"""simple docstring"""


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
 from PIL import Image
else:






 class    a__    :


    @staticmethod
    def 	snake_case__	(     *_UpperCAmelCase,				**_UpperCAmelCase  ):
         '''simple docstring'''



         pass







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							hashlib.mda(image.tobytes()      )
     return m.hexdigest()[:10]







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							np.array(A      )
     lowercase__				 =							npimg.shape
     return {"hash": hashimage(A      ), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class    a__    (   unittest.TestCase	):
   snake_case_							=      dict(
       (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if MODEL_FOR_MASK_GENERATION_MAPPING else [])	)
   snake_case_							=      dict(
       (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()	) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])	)


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							MaskGenerationPipeline(model=_UpperCAmelCase,				image_processor=_UpperCAmelCase  )
        return image_segmenter, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        pass


   @require_tf
   @unittest.skip("Image segmentation not implemented in TF"  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   @slow
   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							pipeline("mask-generation",				model="facebook/sam-vit-huge"  )

        lowercase__				 =							image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg",				points_per_batch=256  )

        # Shortening by hashing
        lowercase__				 =							[]
        for i, o in enumerate(outputs["masks"]  ):
             new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase  ), "scores": outputs["scores"][i]}]

        # fmt: off
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[
                {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_444},
                {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.021},
                {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_167},
                {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_132},
                {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_053},
                {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9_967},
                {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.993},
                {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9_909},
                {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9_879},
                {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9_834},
                {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9_716},
                {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9_612},
                {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9_599},
                {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9_552},
                {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9_532},
                {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9_516},
                {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9_499},
                {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9_483},
                {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9_464},
                {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.943},
                {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.943},
                {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9_408},
                {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9_335},
                {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9_326},
                {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9_262},
                {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8_999},
                {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8_986},
                {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8_984},
                {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8_873},
                {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8_871}
            ],				)
        # fmt: on


   @require_torch
   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"facebook/sam-vit-huge"
        lowercase__				 =							pipeline("mask-generation",				model=_UpperCAmelCase  )

        lowercase__				 =							image_segmenter(
            "http://images.cocodataset.org/val2017/000000039769.jpg",				pred_iou_thresh=1,				points_per_batch=256  )

        # Shortening by hashing
        lowercase__				 =							[]
        for i, o in enumerate(outputs["masks"]  ):
             new_outupt += [{"mask": mask_to_test_readable(_UpperCAmelCase  ), "scores": outputs["scores"][i]}]

        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[
                {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0_444},
                {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_210},
                {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0_167},
                {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0_132},
                {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0_053},
            ],				)





"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    a__    (   _a	):
   snake_case_							=      (IPNDMScheduler,)
   snake_case_							=      (("num_inference_steps", 50),)


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							{"num_train_timesteps": 1000}
        config.update(**_UpperCAmelCase  )
        return config


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )
        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )
        lowercase__				 =							self.dummy_sample
        lowercase__				 =							0.1 * sample
        lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config(**_UpperCAmelCase  )
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )
             scheduler.set_timesteps(_UpperCAmelCase  )
             # copy over dummy past residuals
             lowercase__				 =							dummy_past_residuals[:]

             if time_step is None:
                  lowercase__				 =							scheduler.timesteps[len(scheduler.timesteps  ) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_UpperCAmelCase  )
                  lowercase__				 =							scheduler_class.from_pretrained(_UpperCAmelCase  )
                  new_scheduler.set_timesteps(_UpperCAmelCase  )
                  # copy over dummy past residuals
                  lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )
        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )
        lowercase__				 =							self.dummy_sample
        lowercase__				 =							0.1 * sample
        lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config()
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )
             scheduler.set_timesteps(_UpperCAmelCase  )

             # copy over dummy past residuals (must be after setting timesteps)
             lowercase__				 =							dummy_past_residuals[:]

             if time_step is None:
                  lowercase__				 =							scheduler.timesteps[len(scheduler.timesteps  ) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_UpperCAmelCase  )
                  lowercase__				 =							scheduler_class.from_pretrained(_UpperCAmelCase  )
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(_UpperCAmelCase  )

                  # copy over dummy past residual (must be after setting timesteps)
                  lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							self.scheduler_classes[0]
        lowercase__				 =							self.get_scheduler_config(**_UpperCAmelCase  )
        lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

        lowercase__				 =							10
        lowercase__				 =							self.dummy_model()
        lowercase__				 =							self.dummy_sample_deter
        scheduler.set_timesteps(_UpperCAmelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        for i, t in enumerate(scheduler.timesteps  ):
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        return sample


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )

        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config()
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

             lowercase__				 =							self.dummy_sample
             lowercase__				 =							0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase,				"set_timesteps"  ):
                  scheduler.set_timesteps(_UpperCAmelCase  )
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase,				"set_timesteps"  ):
                  lowercase__				 =							num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.timesteps[5]
             lowercase__				 =							scheduler.timesteps[6]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             self.assertEqual(output_a.shape,				sample.shape  )
             self.assertEqual(output_a.shape,				output_a.shape  )

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

             self.assertEqual(output_a.shape,				sample.shape  )
             self.assertEqual(output_a.shape,				output_a.shape  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for timesteps in [100, 1000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase,				time_step=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for t, num_inference_steps in zip([1, 5, 10],				[10, 50, 100]  ):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase,				time_step=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop()
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_mean.item() - 254_0529  ) < 10




