




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      UpperCAmelCase						(			unittest.TestCase   ,					A   ):






   def  _a						(	self   :       str     )->			Optional[Any]:
     _lowercase: str						     =    load_tool('image-captioning'     )
     self.tool.setup()
     _lowercase: Dict						     =    load_tool('image-captioning'						,					remote=UpperCamelCase_     )






   def  _a						(	self   :       Any     )->			Dict:
     _lowercase: str						     =    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'     )     ) / '000000039769.png'     )
     _lowercase: List[str]						     =    self.tool(UpperCamelCase_     )
     self.assertEqual(UpperCamelCase_						,					'two cats sleeping on a couch'     )






   def  _a						(	self   :       Optional[Any]     )->			int:
     _lowercase: Union[str, Any]						     =    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'     )     ) / '000000039769.png'     )
     _lowercase: int						     =    self.remote_tool(UpperCamelCase_     )
     self.assertEqual(UpperCamelCase_						,					'two cats sleeping on a couch'     )






   def  _a						(	self   :       Any     )->			Union[str, Any]:
     _lowercase: str						     =    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'     )     ) / '000000039769.png'     )
     _lowercase: List[Any]						     =    self.tool(image=UpperCamelCase_     )
     self.assertEqual(UpperCamelCase_						,					'two cats sleeping on a couch'     )






   def  _a						(	self   :       Any     )->			Dict:
     _lowercase: Optional[int]						     =    Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'     )     ) / '000000039769.png'     )
     _lowercase: Optional[int]						     =    self.remote_tool(image=UpperCamelCase_     )
     self.assertEqual(UpperCamelCase_						,					'two cats sleeping on a couch'     )






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

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

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

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
