"""simple docstring"""



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	__lowerCAmelCase					(      a__  ,   a__  ,   unittest.TestCase):




    '''simple docstring'''






    __magic_name__   :			Optional[Any]       		=       AutoencoderKL
    __magic_name__   :			Any       		=       "sample"
    __magic_name__   :			Optional[int]       		=       1E-2





    @property
    def 					_UpperCAmelCase		(     self				:	int							):
          A__	:      List[Any]      =4
          A__	:      str      =3
          A__	:      Optional[Any]      =(32, 32)

          A__	:      Dict      =floats_tensor((batch_size, num_channels) + sizes							).to(lowerCamelCase_							)

          return {"sample": image}





    @property
    def 					_UpperCAmelCase		(     self				:	str							):
          return (3, 32, 32)





    @property
    def 					_UpperCAmelCase		(     self				:	Optional[Any]							):
          return (3, 32, 32)





    def 					_UpperCAmelCase		(     self				:	str							):
          A__	:      Optional[Any]      ={
              "block_out_channels": [32, 64],
              "in_channels": 3,
              "out_channels": 3,
              "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
              "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
              "latent_channels": 4,
          }
          A__	:      Any      =self.dummy_input
          return init_dict, inputs_dict





    def 					_UpperCAmelCase		(     self				:	List[str]							):
          pass





    def 					_UpperCAmelCase		(     self				:	List[str]							):
          pass





    @unittest.skipIf(torch_device == "mps"					,				"Gradient checkpointing skipped on MPS"							)
    def 					_UpperCAmelCase		(     self				:	int							):
          # enable deterministic behavior for gradient checkpointing
          A__      ,   A__	:      Optional[Any]      =self.prepare_init_args_and_inputs_for_common()
          A__	:      Any      =self.model_class(**lowerCamelCase_							)
          model.to(lowerCamelCase_							)

          assert not model.is_gradient_checkpointing and model.training

          A__	:      Dict      =model(**lowerCamelCase_							).sample
          # run the backwards pass on the model. For backwards pass, for simplicity purpose,
          # we won't calculate the loss and rather backprop on out.sum()
          model.zero_grad()

          A__	:      List[Any]      =torch.randn_like(lowerCamelCase_							)
          A__	:      Optional[Any]      =(out - labels).mean()
          loss.backward()

          # re-instantiate the model now enabling gradient checkpointing
          A__	:      Any      =self.model_class(**lowerCamelCase_							)
          # clone model
          model_a.load_state_dict(model.state_dict()							)
          model_a.to(lowerCamelCase_							)
          model_a.enable_gradient_checkpointing()

          assert model_a.is_gradient_checkpointing and model_a.training

          A__	:      Dict      =model_a(**lowerCamelCase_							).sample
          # run the backwards pass on the model. For backwards pass, for simplicity purpose,
          # we won't calculate the loss and rather backprop on out.sum()
          model_a.zero_grad()
          A__	:      Dict      =(out_a - labels).mean()
          loss_a.backward()

          # compare the output and parameters gradients
          self.assertTrue((loss - loss_a).abs() < 1E-5							)
          A__	:      Any      =dict(model.named_parameters()							)
          A__	:      int      =dict(model_a.named_parameters()							)
          for name, param in named_params.items():
                self.assertTrue(torch_all_close(param.grad.data					,				named_params_a[name].grad.data					,				atol=5E-5							)							)





    def 					_UpperCAmelCase		(     self				:	int							):
          A__      ,   A__	:      int      =AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"					,				output_loading_info=lowerCamelCase_							)
          self.assertIsNotNone(lowerCamelCase_							)
          self.assertEqual(len(loading_info["missing_keys"]							)					,				0							)

          model.to(lowerCamelCase_							)
          A__	:      Any      =model(**self.dummy_input							)

