
"""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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Tuple							=					AutoencoderKL
				SCREAMING_SNAKE_CASE_:							Tuple							=					"sample"
				SCREAMING_SNAKE_CASE_:							Tuple							=					1e-2


				@property
				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			4
											_lowerCAmelCase  	=			3
											_lowerCAmelCase  	=			(32, 32)

											_lowerCAmelCase  	=			floats_tensor((batch_size, num_channels) + sizes  ).to(UpperCAmelCase_  )

											return {"sample": image}


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Dict:







											"""simple docstring"""




											return (3, 32, 32)


				@property
				def 				__lowerCamelCase	(					self	:			int  )		->  Optional[int]:







											"""simple docstring"""




											return (3, 32, 32)


				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			{
											    'block_out_channels': [32, 64],
											    'in_channels': 3,
											    'out_channels': 3,
											    'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
											    'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
											    'latent_channels': 4,
											}
											_lowerCAmelCase  	=			self.dummy_input
											return init_dict, inputs_dict


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  str:







											"""simple docstring"""




											pass


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







											"""simple docstring"""




											pass


				@unittest.skipIf(torch_device == 'mps'				,     'Gradient checkpointing skipped on MPS'  )
				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			self.prepare_init_args_and_inputs_for_common()
											_lowerCAmelCase  	=			self.model_class(**UpperCAmelCase_  )
											model.to(UpperCAmelCase_  )

											assert not model.is_gradient_checkpointing and model.training

											_lowerCAmelCase  	=			model(**UpperCAmelCase_  ).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()

											_lowerCAmelCase  	=			torch.randn_like(UpperCAmelCase_  )
											_lowerCAmelCase  	=			(out - labels).mean()
											loss.backward()

											# re-instantiate the model now enabling gradient checkpointing
											_lowerCAmelCase  	=			self.model_class(**UpperCAmelCase_  )
											# clone model
											model_a.load_state_dict(model.state_dict()  )
											model_a.to(UpperCAmelCase_  )
											model_a.enable_gradient_checkpointing()

											assert model_a.is_gradient_checkpointing and model_a.training

											_lowerCAmelCase  	=			model_a(**UpperCAmelCase_  ).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()
											_lowerCAmelCase  	=			(out_a - labels).mean()
											loss_a.backward()

											# compare the output and parameters gradients
											self.assertTrue((loss - loss_a).abs() < 1E-5  )
											_lowerCAmelCase  	=			dict(model.named_parameters()  )
											_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			int  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase	,		_lowerCAmelCase  	=			AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'				,     output_loading_info=UpperCAmelCase_  )
											self.assertIsNotNone(UpperCAmelCase_  )
											self.assertEqual(len(loading_info['missing_keys']  )				,     0  )

											model.to(UpperCAmelCase_  )
											_lowerCAmelCase  	=			model(**self.dummy_input  )

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







				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			AutoencoderKL.from_pretrained('fusing/autoencoder-kl-dummy'  )
											_lowerCAmelCase  	=			model.to(UpperCAmelCase_  )
											model.eval()

											if torch_device == "mps":
																		_lowerCAmelCase  	=			torch.manual_seed(0  )
											else:
																		_lowerCAmelCase  	=			torch.Generator(device=UpperCAmelCase_  ).manual_seed(0  )

											_lowerCAmelCase  	=			torch.randn(
											    1				,     model.config.in_channels				,     model.config.sample_size				,     model.config.sample_size				,     generator=torch.manual_seed(0  )				,     )
											_lowerCAmelCase  	=			image.to(UpperCAmelCase_  )
											with torch.no_grad():
																		_lowerCAmelCase  	=			model(UpperCAmelCase_				,     sample_posterior=UpperCAmelCase_				,     generator=UpperCAmelCase_  ).sample

