



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        UpperCAmelCase_  (       UpperCamelCase_   ,   UpperCamelCase_   ,   unittest.TestCase	):

						'''simple docstring'''




						UpperCamelCase__      :			Dict      =  AutoencoderKL
						UpperCamelCase__      :			Union[str, Any]      =  '''sample'''
						UpperCamelCase__      :			str      =  1e-2






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 4
													__SCREAMING_SNAKE_CASE									= 3
													__SCREAMING_SNAKE_CASE									= (32, 32)

													__SCREAMING_SNAKE_CASE									= floats_tensor((batch_size, num_channels) + sizes		).to(_A		)

													return {"sample": image}






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return (3, 32, 32)






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return (3, 32, 32)






						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

													pass






						def 	_A     (   self		):







													'''simple docstring'''

													pass






						@unittest.skipIf(torch_device == 'mps'				,							'Gradient checkpointing skipped on MPS'		)
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= self.prepare_init_args_and_inputs_for_common()
													__SCREAMING_SNAKE_CASE									= self.model_class(**_A		)
													model.to(_A		)

													assert not model.is_gradient_checkpointing and model.training

													__SCREAMING_SNAKE_CASE									= model(**_A		).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()

													__SCREAMING_SNAKE_CASE									= torch.randn_like(_A		)
													__SCREAMING_SNAKE_CASE									= (out - labels).mean()
													loss.backward()

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

													assert model_a.is_gradient_checkpointing and model_a.training

													__SCREAMING_SNAKE_CASE									= model_a(**_A		).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()
													__SCREAMING_SNAKE_CASE									= (out_a - labels).mean()
													loss_a.backward()

													# compare the output and parameters gradients
													self.assertTrue((loss - loss_a).abs() < 1e-5		)
													__SCREAMING_SNAKE_CASE									= dict(model.named_parameters()		)
													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

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

													model.to(_A		)
													__SCREAMING_SNAKE_CASE									= model(**self.dummy_input		)

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






						def 	_A     (   self		):







													'''simple docstring'''

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

													if torch_device == "mps":
																				__SCREAMING_SNAKE_CASE									= torch.manual_seed(0		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.Generator(device=_A		).manual_seed(0		)

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

													__SCREAMING_SNAKE_CASE									= 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":
																				__SCREAMING_SNAKE_CASE									= 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":
																				__SCREAMING_SNAKE_CASE									= torch.tensor(
																				    [-0.1_3_5_2, 0.0_8_7_8, 0.0_4_1_9, -0.0_8_1_8, -0.1_0_6_9, 0.0_6_8_8, -0.1_4_5_8, -0.4_4_4_6, -0.0_0_2_6]		)
													else:
																				__SCREAMING_SNAKE_CASE									= torch.tensor(
																				    [-0.2_4_2_1, 0.4_6_4_2, 0.2_5_0_7, -0.0_4_3_8, 0.0_6_8_2, 0.3_1_6_0, -0.2_0_1_8, -0.0_7_2_7, 0.2_4_8_5]		)

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







@slow
class        UpperCAmelCase_  (       unittest.TestCase	):

						'''simple docstring'''




						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

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






						def 	_A     (   self		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A=0				,							_A=(4, 3, 512, 512)				,							_A=False		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A="CompVis/stable-diffusion-v1-4"				,							_A=False		):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE									= AutoencoderKL.from_pretrained(
													    _A				,							subfolder='vae'				,							torch_dtype=_A				,							revision=_A				,							)
													model.to(_A		).eval()

													return model






						def 	_A     (   self				,							_A=0		):







													'''simple docstring'''

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






						@parameterized.expand(
						    [
						        # fmt: off
						        [33, [-0.1_6_0_3, 0.9_8_7_8, -0.0_4_9_5, -0.0_7_9_0, -0.2_7_0_9, 0.8_3_7_5, -0.2_0_6_0, -0.0_8_2_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
						        [47, [-0.2_3_7_6, 0.1_1_6_8, 0.1_3_3_2, -0.4_8_4_0, -0.2_5_0_8, -0.0_7_9_1, -0.0_4_9_3, -0.4_0_8_9], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
						        # fmt: on
						    ]		)
						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_sd_vae_model()
													__SCREAMING_SNAKE_CASE									= self.get_sd_image(_A		)
													__SCREAMING_SNAKE_CASE									= self.get_generator(_A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(_A				,							generator=_A				,							sample_posterior=_A		).sample

													assert sample.shape == image.shape

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

													assert torch_all_close(_A				,							_A				,							atol=3e-3		)






