


import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   __lowercase				(  __snake_case ,					unittest.TestCase  ):
      UpperCamelCase								=       DDIMPipeline
      UpperCamelCase								=       UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      UpperCamelCase								=       PipelineTesterMixin.required_optional_params - {
          '''num_images_per_prompt''',
          '''latents''',
          '''callback''',
          '''callback_steps''',
      }
      UpperCamelCase								=       UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
      UpperCamelCase								=       False





      def        _lowercase				(		self						:					Optional[int]       )    ->   List[Any]:






         """simple docstring"""







         torch.manual_seed(0       )
         UpperCAmelCase							=     UNetaDModel(
             block_out_channels=(3_2, 6_4)					,       layers_per_block=2					,       sample_size=3_2					,       in_channels=3					,       out_channels=3					,       down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,       up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,       )
         UpperCAmelCase							=     DDIMScheduler()
         UpperCAmelCase							=     {"""unet""": unet, """scheduler""": scheduler}
         return components





      def        _lowercase				(		self						:					int					,       __lowerCamelCase						:					Dict					,       __lowerCamelCase						:					Optional[Any]=0       )    ->   Optional[Any]:






         """simple docstring"""







         if str(__lowerCamelCase       ).startswith("""mps"""       ):
            UpperCAmelCase							=     torch.manual_seed(__lowerCamelCase       )
         else:
            UpperCAmelCase							=     torch.Generator(device=__lowerCamelCase       ).manual_seed(__lowerCamelCase       )
         UpperCAmelCase							=     {
             """batch_size""": 1,
             """generator""": generator,
             """num_inference_steps""": 2,
             """output_type""": """numpy""",
         }
         return inputs





      def        _lowercase				(		self						:					Tuple       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     """cpu"""

         UpperCAmelCase							=     self.get_dummy_components()
         UpperCAmelCase							=     self.pipeline_class(**__lowerCamelCase       )
         pipe.to(__lowerCamelCase       )
         pipe.set_progress_bar_config(disable=__lowerCamelCase       )

         UpperCAmelCase							=     self.get_dummy_inputs(__lowerCamelCase       )
         UpperCAmelCase							=     pipe(**__lowerCamelCase       ).images
         UpperCAmelCase							=     image[0, -3:, -3:, -1]

         self.assertEqual(image.shape					,       (1, 3_2, 3_2, 3)       )
         UpperCAmelCase							=     np.array(
             [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4]       )
         UpperCAmelCase							=     np.abs(image_slice.flatten() - expected_slice       ).max()
         self.assertLessEqual(__lowerCamelCase					,       1e-3       )





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






         """simple docstring"""







         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )





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






         """simple docstring"""







         super().test_save_load_local(expected_max_difference=3e-3       )





      def        _lowercase				(		self						:					Tuple       )    ->   Dict:






         """simple docstring"""







         super().test_save_load_optional_components(expected_max_difference=3e-3       )





      def        _lowercase				(		self						:					Dict       )    ->   Optional[Any]:






         """simple docstring"""







         super().test_inference_batch_single_identical(expected_max_diff=3e-3       )






@slow
@require_torch_gpu
class   __lowercase				(  unittest.TestCase  ):





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






         """simple docstring"""







         UpperCAmelCase							=     """google/ddpm-cifar10-32"""

         UpperCAmelCase							=     UNetaDModel.from_pretrained(__lowerCamelCase       )
         UpperCAmelCase							=     DDIMScheduler()

         UpperCAmelCase							=     DDIMPipeline(unet=__lowerCamelCase					,       scheduler=__lowerCamelCase       )
         ddim.to(__lowerCamelCase       )
         ddim.set_progress_bar_config(disable=__lowerCamelCase       )

         UpperCAmelCase							=     torch.manual_seed(0       )
         UpperCAmelCase							=     ddim(generator=__lowerCamelCase					,       eta=0.0					,       output_type="""numpy"""       ).images

         UpperCAmelCase							=     image[0, -3:, -3:, -1]

         assert image.shape == (1, 3_2, 3_2, 3)
         UpperCAmelCase							=     np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     """google/ddpm-ema-bedroom-256"""

         UpperCAmelCase							=     UNetaDModel.from_pretrained(__lowerCamelCase       )
         UpperCAmelCase							=     DDIMScheduler.from_pretrained(__lowerCamelCase       )

         UpperCAmelCase							=     DDIMPipeline(unet=__lowerCamelCase					,       scheduler=__lowerCamelCase       )
         ddpm.to(__lowerCamelCase       )
         ddpm.set_progress_bar_config(disable=__lowerCamelCase       )

         UpperCAmelCase							=     torch.manual_seed(0       )
         UpperCAmelCase							=     ddpm(generator=__lowerCamelCase					,       output_type="""numpy"""       ).images

         UpperCAmelCase							=     image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_5_6, 2_5_6, 3)
         UpperCAmelCase							=     np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2








__a     =			[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0
   while place < len(lowerCAmelCase_   ):
      if (place + 1 < len(lowerCAmelCase_   )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   UpperCAmelCase							=     []
   for arabic, roman in ROMAN:
      ((UpperCAmelCase)				,  (UpperCAmelCase))							=     divmod(lowerCAmelCase_  ,						lowerCAmelCase_   )
      result.append(roman * factor   )
      if number == 0:
         break
   return "".join(lowerCAmelCase_   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