											_lowerCAmelCase  	=			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":
																		_lowerCAmelCase  	=			torch.tensor(
																		    [
																		        -4.0_0_7_8E-0_1,
																		        -3.8_3_2_3E-0_4,
																		        -1.2_6_8_1E-0_1,
																		        -1.1_4_6_2E-0_1,
																		        2.0_0_9_5E-0_1,
																		        1.0_8_9_3E-0_1,
																		        -8.8_2_4_7E-0_2,
																		        -3.0_3_6_1E-0_1,
																		        -9.8_6_4_4E-0_3,
																		    ]  )
											elif torch_device == "cpu":
																		_lowerCAmelCase  	=			torch.tensor(
																		    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]  )
											else:
																		_lowerCAmelCase  	=			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(UpperCAmelCase_				,     UpperCAmelCase_				,     rtol=1E-2  )  )






@slow
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Optional[Any]  )		->  Optional[int]:







											"""simple docstring"""




											return F"""gaussian_noise_s={seed}_shape={'_'.join([str(UpperCAmelCase_  ) for s in shape]  )}.npy"""


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Any:







											"""simple docstring"""




											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[Any]=0				,     UpperCAmelCase_	:			Union[str, Any]=(4, 3, 512, 512)				,     UpperCAmelCase_	:			Any=False  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.floataa if fpaa else torch.floataa
											_lowerCAmelCase  	=			torch.from_numpy(load_hf_numpy(self.get_file_format(UpperCAmelCase_				,     UpperCAmelCase_  )  )  ).to(UpperCAmelCase_  ).to(UpperCAmelCase_  )
											return image


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Optional[int]="CompVis/stable-diffusion-v1-4"				,     UpperCAmelCase_	:			str=False  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			'fp16' if fpaa else None
											_lowerCAmelCase  	=			torch.floataa if fpaa else torch.floataa

											_lowerCAmelCase  	=			AutoencoderKL.from_pretrained(
											    UpperCAmelCase_				,     subfolder='vae'				,     torch_dtype=UpperCAmelCase_				,     revision=UpperCAmelCase_				,     )
											model.to(UpperCAmelCase_  ).eval()

											return model


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







											"""simple docstring"""




											if torch_device == "mps":
																		return torch.manual_seed(UpperCAmelCase_  )
											return torch.Generator(device=UpperCAmelCase_  ).manual_seed(UpperCAmelCase_  )


				@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 				__lowerCamelCase	(					self	:			Union[str, Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			List[Any]				,     UpperCAmelCase_	:			str  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_sd_vae_model()
											_lowerCAmelCase  	=			self.get_sd_image(UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.get_generator(UpperCAmelCase_  )

											with torch.no_grad():
																		_lowerCAmelCase  	=			model(UpperCAmelCase_				,     generator=UpperCAmelCase_				,     sample_posterior=UpperCAmelCase_  ).sample

											assert sample.shape == image.shape

											_lowerCAmelCase  	=			sample[-1, -2:, -2:, :2].flatten().float().cpu()
											_lowerCAmelCase  	=			torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice  )

											assert torch_all_close(UpperCAmelCase_				,     UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			List[str]				,     UpperCAmelCase_	:			Tuple				,     UpperCAmelCase_	:			List[str]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_sd_vae_model(fpaa=UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.get_sd_image(UpperCAmelCase_				,     fpaa=UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.get_generator(UpperCAmelCase_  )

											with torch.no_grad():
																		_lowerCAmelCase  	=			model(UpperCAmelCase_				,     generator=UpperCAmelCase_				,     sample_posterior=UpperCAmelCase_  ).sample

											assert sample.shape == image.shape

											_lowerCAmelCase  	=			sample[-1, -2:, :2, -2:].flatten().float().cpu()
											_lowerCAmelCase  	=			torch.tensor(UpperCAmelCase_  )

											assert torch_all_close(UpperCAmelCase_				,     UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			Any  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_sd_vae_model()
											_lowerCAmelCase  	=			self.get_sd_image(UpperCAmelCase_  )

											with torch.no_grad():
																		_lowerCAmelCase  	=			model(UpperCAmelCase_  ).sample