						@parameterized.expand(
						    [
						        # fmt: off
						        [33, [-0.0_5_1_3, 0.0_2_8_9, 1.3_7_9_9, 0.2_1_6_6, -0.2_5_7_3, -0.0_8_7_1, 0.5_1_0_3, -0.0_9_9_9]],
						        [47, [-0.4_1_2_8, -0.1_3_2_0, -0.3_7_0_4, 0.1_9_6_5, -0.4_1_1_6, -0.2_3_3_2, -0.3_3_4_0, 0.2_2_4_7]],
						        # fmt: on
						    ]		)
						@require_torch_gpu
						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_sd_vae_model(fpaa=_A		)
													__SCREAMING_SNAKE_CASE									= self.get_sd_image(_A				,							fpaa=_A		)
													__SCREAMING_SNAKE_CASE									= self.get_generator(_A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(_A				,							generator=_A				,							sample_posterior=_A		).sample

													assert sample.shape == image.shape

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

													assert torch_all_close(_A				,							_A				,							atol=1e-2		)






						@parameterized.expand(
						    [
						        # fmt: off
						        [33, [-0.1_6_0_9, 0.9_8_6_6, -0.0_4_8_7, -0.0_7_7_7, -0.2_7_1_6, 0.8_3_6_8, -0.2_0_5_5, -0.0_8_1_4], [-0.2_3_9_5, 0.0_0_9_8, 0.0_1_0_2, -0.0_7_0_9, -0.2_8_4_0, -0.0_2_7_4, -0.0_7_1_8, -0.1_8_2_4]],
						        [47, [-0.2_3_7_7, 0.1_1_4_7, 0.1_3_3_3, -0.4_8_4_1, -0.2_5_0_6, -0.0_8_0_5, -0.0_4_9_1, -0.4_0_8_5], [0.0_3_5_0, 0.0_8_4_7, 0.0_4_6_7, 0.0_3_4_4, -0.0_8_4_2, -0.0_5_4_7, -0.0_6_3_3, -0.1_1_3_1]],
						        # fmt: on
						    ]		)
						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_sd_vae_model()
													__SCREAMING_SNAKE_CASE									= self.get_sd_image(_A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model(_A		).sample

													assert sample.shape == image.shape

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

													assert torch_all_close(_A				,							_A				,							atol=3e-3		)






						@parameterized.expand(
						    [
						        # fmt: off
						        [13, [-0.2_0_5_1, -0.1_8_0_3, -0.2_3_1_1, -0.2_1_1_4, -0.3_2_9_2, -0.3_5_7_4, -0.2_9_5_3, -0.3_3_2_3]],
						        [37, [-0.2_6_3_2, -0.2_6_2_5, -0.2_1_9_9, -0.2_7_4_1, -0.4_5_3_9, -0.4_9_9_0, -0.3_7_2_0, -0.4_9_2_5]],
						        # fmt: on
						    ]		)
						@require_torch_gpu
						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_sd_vae_model()
													__SCREAMING_SNAKE_CASE									= self.get_sd_image(_A				,							shape=(3, 4, 64, 64)		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model.decode(_A		).sample

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

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

													assert torch_all_close(_A				,							_A				,							atol=1e-3		)






						@parameterized.expand(
						    [
						        # fmt: off
						        [27, [-0.0_3_6_9, 0.0_2_0_7, -0.0_7_7_6, -0.0_6_8_2, -0.1_7_4_7, -0.1_9_3_0, -0.1_4_6_5, -0.2_0_3_9]],
						        [16, [-0.1_6_2_8, -0.2_1_3_4, -0.2_7_4_7, -0.2_6_4_2, -0.3_7_7_4, -0.4_4_0_4, -0.3_6_8_7, -0.4_2_7_7]],
						        # fmt: on
						    ]		)
						@require_torch_gpu
						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_sd_vae_model(fpaa=_A		)
													__SCREAMING_SNAKE_CASE									= self.get_sd_image(_A				,							shape=(3, 4, 64, 64)				,							fpaa=_A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model.decode(_A		).sample

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

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

													assert torch_all_close(_A				,							_A				,							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 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_sd_vae_model(fpaa=_A		)
													__SCREAMING_SNAKE_CASE									= self.get_sd_image(_A				,							shape=(3, 4, 64, 64)				,							fpaa=_A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model.decode(_A		).sample

													model.enable_xformers_memory_efficient_attention()
													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model.decode(_A		).sample

