import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__SCREAMING_SNAKE_CASE						:    Any     = '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'



class    lowercase_    (     unittest.TestCase						, __snake_case		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     Union[str, Any]				=    load_tool("summarization"				)
													self.tool.setup()
													_snake_case						:     Optional[int]				=    load_tool("summarization"      ,				remote=lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.tool(lowercase_				)
													self.assertEqual(
													    lowercase_      ,				"Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."      ,				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Union[str, Any]				=    self.remote_tool(lowercase_				)
													self.assertEqual(
													    lowercase_      ,				"Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."      ,				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Tuple				=    self.tool(text=lowercase_				)
													self.assertEqual(
													    lowercase_      ,				"Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."      ,				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.remote_tool(text=lowercase_				)
													self.assertEqual(
													    lowercase_      ,				"Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."      ,				)





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

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


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

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

         for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              lowerCAmelCase__		= self.unet(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).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
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).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(SCREAMING_SNAKE_CASE__   )

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

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