


'''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      lowercase			(							_UpperCAmelCase			,	_UpperCAmelCase			,	unittest.TestCase      ):

		"""simple docstring"""

		_a     :   Union[str, Any]									=       AutoencoderKL
		_a     :   Optional[int]									=       """sample"""
		_a     :   Optional[int]									=       1E-2




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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

							UpperCamelCase__     :Union[str, Any]							      =				floats_tensor((batch_size, num_channels) + sizes							).to(lowercase__							)

							return {"sample": image}




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return (3, 32, 32)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return (3, 32, 32)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				{
							    """block_out_channels""": [32, 64],
							    """in_channels""": 3,
							    """out_channels""": 3,
							    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
							    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
							    """latent_channels""": 4,
							}
							UpperCamelCase__     :Optional[int]							      =				self.dummy_input
							return init_dict, inputs_dict




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.prepare_init_args_and_inputs_for_common()
							UpperCamelCase__     :Optional[Any]							      =				self.model_class(**lowercase__							)
							model.to(lowercase__							)

							assert not model.is_gradient_checkpointing and model.training

							UpperCamelCase__     :Optional[Any]							      =				model(**lowercase__							).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()

							UpperCamelCase__     :Any							      =				torch.randn_like(lowercase__							)
							UpperCamelCase__     :Dict							      =				(out - labels).mean()
							loss.backward()

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

							assert model_a.is_gradient_checkpointing and model_a.training

							UpperCamelCase__     :List[Any]							      =				model_a(**lowercase__							).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()
							UpperCamelCase__     :Any							      =				(out_a - labels).mean()
							loss_a.backward()

							# compare the output and parameters gradients
							self.assertTrue((loss - loss_a).abs() < 1e-5							)
							UpperCamelCase__     :Any							      =				dict(model.named_parameters()							)
							UpperCamelCase__     :List[Any]							      =				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							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''					,     output_loading_info=lowercase__							)
							self.assertIsNotNone(lowercase__							)
							self.assertEqual(len(loading_info['''missing_keys''']							)					,     0							)

							model.to(lowercase__							)
							UpperCamelCase__     :List[Any]							      =				model(**self.dummy_input							)

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''							)
							UpperCamelCase__     :Any							      =				model.to(lowercase__							)
							model.eval()

							if torch_device == "mps":
												UpperCamelCase__     :List[str]							      =				torch.manual_seed(0							)
							else:
												UpperCamelCase__     :List[Any]							      =				torch.Generator(device=lowercase__							).manual_seed(0							)

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

							UpperCamelCase__     :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":
												UpperCamelCase__     :str							      =				torch.tensor(
												    [
												        -4.00_78e-01,
												        -3.83_23e-04,
												        -1.26_81e-01,
												        -1.14_62e-01,
												        2.00_95e-01,
												        1.08_93e-01,
												        -8.82_47e-02,
												        -3.03_61e-01,
												        -9.86_44e-03,
												    ]							)
							elif torch_device == "cpu":
												UpperCamelCase__     :int							      =				torch.tensor(
												    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]							)
							else:
												UpperCamelCase__     :Optional[Any]							      =				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(lowercase__					,     lowercase__					,     rtol=1e-2							)							)



@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




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

							'''simple docstring'''


							UpperCamelCase__     :int							      =				torch.floataa if fpaa else torch.floataa
							UpperCamelCase__     :Dict							      =				torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase__					,     lowercase__							)							)							).to(lowercase__							).to(lowercase__							)
							return image




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

							'''simple docstring'''


							UpperCamelCase__     :str							      =				"""fp16""" if fpaa else None
							UpperCamelCase__     :Dict							      =				torch.floataa if fpaa else torch.floataa

							UpperCamelCase__     :Optional[Any]							      =				AutoencoderKL.from_pretrained(
							    lowercase__					,     subfolder='''vae'''					,     torch_dtype=lowercase__					,     revision=lowercase__					,     )
							model.to(lowercase__							).eval()

							return model




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0							):

							'''simple docstring'''


