				1_0  )
     self.assertEqual(x[:2]    ,				(3_0, 1_0)  )
     self.assertEqual(x[:]    ,				(3_0, 1_0)  )



  def 					lowerCAmelCase__  (     self       :       Dict  )   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					ModelOutputTest(a=3_0    ,				b=1_0  )
     self.assertEqual(x["a"]    ,				3_0  )
     self.assertEqual(x["b"]    ,				1_0  )
     with self.assertRaises(_lowerCAmelCase  ):
        snake_case_					    =					x["c"]

     snake_case_					    =					ModelOutputTest(a=3_0    ,				c=1_0  )
     self.assertEqual(x["a"]    ,				3_0  )
     self.assertEqual(x["c"]    ,				1_0  )
     with self.assertRaises(_lowerCAmelCase  ):
        snake_case_					    =					x["b"]



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




     """simple docstring"""
     snake_case_					    =					ModelOutputTest(a=3_0  )
     self.assertEqual(list(x.keys()  )    ,				["a"]  )
     self.assertEqual(list(x.values()  )    ,				[3_0]  )
     self.assertEqual(list(x.items()  )    ,				[("a", 3_0)]  )
     self.assertEqual(list(_lowerCAmelCase  )    ,				["a"]  )

     snake_case_					    =					ModelOutputTest(a=3_0    ,				b=1_0  )
     self.assertEqual(list(x.keys()  )    ,				["a", "b"]  )
     self.assertEqual(list(x.values()  )    ,				[3_0, 1_0]  )
     self.assertEqual(list(x.items()  )    ,				[("a", 3_0), ("b", 1_0)]  )
     self.assertEqual(list(_lowerCAmelCase  )    ,				["a", "b"]  )

     snake_case_					    =					ModelOutputTest(a=3_0    ,				c=1_0  )
     self.assertEqual(list(x.keys()  )    ,				["a", "c"]  )
     self.assertEqual(list(x.values()  )    ,				[3_0, 1_0]  )
     self.assertEqual(list(x.items()  )    ,				[("a", 3_0), ("c", 1_0)]  )
     self.assertEqual(list(_lowerCAmelCase  )    ,				["a", "c"]  )

     with self.assertRaises(_lowerCAmelCase  ):
        snake_case_					    =					x.update({"d": 2_0}  )
     with self.assertRaises(_lowerCAmelCase  ):
        del x["a"]
     with self.assertRaises(_lowerCAmelCase  ):
        snake_case_					    =					x.pop("a"  )
     with self.assertRaises(_lowerCAmelCase  ):
        snake_case_					    =					x.setdefault("d"    ,				3_2  )



  def 					lowerCAmelCase__  (     self       :       Dict  )   -> List[str]:




     """simple docstring"""
     snake_case_					    =					ModelOutputTest(a=3_0  )
     snake_case_					    =					1_0
     self.assertEqual(x.a    ,				1_0  )
     self.assertEqual(x["a"]    ,				1_0  )



  def 					lowerCAmelCase__  (     self       :       str  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					ModelOutputTest(a=3_0  )
     snake_case_					    =					1_0
     self.assertEqual(x.a    ,				1_0  )
     self.assertEqual(x["a"]    ,				1_0  )



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




     """simple docstring"""
     snake_case_					    =					ModelOutputTest({"a": 3_0, "b": 1_0}  )
     self.assertEqual(list(x.keys()  )    ,				["a", "b"]  )
     self.assertEqual(x.a    ,				3_0  )
     self.assertEqual(x.b    ,				1_0  )



  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> List[str]:




     """simple docstring"""
     snake_case_					    =					ModelOutputTest([("a", 3_0), ("b", 1_0)]  )
     self.assertEqual(list(x.keys()  )    ,				["a", "b"]  )
     self.assertEqual(x.a    ,				3_0  )
     self.assertEqual(x.b    ,				1_0  )

     with self.assertRaises(_lowerCAmelCase  ):
        snake_case_					    =					ModelOutputTest([("a", 3_0), (1_0, 1_0)]  )

     snake_case_					    =					ModelOutputTest(a=(3_0, 3_0)  )
     self.assertEqual(list(x.keys()  )    ,				["a"]  )
     self.assertEqual(x.a    ,				(3_0, 3_0)  )








from typing import List, Optional, Tuple, Union

import torch

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


class     __lowerCAmelCase     ( a					):






  """simple docstring"""





  def __init__(     self       :       List[str]    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Tuple  )   -> str:




     """simple docstring"""
     super().__init__()
     self.register_modules(unet=_lowerCAmelCase    ,				scheduler=_lowerCAmelCase  )



  @torch.no_grad()
  def __call__(     self       :       Tuple    ,				_lowerCAmelCase       :       int = 1    ,				_lowerCAmelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				_lowerCAmelCase       :       int = 1_0_0_0    ,				_lowerCAmelCase       :       Optional[str] = "pil"    ,				_lowerCAmelCase       :       bool = True    ,				)   -> Union[ImagePipelineOutput, Tuple]:




     """simple docstring"""
     # Sample gaussian noise to begin loop
     if isinstance(self.unet.config.sample_size    ,				_lowerCAmelCase  ):
        snake_case_					    =					(
            batch_size,
            self.unet.config.in_channels,
            self.unet.config.sample_size,
            self.unet.config.sample_size,
        )
     else:
        snake_case_					    =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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

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

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_lowerCAmelCase  )