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





    def 					_UpperCAmelCase		(     self				:	Optional[int]							):
          A__	:      List[str]      =AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"							)
          A__	:      List[Any]      =model.to(lowerCamelCase_							)
          model.eval()

          if torch_device == "mps":
                A__	:      Any      =torch.manual_seed(0							)
          else:
                A__	:      Optional[int]      =torch.Generator(device=lowerCamelCase_							).manual_seed(0							)

          A__	:      List[Any]      =torch.randn(
              1					,				model.config.in_channels					,				model.config.sample_size					,				model.config.sample_size					,				generator=torch.manual_seed(0							)					,				)
          A__	:      Optional[Any]      =image.to(lowerCamelCase_							)
          with torch.no_grad():
                A__	:      Any      =model(lowerCamelCase_					,				sample_posterior=lowerCamelCase_					,				generator=lowerCamelCase_							).sample

          A__	:      Union[str, Any]      =output[0, -1, -3:, -3:].flatten().cpu()

          # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
          # the expected output slices are not the same for CPU and GPU.
          if torch_device == "mps":
                A__	:      Any      =torch.tensor(
                    [
                        -4.0078E-01,
                        -3.8323E-04,
                        -1.2681E-01,
                        -1.1462E-01,
                        2.0095E-01,
                        1.0893E-01,
                        -8.8247E-02,
                        -3.0361E-01,
                        -9.8644E-03,
                    ]							)
          elif torch_device == "cpu":
                A__	:      List[str]      =torch.tensor(
                    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]							)
          else:
                A__	:      Tuple      =torch.tensor(
                    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]							)

          self.assertTrue(torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				rtol=1E-2							)							)






@slow
class 	__lowerCAmelCase					(      unittest.TestCase):




    '''simple docstring'''






    def 					_UpperCAmelCase		(     self				:	Any					,				UpperCamelCase__				:	Optional[Any]					,				UpperCamelCase__				:	Optional[Any]							):
          return F'''gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase_							) for s in shape]							)}.npy'''





    def 					_UpperCAmelCase		(     self				:	Union[str, Any]							):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()





    def 					_UpperCAmelCase		(     self				:	Optional[Any]					,				UpperCamelCase__				:	Union[str, Any]=0					,				UpperCamelCase__				:	Any=(4, 3, 512, 512)					,				UpperCamelCase__				:	int=False							):
          A__	:      int      =torch.floataa if fpaa else torch.floataa
          A__	:      int      =torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase_					,				lowerCamelCase_							)							)							).to(lowerCamelCase_							).to(lowerCamelCase_							)
          return image





    def 					_UpperCAmelCase		(     self				:	List[str]					,				UpperCamelCase__				:	Union[str, Any]="CompVis/stable-diffusion-v1-4"					,				UpperCamelCase__				:	Tuple=False							):
          A__	:      Union[str, Any]      ="fp16" if fpaa else None
          A__	:      str      =torch.floataa if fpaa else torch.floataa

          A__	:      List[str]      =AutoencoderKL.from_pretrained(
              lowerCamelCase_					,				subfolder="vae"					,				torch_dtype=lowerCamelCase_					,				revision=lowerCamelCase_					,				)
          model.to(lowerCamelCase_							).eval()

          return model





    def 					_UpperCAmelCase		(     self				:	Dict					,				UpperCamelCase__				:	List[Any]=0							):
          if torch_device == "mps":
                return torch.manual_seed(lowerCamelCase_							)
          return torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)





    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
            [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
            # fmt: on
        ]							)
    def 					_UpperCAmelCase		(     self				:	str					,				UpperCamelCase__				:	Dict					,				UpperCamelCase__				:	Dict					,				UpperCamelCase__				:	int							):
          A__	:      Tuple      =self.get_sd_vae_model()
          A__	:      Any      =self.get_sd_image(lowerCamelCase_							)
          A__	:      Optional[Any]      =self.get_generator(lowerCamelCase_							)

          with torch.no_grad():
                A__	:      Optional[int]      =model(lowerCamelCase_					,				generator=lowerCamelCase_					,				sample_posterior=lowerCamelCase_							).sample

          assert sample.shape == image.shape

          A__	:      Optional[Any]      =sample[-1, -2:, -2:, :2].flatten().float().cpu()
          A__	:      Any      =torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice							)

          assert torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				atol=3E-3							)





