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


lowercase_     :   str 				=				logging.get_logger(__name__)

enable_full_determinism()






class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      UNetaDModel
    __a  =      "sample"
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Dict=      4
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      3
     SCREAMING_SNAKE_CASE__:   Any=      (32, 32)

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.tensor([10]   ).to(lowerCAmelCase   )

     return {"sample": noise, "timestep": time_step}
    @property
    def 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     return (3, 32, 32)
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     return (3, 32, 32)



    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_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,
     }
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.dummy_input
     return init_dict, inputs_dict






class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      UNetaDModel
    __a  =      "sample"
    @property
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Tuple=      4
     SCREAMING_SNAKE_CASE__:   Tuple=      4
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      (32, 32)

     SCREAMING_SNAKE_CASE__:   Tuple=      floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.tensor([10]   ).to(lowerCAmelCase   )

     return {"sample": noise, "timestep": time_step}
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     return (4, 32, 32)
    @property
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     return (4, 32, 32)
    def 						UpperCamelCase_				(  self   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      {
         '''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'''),
     }
     SCREAMING_SNAKE_CASE__:   int=      self.dummy_input
     return init_dict, inputs_dict
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=lowerCAmelCase   )

     self.assertIsNotNone(lowerCAmelCase   )
     self.assertEqual(len(loading_info['''missing_keys''']   ) , 0   )

     model.to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      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 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Dict=      UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      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 						UpperCamelCase_				(  self   )       ->					Optional[Any]:
     # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update''' , output_loading_info=lowerCAmelCase   )
     model_accelerate.to(lowerCAmelCase   )
     model_accelerate.eval()

     SCREAMING_SNAKE_CASE__:   int=      torch.randn(
         1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0   ) , )
     SCREAMING_SNAKE_CASE__:   Tuple=      noise.to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      torch.tensor([10] * noise.shape[0]   ).to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   str=      model_accelerate(lowerCAmelCase , lowerCAmelCase   )['''sample''']

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

     SCREAMING_SNAKE_CASE__:   Dict=      UNetaDModel.from_pretrained(
         '''fusing/unet-ldm-dummy-update''' , output_loading_info=lowerCAmelCase , low_cpu_mem_usage=lowerCAmelCase   )
     model_normal_load.to(lowerCAmelCase   )
     model_normal_load.eval()
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      model_normal_load(lowerCAmelCase , lowerCAmelCase   )['''sample''']

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



    def 						UpperCamelCase_				(  self   )       ->					int:
     SCREAMING_SNAKE_CASE__:   int=      UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''   )
     model.eval()
     model.to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   int=      torch.randn(
         1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0   ) , )
     SCREAMING_SNAKE_CASE__:   Dict=      noise.to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      torch.tensor([10] * noise.shape[0]   ).to(lowerCAmelCase   )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE__:   Dict=      model(lowerCAmelCase , lowerCAmelCase   ).sample

     SCREAMING_SNAKE_CASE__:   List[str]=      output[0, -1, -3:, -3:].flatten().cpu()
     # fmt: off
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]   )
     # fmt: on

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






class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      UNetaDModel
    __a  =      "sample"
    @property
    def 						UpperCamelCase_				(  self , lowerCAmelCase=(32, 32)   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   int=      4
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      3

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

     return {"sample": noise, "timestep": time_step}
    @property
    def 						UpperCamelCase_				(  self   )       ->					Any:
     return (3, 32, 32)
    @property
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     return (3, 32, 32)
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   List[str]=      {
         '''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''',
         ],
     }
     SCREAMING_SNAKE_CASE__:   str=      self.dummy_input
     return init_dict, inputs_dict
    @slow
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   str=      UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256''' , output_loading_info=lowerCAmelCase   )
     self.assertIsNotNone(lowerCAmelCase   )
     self.assertEqual(len(loading_info['''missing_keys''']   ) , 0   )

     model.to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      self.dummy_input
     SCREAMING_SNAKE_CASE__:   Optional[int]=      floats_tensor((4, 3) + (256, 256)   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      noise
     SCREAMING_SNAKE_CASE__:   Optional[int]=      model(**lowerCAmelCase   )

     assert image is not None, "Make sure output is not None"
    @slow
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Dict=      UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''   )
     model.to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   List[str]=      4
     SCREAMING_SNAKE_CASE__:   Dict=      3
     SCREAMING_SNAKE_CASE__:   Optional[int]=      (256, 256)

     SCREAMING_SNAKE_CASE__:   Tuple=      torch.ones((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.tensor(batch_size * [1e-4]   ).to(lowerCAmelCase   )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      model(lowerCAmelCase , lowerCAmelCase   ).sample

     SCREAMING_SNAKE_CASE__:   Tuple=      output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]   )
     # fmt: on

     self.assertTrue(torch_all_close(lowerCAmelCase , lowerCAmelCase , rtol=1e-2   )   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''   )
     model.to(lowerCAmelCase   )

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

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

     with torch.no_grad():
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      model(lowerCAmelCase , lowerCAmelCase   ).sample

     SCREAMING_SNAKE_CASE__:   str=      output[0, -3:, -3:, -1].flatten().cpu()
     # fmt: off
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]   )
     # fmt: on

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



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     # not required for this model
     pass


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_     :   Dict 				=				{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AutoformerForPrediction',
          'AutoformerModel',
          'AutoformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      lowercase_     :   Tuple 				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

