


'''simple docstring'''






import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
 from PIL import Image






class 					__magic_name__							(		unittest.TestCase					,_lowerCAmelCase				):






       def 			lowerCAmelCase		(			self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase   :   int							    =load_tool('image-question-answering')
          self.tool.setup()
          _UpperCAmelCase   :   int							    =load_tool('image-question-answering'  ,							remote=A__)






       def 			lowerCAmelCase		(			self)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase   :   Union[str, Any]							    =Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png')
          _UpperCAmelCase   :   Union[str, Any]							    =self.tool(A__  ,							'How many cats are sleeping on the couch?')
          self.assertEqual(A__  ,							'2')






       def 			lowerCAmelCase		(			self)						->  str:




          '''simple docstring'''
          _UpperCAmelCase   :   Any							    =Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png')
          _UpperCAmelCase   :   List[Any]							    =self.remote_tool(A__  ,							'How many cats are sleeping on the couch?')
          self.assertEqual(A__  ,							'2')






       def 			lowerCAmelCase		(			self)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase   :   Optional[Any]							    =Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png')
          _UpperCAmelCase   :   int							    =self.tool(image=A__  ,							question='How many cats are sleeping on the couch?')
          self.assertEqual(A__  ,							'2')






       def 			lowerCAmelCase		(			self)						->  List[Any]:




          '''simple docstring'''
          _UpperCAmelCase   :   List[Any]							    =Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png')
          _UpperCAmelCase   :   Optional[Any]							    =self.remote_tool(image=A__  ,							question='How many cats are sleeping on the couch?')
          self.assertEqual(A__  ,							'2')







from typing import List, Optional, Tuple, Union

import torch

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






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)