											assert sample.shape == image.shape

											_lowerCAmelCase  	=			sample[-1, -2:, -2:, :2].flatten().float().cpu()
											_lowerCAmelCase  	=			torch.tensor(expected_slice_mps if torch_device == 'mps' else expected_slice  )

											assert torch_all_close(UpperCAmelCase_				,     UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Union[str, Any]  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_sd_vae_model()
											_lowerCAmelCase  	=			self.get_sd_image(UpperCAmelCase_				,     shape=(3, 4, 64, 64)  )

											with torch.no_grad():
																		_lowerCAmelCase  	=			model.decode(UpperCAmelCase_  ).sample

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

											_lowerCAmelCase  	=			sample[-1, -2:, :2, -2:].flatten().cpu()
											_lowerCAmelCase  	=			torch.tensor(UpperCAmelCase_  )

											assert torch_all_close(UpperCAmelCase_				,     UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			Dict  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_sd_vae_model(fpaa=UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.get_sd_image(UpperCAmelCase_				,     shape=(3, 4, 64, 64)				,     fpaa=UpperCAmelCase_  )

											with torch.no_grad():
																		_lowerCAmelCase  	=			model.decode(UpperCAmelCase_  ).sample

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

											_lowerCAmelCase  	=			sample[-1, -2:, :2, -2:].flatten().float().cpu()
											_lowerCAmelCase  	=			torch.tensor(UpperCAmelCase_  )

											assert torch_all_close(UpperCAmelCase_				,     UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			Tuple  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_sd_vae_model(fpaa=UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.get_sd_image(UpperCAmelCase_				,     shape=(3, 4, 64, 64)				,     fpaa=UpperCAmelCase_  )

											with torch.no_grad():
																		_lowerCAmelCase  	=			model.decode(UpperCAmelCase_  ).sample

											model.enable_xformers_memory_efficient_attention()
											with torch.no_grad():
																		_lowerCAmelCase  	=			model.decode(UpperCAmelCase_  ).sample

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

											assert torch_all_close(UpperCAmelCase_				,     UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			int  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_sd_vae_model()
											_lowerCAmelCase  	=			self.get_sd_image(UpperCAmelCase_				,     shape=(3, 4, 64, 64)  )

											with torch.no_grad():
																		_lowerCAmelCase  	=			model.decode(UpperCAmelCase_  ).sample

											model.enable_xformers_memory_efficient_attention()
											with torch.no_grad():
																		_lowerCAmelCase  	=			model.decode(UpperCAmelCase_  ).sample

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

											assert torch_all_close(UpperCAmelCase_				,     UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			Optional[int]				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Optional[Any]  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.get_sd_vae_model()
											_lowerCAmelCase  	=			self.get_sd_image(UpperCAmelCase_  )
											_lowerCAmelCase  	=			self.get_generator(UpperCAmelCase_  )

											with torch.no_grad():
																		_lowerCAmelCase  	=			model.encode(UpperCAmelCase_  ).latent_dist
																		_lowerCAmelCase  	=			dist.sample(generator=UpperCAmelCase_  )

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

											_lowerCAmelCase  	=			sample[0, -1, -3:, -3:].flatten().cpu()
											_lowerCAmelCase  	=			torch.tensor(UpperCAmelCase_  )

											_lowerCAmelCase  	=			3E-3 if torch_device != 'mps' else 1E-2
											assert torch_all_close(UpperCAmelCase_				,     UpperCAmelCase_				,     atol=UpperCAmelCase_  )








"""simple docstring"""



import pytest


_snake_case          =						'''__dummy_dataset1__'''

_snake_case          =						'''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			dataset_loading_script_name
							_lowerCAmelCase  	=			tmp_path / 'datasets' / script_name
							script_dir.mkdir(parents=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			script_dir / f"""{script_name}.py"""
							with open(SCREAMING_SNAKE_CASE     ,  'w'						) as f:
														f.write(SCREAMING_SNAKE_CASE						)
							return str(SCREAMING_SNAKE_CASE						)






