






import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowerCamelCase__							=     logging.get_logger(__name__)

enable_full_determinism()




class 			snake_case__   (							lowercase__	,    lowercase__	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Union[str, Any] 				= UNetaDModel
       lowerCamelCase							:				str 				= 'sample'



       @property
       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						4
            __snake_case			:Optional[int]          =						3
            __snake_case			:Dict          =						(32, 32)

            __snake_case			:Optional[Any]          =						floats_tensor((batch_size, num_channels) + sizes    ).to(a__    )
            __snake_case			:Tuple          =						torch.tensor([10]    ).to(a__    )

            return {"sample": noise, "timestep": time_step}



       @property
       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return (3, 32, 32)



       @property
       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            return (3, 32, 32)



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:List[str]          =						{
                """block_out_channels""": (32, 64),
                """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
                """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
                """attention_head_dim""": 3,
                """out_channels""": 3,
                """in_channels""": 3,
                """layers_per_block""": 2,
                """sample_size""": 32,
            }
            __snake_case			:Tuple          =						self.dummy_input
            return init_dict, inputs_dict




class 			snake_case__   (							lowercase__	,    lowercase__	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				str 				= UNetaDModel
       lowerCamelCase							:				Tuple 				= 'sample'



       @property
       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Any          =						4
            __snake_case			:Optional[int]          =						4
            __snake_case			:Union[str, Any]          =						(32, 32)

            __snake_case			:List[Any]          =						floats_tensor((batch_size, num_channels) + sizes    ).to(a__    )
            __snake_case			:Any          =						torch.tensor([10]    ).to(a__    )

            return {"sample": noise, "timestep": time_step}



       @property
       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            return (4, 32, 32)



       @property
       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return (4, 32, 32)



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Dict          =						{
                """sample_size""": 32,
                """in_channels""": 4,
                """out_channels""": 4,
                """layers_per_block""": 2,
                """block_out_channels""": (32, 64),
                """attention_head_dim""": 32,
                """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
                """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
            }
            __snake_case			:str          =						self.dummy_input
            return init_dict, inputs_dict



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






            '''simple docstring'''





            __snake_case       , __snake_case			:Optional[Any]          =						UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""			,						output_loading_info=a__    )

            self.assertIsNotNone(a__    )
            self.assertEqual(len(loading_info["""missing_keys"""]    )			,						0    )

            model.to(a__    )
            __snake_case			:Dict          =						model(**self.dummy_input    ).sample

            assert image is not None, "Make sure output is not None"



       @unittest.skipIf(torch_device != """cuda"""			,						"""This test is supposed to run on GPU"""    )
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case       , __snake_case			:Tuple          =						UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""			,						output_loading_info=a__    )
            model.to(a__    )
            __snake_case			:List[str]          =						model(**self.dummy_input    ).sample

            assert image is not None, "Make sure output is not None"



       @unittest.skipIf(torch_device != """cuda"""			,						"""This test is supposed to run on GPU"""    )
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case       , __snake_case			:int          =						UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""			,						output_loading_info=a__    )
            model_accelerate.to(a__    )
            model_accelerate.eval()

            __snake_case			:Tuple          =						torch.randn(
                1			,						model_accelerate.config.in_channels			,						model_accelerate.config.sample_size			,						model_accelerate.config.sample_size			,						generator=torch.manual_seed(0    )			,						)
            __snake_case			:Optional[int]          =						noise.to(a__    )
            __snake_case			:Dict          =						torch.tensor([10] * noise.shape[0]    ).to(a__    )

            __snake_case			:str          =						model_accelerate(a__			,						a__    )["""sample"""]

            # two models don't need to stay in the device at the same time
            del model_accelerate
            torch.cuda.empty_cache()
            gc.collect()

            __snake_case       , __snake_case			:Optional[int]          =						UNetaDModel.from_pretrained(
                """fusing/unet-ldm-dummy-update"""			,						output_loading_info=a__			,						low_cpu_mem_usage=a__    )
            model_normal_load.to(a__    )
            model_normal_load.eval()
            __snake_case			:List[Any]          =						model_normal_load(a__			,						a__    )["""sample"""]

            assert torch_all_close(a__			,						a__			,						rtol=1e-3    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""    )
            model.eval()
            model.to(a__    )

            __snake_case			:str          =						torch.randn(
                1			,						model.config.in_channels			,						model.config.sample_size			,						model.config.sample_size			,						generator=torch.manual_seed(0    )			,						)
            __snake_case			:Any          =						noise.to(a__    )
            __snake_case			:Tuple          =						torch.tensor([10] * noise.shape[0]    ).to(a__    )

            with torch.no_grad():
                 __snake_case			:Optional[int]          =						model(a__			,						a__    ).sample

