"""simple docstring"""




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


_a      :		Optional[int]           =   logging.get_logger(__name__)

enable_full_determinism()
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Optional[int]							=		UNetaDModel
     _UpperCamelCase							:			int							=		"sample"






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     4
       _lowerCAmelCase						:       Optional[int]  			=     3
       _lowerCAmelCase						:       List[str]  			=     (32, 32)

       _lowerCAmelCase						:       str  			=     floats_tensor((batch_size, num_channels) + sizes  ).to(a__  )
       _lowerCAmelCase						:       Dict  			=     torch.tensor([10]  ).to(a__  )

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






     @property
     def     __A  (		self  ):
       return (3, 32, 32)






     @property
     def     __A  (		self  ):
       return (3, 32, 32)






     def     __A  (		self  ):
       _lowerCAmelCase						:       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,
       }
       _lowerCAmelCase						:       int  			=     self.dummy_input
       return init_dict, inputs_dict







class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Tuple							=		UNetaDModel
     _UpperCamelCase							:			Dict							=		"sample"






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     4
       _lowerCAmelCase						:       int  			=     4
       _lowerCAmelCase						:       Optional[int]  			=     (32, 32)

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

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






     @property
     def     __A  (		self  ):
       return (4, 32, 32)






     @property
     def     __A  (		self  ):
       return (4, 32, 32)






     def     __A  (		self  ):
       _lowerCAmelCase						:       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"""),
       }
       _lowerCAmelCase						:       int  			=     self.dummy_input
       return init_dict, inputs_dict






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     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__  )
       _lowerCAmelCase						:       Any  			=     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     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""				,     output_loading_info=a__  )
       model.to(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     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     __A  (		self  ):
       # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
       _lowerCAmelCase			,	_lowerCAmelCase						:       List[Any]  			=     UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""				,     output_loading_info=a__  )
       model_accelerate.to(a__  )
       model_accelerate.eval()

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

       _lowerCAmelCase						:       Dict  			=     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()

       _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     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()
       _lowerCAmelCase						:       Dict  			=     model_normal_load(a__				,     a__  )["""sample"""]

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""  )
       model.eval()
       model.to(a__  )

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

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

       _lowerCAmelCase						:       int  			=     output[0, -1, -3:, -3:].flatten().cpu()
       # fmt: off
       _lowerCAmelCase						:       Optional[int]  			=     torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0]  )
       # fmt: on

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







class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[str]							=		UNetaDModel
     _UpperCamelCase							:			Any							=		"sample"






     @property
     def     __A  (		self				,     a__=(32, 32)  ):
       _lowerCAmelCase						:       Optional[int]  			=     4
       _lowerCAmelCase						:       Dict  			=     3

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

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






     @property
     def     __A  (		self  ):
       return (3, 32, 32)






     @property
     def     __A  (		self  ):
       return (3, 32, 32)






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     {
           """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""",
           ],
       }
       _lowerCAmelCase						:       Optional[int]  			=     self.dummy_input
       return init_dict, inputs_dict






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, 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__  )
       _lowerCAmelCase						:       str  			=     self.dummy_input
       _lowerCAmelCase						:       Optional[Any]  			=     floats_tensor((4, 3) + (256, 256)  ).to(a__  )
       _lowerCAmelCase						:       Any  			=     noise
       _lowerCAmelCase						:       str  			=     model(**a__  )

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






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""  )
       model.to(a__  )

       _lowerCAmelCase						:       str  			=     4
       _lowerCAmelCase						:       List[str]  			=     3
       _lowerCAmelCase						:       Any  			=     (256, 256)

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

       with torch.no_grad():
         _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     a__  ).sample

       _lowerCAmelCase						:       Optional[Any]  			=     output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       _lowerCAmelCase						:       int  			=     torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8]  )
       # fmt: on

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""  )
       model.to(a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     4
       _lowerCAmelCase						:       Tuple  			=     3
       _lowerCAmelCase						:       int  			=     (32, 32)

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

       with torch.no_grad():
         _lowerCAmelCase						:       int  			=     model(a__				,     a__  ).sample

       _lowerCAmelCase						:       Tuple  			=     output[0, -3:, -3:, -1].flatten().cpu()
       # fmt: off
       _lowerCAmelCase						:       Optional[Any]  			=     torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6]  )
       # fmt: on

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






     def     __A  (		self  ):
       # not required for this model
       pass








from typing import TYPE_CHECKING

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


lowerCAmelCase__      		= {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechEncoderDecoderModel']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
    from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