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

													assert torch_all_close(_A				,							_A				,							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 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_sd_vae_model()
													__SCREAMING_SNAKE_CASE									= self.get_sd_image(_A				,							shape=(3, 4, 64, 64)		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model.decode(_A		).sample

													model.enable_xformers_memory_efficient_attention()
													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model.decode(_A		).sample

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

													assert torch_all_close(_A				,							_A				,							atol=1e-2		)






						@parameterized.expand(
						    [
						        # fmt: off
						        [33, [-0.3_0_0_1, 0.0_9_1_8, -2.6_9_8_4, -3.9_7_2_0, -3.2_0_9_9, -5.0_3_5_3, 1.7_3_3_8, -0.2_0_6_5, 3.4_2_6_7]],
						        [47, [-1.5_0_3_0, -4.3_8_7_1, -6.0_3_5_5, -9.1_1_5_7, -1.6_6_6_1, -2.7_8_5_3, 2.1_6_0_7, -5.0_8_2_3, 2.5_6_3_3]],
						        # fmt: on
						    ]		)
						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_sd_vae_model()
													__SCREAMING_SNAKE_CASE									= self.get_sd_image(_A		)
													__SCREAMING_SNAKE_CASE									= self.get_generator(_A		)

													with torch.no_grad():
																				__SCREAMING_SNAKE_CASE									= model.encode(_A		).latent_dist
																				__SCREAMING_SNAKE_CASE									= dist.sample(generator=_A		)

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

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

													__SCREAMING_SNAKE_CASE									= 3e-3 if torch_device != 'mps' else 1e-2
													assert torch_all_close(_A				,							_A				,							atol=_A		)











import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
			import sqlitea

			import sqlalchemy

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A = None				,							_A = None				,							_A = False				,							**_A				,							):







													'''simple docstring'''

													super().__init__(features=_A				,							cache_dir=_A				,							keep_in_memory=_A				,							**_A		)
													__SCREAMING_SNAKE_CASE									= Sql(
													    cache_dir=_A				,							features=_A				,							sql=_A				,							con=_A				,							**_A				,							)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None

													self.builder.download_and_prepare(
													    download_config=_A				,							download_mode=_A				,							verification_mode=_A				,							base_path=_A				,							)

													# Build dataset for splits
													__SCREAMING_SNAKE_CASE									= self.builder.as_dataset(
													    split='train'				,							verification_mode=_A				,							in_memory=self.keep_in_memory		)
													return dataset







class        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A				,							_A = None				,							_A = None				,							**_A				,							):







													'''simple docstring'''

													if num_proc is not None and num_proc <= 0:
																				raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""		)

													__SCREAMING_SNAKE_CASE									= dataset
													__SCREAMING_SNAKE_CASE									= name
													__SCREAMING_SNAKE_CASE									= con
													__SCREAMING_SNAKE_CASE									= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													__SCREAMING_SNAKE_CASE									= num_proc
													__SCREAMING_SNAKE_CASE									= to_sql_kwargs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('sql'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('con'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('index'				,							_A		)

													__SCREAMING_SNAKE_CASE									= self._write(index=_A				,							**self.to_sql_kwargs		)
													return written






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= args
													__SCREAMING_SNAKE_CASE									= {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
													__SCREAMING_SNAKE_CASE									= query_table(
													    table=self.dataset.data				,							key=slice(_A				,							offset + self.batch_size		)				,							indices=self.dataset._indices				,							)
													__SCREAMING_SNAKE_CASE									= batch.to_pandas()
													__SCREAMING_SNAKE_CASE									= df.to_sql(self.name				,							self.con				,							index=_A				,							**_A		)
													return num_rows or len(_A		)






						def 	_A     (   self				,							_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 0

													if self.num_proc is None or self.num_proc == 1:
																				for offset in logging.tqdm(
																				    range(0				,							len(self.dataset		)				,							self.batch_size		)				,							unit='ba'				,							disable=not logging.is_progress_bar_enabled()				,							desc='Creating SQL from Arrow format'				,							):
																											written += self._batch_sql((offset, index, to_sql_kwargs)		)
													else:
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= len(self.dataset		), self.batch_size
																				with multiprocessing.Pool(self.num_proc		) as pool:
																											for num_rows in logging.tqdm(
																											    pool.imap(
																											        self._batch_sql				,							[(offset, index, to_sql_kwargs) for offset in range(0				,							_A				,							_A		)]				,							)				,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,							unit='ba'				,							disable=not logging.is_progress_bar_enabled()				,							desc='Creating SQL from Arrow format'				,							):
																																		written += num_rows

													return written