            __snake_case			:Optional[Any]          =						output[0, -1, -3:, -3:].flatten().cpu()
            # fmt: off
            __snake_case			:Dict          =						torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]    )
            # fmt: on

            self.assertTrue(torch_all_close(a__			,						a__			,						rtol=1e-3    )    )




class 			snake_case__   (							lowercase__	,    lowercase__	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= UNetaDModel
       lowerCamelCase							:				str 				= 'sample'



       @property
       def 					__lowercase						(							self			,						a__=(32, 32)    ) ->      str:






            '''simple docstring'''





            __snake_case			:List[str]          =						4
            __snake_case			:Tuple          =						3

            __snake_case			:str          =						floats_tensor((batch_size, num_channels) + sizes    ).to(a__    )
            __snake_case			:Optional[Any]          =						torch.tensor(batch_size * [10]    ).to(dtype=torch.intaa			,						device=a__    )

            return {"sample": noise, "timestep": time_step}



       @property
       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            return (3, 32, 32)



       @property
       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            return (3, 32, 32)



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:int          =						{
                """block_out_channels""": [32, 64, 64, 64],
                """in_channels""": 3,
                """layers_per_block""": 1,
                """out_channels""": 3,
                """time_embedding_type""": """fourier""",
                """norm_eps""": 1e-6,
                """mid_block_scale_factor""": math.sqrt(2.0    ),
                """norm_num_groups""": None,
                """down_block_types""": [
                    """SkipDownBlock2D""",
                    """AttnSkipDownBlock2D""",
                    """SkipDownBlock2D""",
                    """SkipDownBlock2D""",
                ],
                """up_block_types""": [
                    """SkipUpBlock2D""",
                    """SkipUpBlock2D""",
                    """AttnSkipUpBlock2D""",
                    """SkipUpBlock2D""",
                ],
            }
            __snake_case			:Tuple          =						self.dummy_input
            return init_dict, inputs_dict



       @slow
       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case       , __snake_case			:Any          =						UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""			,						output_loading_info=a__    )
            self.assertIsNotNone(a__    )
            self.assertEqual(len(loading_info["""missing_keys"""]    )			,						0    )

            model.to(a__    )
            __snake_case			:int          =						self.dummy_input
            __snake_case			:Dict          =						floats_tensor((4, 3) + (2_56, 2_56)    ).to(a__    )
            __snake_case			:str          =						noise
            __snake_case			:str          =						model(**a__    )

            assert image is not None, "Make sure output is not None"



       @slow
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""    )
            model.to(a__    )

            __snake_case			:List[str]          =						4
            __snake_case			:List[Any]          =						3
            __snake_case			:Union[str, Any]          =						(2_56, 2_56)

            __snake_case			:Tuple          =						torch.ones((batch_size, num_channels) + sizes    ).to(a__    )
            __snake_case			:Tuple          =						torch.tensor(batch_size * [1e-4]    ).to(a__    )

            with torch.no_grad():
                 __snake_case			:Tuple          =						model(a__			,						a__    ).sample

            __snake_case			:Any          =						output[0, -3:, -3:, -1].flatten().cpu()
            # fmt: off
            __snake_case			:Tuple          =						torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]    )
            # fmt: on

            self.assertTrue(torch_all_close(a__			,						a__			,						rtol=1e-2    )    )



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






            '''simple docstring'''





            __snake_case			:Dict          =						UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""    )
            model.to(a__    )

            __snake_case			:Union[str, Any]          =						4
            __snake_case			:Optional[Any]          =						3
            __snake_case			:Optional[int]          =						(32, 32)

            __snake_case			:List[Any]          =						torch.ones((batch_size, num_channels) + sizes    ).to(a__    )
            __snake_case			:str          =						torch.tensor(batch_size * [1e-4]    ).to(a__    )

            with torch.no_grad():
                 __snake_case			:str          =						model(a__			,						a__    ).sample

            __snake_case			:List[Any]          =						output[0, -3:, -3:, -1].flatten().cpu()
            # fmt: off
            __snake_case			:Dict          =						torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]    )
            # fmt: on

            self.assertTrue(torch_all_close(a__			,						a__			,						rtol=1e-2    )    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            pass







from typing import TYPE_CHECKING

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


lowerCamelCase__							=     {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     ["""DPTFeatureExtractor"""]
     lowerCamelCase__							=     ["""DPTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """DPTForDepthEstimation""",
         """DPTForSemanticSegmentation""",
         """DPTModel""",
         """DPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_dpt import DPTFeatureExtractor
          from .image_processing_dpt import DPTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpt import (
              DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPTForDepthEstimation,
              DPTForSemanticSegmentation,
              DPTModel,
              DPTPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase__							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