    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
            [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
            # fmt: on
        ]							)
    @require_torch_gpu
    def 					_UpperCAmelCase		(     self				:	Union[str, Any]					,				UpperCamelCase__				:	Any					,				UpperCamelCase__				:	str							):
          A__	:      Tuple      =self.get_sd_vae_model(fpaa=lowerCamelCase_							)
          A__	:      List[Any]      =self.get_sd_image(lowerCamelCase_					,				fpaa=lowerCamelCase_							)
          A__	:      Tuple      =self.get_generator(lowerCamelCase_							)

          with torch.no_grad():
                A__	:      str      =model(lowerCamelCase_					,				generator=lowerCamelCase_					,				sample_posterior=lowerCamelCase_							).sample

          assert sample.shape == image.shape

          A__	:      str      =sample[-1, -2:, :2, -2:].flatten().float().cpu()
          A__	:      List[Any]      =torch.tensor(lowerCamelCase_							)

          assert torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				atol=1E-2							)





    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
            [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
            # fmt: on
        ]							)
    def 					_UpperCAmelCase		(     self				:	List[str]					,				UpperCamelCase__				:	Union[str, Any]					,				UpperCamelCase__				:	int					,				UpperCamelCase__				:	Tuple							):
          A__	:      Dict      =self.get_sd_vae_model()
          A__	:      str      =self.get_sd_image(lowerCamelCase_							)

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

          assert sample.shape == image.shape

          A__	:      List[Any]      =sample[-1, -2:, -2:, :2].flatten().float().cpu()
          A__	:      Tuple      =torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice							)

          assert torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				atol=3E-3							)





    @parameterized.expand(
        [
            # fmt: off
            [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
            [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
            # fmt: on
        ]							)
    @require_torch_gpu
    def 					_UpperCAmelCase		(     self				:	str					,				UpperCamelCase__				:	int					,				UpperCamelCase__				:	Union[str, Any]							):
          A__	:      Any      =self.get_sd_vae_model()
          A__	:      Optional[Any]      =self.get_sd_image(lowerCamelCase_					,				shape=(3, 4, 64, 64)							)

          with torch.no_grad():
                A__	:      Union[str, Any]      =model.decode(lowerCamelCase_							).sample

          assert list(sample.shape							) == [3, 3, 512, 512]

          A__	:      Dict      =sample[-1, -2:, :2, -2:].flatten().cpu()
          A__	:      int      =torch.tensor(lowerCamelCase_							)

          assert torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				atol=1E-3							)





    @parameterized.expand(
        [
            # fmt: off
            [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
            [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
            # fmt: on
        ]							)
    @require_torch_gpu
    def 					_UpperCAmelCase		(     self				:	str					,				UpperCamelCase__				:	Any					,				UpperCamelCase__				:	Optional[Any]							):
          A__	:      Tuple      =self.get_sd_vae_model(fpaa=lowerCamelCase_							)
          A__	:      List[str]      =self.get_sd_image(lowerCamelCase_					,				shape=(3, 4, 64, 64)					,				fpaa=lowerCamelCase_							)

          with torch.no_grad():
                A__	:      Any      =model.decode(lowerCamelCase_							).sample

          assert list(sample.shape							) == [3, 3, 512, 512]

          A__	:      List[Any]      =sample[-1, -2:, :2, -2:].flatten().float().cpu()
          A__	:      List[str]      =torch.tensor(lowerCamelCase_							)

          assert torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				atol=5E-3							)





    @parameterized.expand([(13,), (16,), (27,)]							)
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()					,				reason="xformers is not required when using PyTorch 2.0."							)
    def 					_UpperCAmelCase		(     self				:	List[Any]					,				UpperCamelCase__				:	str							):
          A__	:      Dict      =self.get_sd_vae_model(fpaa=lowerCamelCase_							)
          A__	:      Optional[Any]      =self.get_sd_image(lowerCamelCase_					,				shape=(3, 4, 64, 64)					,				fpaa=lowerCamelCase_							)

          with torch.no_grad():
                A__	:      Tuple      =model.decode(lowerCamelCase_							).sample

