





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    __SCREAMING_SNAKE_CASE			( UpperCamelCase_     ,unittest.TestCase					):
       _UpperCAmelCase      :  Tuple     =    DDIMPipeline
       _UpperCAmelCase      :  Union[str, Any]     =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       _UpperCAmelCase      :  Union[str, Any]     =    PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       _UpperCAmelCase      :  Tuple     =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       _UpperCAmelCase      :  Dict     =    False







       def 						__lowerCamelCase    (      self     :	Optional[int]			)   ->Any:
          torch.manual_seed(0			)
          lowerCamelCase__   :						Dict					   =							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''')		,					)
          lowerCamelCase__   :						Tuple					   =							DDIMScheduler()
          lowerCamelCase__   :						str					   =							{'''unet''': unet, '''scheduler''': scheduler}
          return components







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





          if str(UpperCamelCase__			).startswith('''mps'''			):
             lowerCamelCase__   :						List[str]					   =							torch.manual_seed(UpperCamelCase__			)
          else:
             lowerCamelCase__   :						int					   =							torch.Generator(device=UpperCamelCase__			).manual_seed(UpperCamelCase__			)
          lowerCamelCase__   :						Union[str, Any]					   =							{
              '''batch_size''': 1,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''output_type''': '''numpy''',
          }
          return inputs







       def 						__lowerCamelCase    (      self     :	str			)   ->Optional[Any]:
          lowerCamelCase__   :						Dict					   =							'''cpu'''

          lowerCamelCase__   :						Optional[Any]					   =							self.get_dummy_components()
          lowerCamelCase__   :						int					   =							self.pipeline_class(**UpperCamelCase__			)
          pipe.to(UpperCamelCase__			)
          pipe.set_progress_bar_config(disable=UpperCamelCase__			)

          lowerCamelCase__   :						Union[str, Any]					   =							self.get_dummy_inputs(UpperCamelCase__			)
          lowerCamelCase__   :						Any					   =							pipe(**UpperCamelCase__			).images
          lowerCamelCase__   :						Optional[int]					   =							image[0, -3:, -3:, -1]

          self.assertEqual(image.shape		,					(1, 3_2, 3_2, 3)			)
          lowerCamelCase__   :						Dict					   =							np.array(
              [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]			)
          lowerCamelCase__   :						Optional[Any]					   =							np.abs(image_slice.flatten() - expected_slice			).max()
          self.assertLessEqual(UpperCamelCase__		,					1e-3			)







       def 						__lowerCamelCase    (      self     :	Tuple			)   ->Union[str, Any]:
          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3			)







       def 						__lowerCamelCase    (      self     :	Union[str, Any]			)   ->List[str]:
          super().test_save_load_local(expected_max_difference=3e-3			)







       def 						__lowerCamelCase    (      self     :	Tuple			)   ->Union[str, Any]:
          super().test_save_load_optional_components(expected_max_difference=3e-3			)







       def 						__lowerCamelCase    (      self     :	str			)   ->Optional[Any]:
          super().test_inference_batch_single_identical(expected_max_diff=3e-3			)


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







       def 						__lowerCamelCase    (      self     :	Any			)   ->Optional[int]:
          lowerCamelCase__   :						Optional[Any]					   =							'''google/ddpm-cifar10-32'''

          lowerCamelCase__   :						Union[str, Any]					   =							UNetaDModel.from_pretrained(UpperCamelCase__			)
          lowerCamelCase__   :						Union[str, Any]					   =							DDIMScheduler()

          lowerCamelCase__   :						str					   =							DDIMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__			)
          ddim.to(UpperCamelCase__			)
          ddim.set_progress_bar_config(disable=UpperCamelCase__			)

          lowerCamelCase__   :						Any					   =							torch.manual_seed(0			)
          lowerCamelCase__   :						Any					   =							ddim(generator=UpperCamelCase__		,					eta=0.0		,					output_type='''numpy'''			).images

          lowerCamelCase__   :						Tuple					   =							image[0, -3:, -3:, -1]

          assert image.shape == (1, 3_2, 3_2, 3)
          lowerCamelCase__   :						int					   =							np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]			)

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







       def 						__lowerCamelCase    (      self     :	Optional[int]			)   ->List[Any]:
          lowerCamelCase__   :						int					   =							'''google/ddpm-ema-bedroom-256'''

          lowerCamelCase__   :						List[str]					   =							UNetaDModel.from_pretrained(UpperCamelCase__			)
          lowerCamelCase__   :						Union[str, Any]					   =							DDIMScheduler.from_pretrained(UpperCamelCase__			)

          lowerCamelCase__   :						Union[str, Any]					   =							DDIMPipeline(unet=UpperCamelCase__		,					scheduler=UpperCamelCase__			)
          ddpm.to(UpperCamelCase__			)
          ddpm.set_progress_bar_config(disable=UpperCamelCase__			)

          lowerCamelCase__   :						int					   =							torch.manual_seed(0			)
          lowerCamelCase__   :						Optional[Any]					   =							ddpm(generator=UpperCamelCase__		,					output_type='''numpy'''			).images

          lowerCamelCase__   :						Any					   =							image[0, -3:, -3:, -1]

          assert image.shape == (1, 2_5_6, 2_5_6, 3)
          lowerCamelCase__   :						int					   =							np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]			)

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       int									=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       int									=     ['YolosFeatureExtractor']
					__UpperCamelCase		:       Union[str, Any]									=     ['YolosImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'YolosForObjectDetection',
					    'YolosModel',
					    'YolosPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_yolos import YolosFeatureExtractor
										from .image_processing_yolos import YolosImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_yolos import (
										    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    YolosForObjectDetection,
										    YolosModel,
										    YolosPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