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
                A__	:      List[str]      =model.decode(lowerCamelCase_							).sample

          assert list(sample.shape							) == [3, 3, 512, 512]

          assert torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				atol=1E-1							)





    @parameterized.expand([(13,), (16,), (37,)]							)
    @require_torch_gpu
    @unittest.skipIf(not is_xformers_available()					,				reason="xformers is not required when using PyTorch 2.0."							)
    def 					_UpperCAmelCase		(     self				:	Union[str, Any]					,				UpperCamelCase__				:	int							):
          A__	:      Tuple      =self.get_sd_vae_model()
          A__	:      Optional[int]      =self.get_sd_image(lowerCamelCase_					,				shape=(3, 4, 64, 64)							)

          with torch.no_grad():
                A__	:      Any      =model.decode(lowerCamelCase_							).sample

          model.enable_xformers_memory_efficient_attention()
          with torch.no_grad():
                A__	:      List[str]      =model.decode(lowerCamelCase_							).sample

          assert list(sample.shape							) == [3, 3, 512, 512]

          assert torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				atol=1E-2							)





    @parameterized.expand(
        [
            # fmt: off
            [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
            [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
            # fmt: on
        ]							)
    def 					_UpperCAmelCase		(     self				:	Optional[int]					,				UpperCamelCase__				:	Any					,				UpperCamelCase__				:	Optional[Any]							):
          A__	:      str      =self.get_sd_vae_model()
          A__	:      str      =self.get_sd_image(lowerCamelCase_							)
          A__	:      List[Any]      =self.get_generator(lowerCamelCase_							)

          with torch.no_grad():
                A__	:      Union[str, Any]      =model.encode(lowerCamelCase_							).latent_dist
                A__	:      Any      =dist.sample(generator=lowerCamelCase_							)

          assert list(sample.shape							) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

          A__	:      Optional[Any]      =sample[0, -1, -3:, -3:].flatten().cpu()
          A__	:      Dict      =torch.tensor(lowerCamelCase_							)

          A__	:      Dict      =3E-3 if torch_device != "mps" else 1E-2
          assert torch_all_close(lowerCamelCase_					,				lowerCamelCase_					,				atol=lowerCamelCase_							)









'''simple docstring'''






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCAmelCase       							=				[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def    _snake_case  (	A					,       A=None					,       A=None					,       A=None			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      True
							while ask_again:
														lowerCAmelCase__			  =      input(A			)
														try:
																					if default is not None and len(A			) == 0:
																												return default
																					return convert_value(A			) if convert_value is not None else result
														except Exception:
																					if error_message is not None:
																												print(A			)


def    _snake_case  (	A					,       A=[]					,       A=None					,       A=0			)		->       List[Any]:
							lowerCAmelCase__			  =      BulletMenu(A					,       A			)
							lowerCAmelCase__			  =      menu.run(default_choice=A			)
							return convert_value(A			) if convert_value is not None else result


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      int(A			)
							return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]			)


def    _snake_case  (	A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      int(A			)
							return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]			)


def    _snake_case  (	A			)		->       str:
							lowerCAmelCase__			  =      int(A			)
							return DynamoBackend(DYNAMO_BACKENDS[value]			).value


def    _snake_case  (	A			)		->       Tuple:
							lowerCAmelCase__			  =      int(A			)
							return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]			)


def    _snake_case  (	A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      int(A			)
							return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]			)


def    _snake_case  (	A			)		->       List[str]:
							return {"yes": True, "no": False}[value.lower()]






class 						a__							(				argparse.RawDescriptionHelpFormatter    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Dict:
														lowerCAmelCase__			  =      super()._format_usage(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      usage.replace('''<command> [<args>] '''   ,       ''''''  )
														return usage