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




		@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					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.get_sd_vae_model()
							UpperCamelCase__     :int							      =				self.get_sd_image(lowercase__							)
							UpperCamelCase__     :Optional[Any]							      =				self.get_generator(lowercase__							)

							with torch.no_grad():
												UpperCamelCase__     :Any							      =				model(lowercase__					,     generator=lowercase__					,     sample_posterior=lowercase__							).sample

							assert sample.shape == image.shape

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

							assert torch_all_close(lowercase__					,     lowercase__					,     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					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self.get_sd_vae_model(fpaa=lowercase__							)
							UpperCamelCase__     :List[str]							      =				self.get_sd_image(lowercase__					,     fpaa=lowercase__							)
							UpperCamelCase__     :Dict							      =				self.get_generator(lowercase__							)

							with torch.no_grad():
												UpperCamelCase__     :Optional[Any]							      =				model(lowercase__					,     generator=lowercase__					,     sample_posterior=lowercase__							).sample

							assert sample.shape == image.shape

							UpperCamelCase__     :List[str]							      =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
							UpperCamelCase__     :Tuple							      =				torch.tensor(lowercase__							)

							assert torch_all_close(lowercase__					,     lowercase__					,     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					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.get_sd_vae_model()
							UpperCamelCase__     :Tuple							      =				self.get_sd_image(lowercase__							)

							with torch.no_grad():
												UpperCamelCase__     :Dict							      =				model(lowercase__							).sample

							assert sample.shape == image.shape

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

							assert torch_all_close(lowercase__					,     lowercase__					,     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					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				self.get_sd_vae_model()
							UpperCamelCase__     :Union[str, Any]							      =				self.get_sd_image(lowercase__					,     shape=(3, 4, 64, 64)							)

							with torch.no_grad():
												UpperCamelCase__     :Dict							      =				model.decode(lowercase__							).sample

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

							UpperCamelCase__     :Any							      =				sample[-1, -2:, :2, -2:].flatten().cpu()
							UpperCamelCase__     :List[Any]							      =				torch.tensor(lowercase__							)

							assert torch_all_close(lowercase__					,     lowercase__					,     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					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				self.get_sd_vae_model(fpaa=lowercase__							)
							UpperCamelCase__     :Union[str, Any]							      =				self.get_sd_image(lowercase__					,     shape=(3, 4, 64, 64)					,     fpaa=lowercase__							)

							with torch.no_grad():
												UpperCamelCase__     :List[Any]							      =				model.decode(lowercase__							).sample

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

							UpperCamelCase__     :Tuple							      =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
							UpperCamelCase__     :Dict							      =				torch.tensor(lowercase__							)

							assert torch_all_close(lowercase__					,     lowercase__					,     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					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				self.get_sd_vae_model(fpaa=lowercase__							)
							UpperCamelCase__     :Optional[int]							      =				self.get_sd_image(lowercase__					,     shape=(3, 4, 64, 64)					,     fpaa=lowercase__							)

							with torch.no_grad():
												UpperCamelCase__     :Any							      =				model.decode(lowercase__							).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
												UpperCamelCase__     :List[Any]							      =				model.decode(lowercase__							).sample

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

							assert torch_all_close(lowercase__					,     lowercase__					,     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					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.get_sd_vae_model()
							UpperCamelCase__     :Any							      =				self.get_sd_image(lowercase__					,     shape=(3, 4, 64, 64)							)

							with torch.no_grad():
												UpperCamelCase__     :List[Any]							      =				model.decode(lowercase__							).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
												UpperCamelCase__     :Optional[Any]							      =				model.decode(lowercase__							).sample

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

							assert torch_all_close(lowercase__					,     lowercase__					,     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					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				self.get_sd_vae_model()
							UpperCamelCase__     :Union[str, Any]							      =				self.get_sd_image(lowercase__							)
							UpperCamelCase__     :int							      =				self.get_generator(lowercase__							)

							with torch.no_grad():
												UpperCamelCase__     :int							      =				model.encode(lowercase__							).latent_dist
												UpperCamelCase__     :Dict							      =				dist.sample(generator=lowercase__							)

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

							UpperCamelCase__     :Any							      =				sample[0, -1, -3:, -3:].flatten().cpu()
							UpperCamelCase__     :int							      =				torch.tensor(lowercase__							)

							UpperCamelCase__     :Tuple							      =				3e-3 if torch_device != """mps""" else 1e-2
							assert torch_all_close(lowercase__					,     lowercase__					,     atol=lowercase__							)



'''simple docstring'''
from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     a   (    __a = ""     )						->			dict[str, float]:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
					UpperCamelCase__     :Optional[int]							      =				BeautifulSoup(requests.get(__a     ).text				,      '''html.parser'''     )
					UpperCamelCase__     :Tuple							      =				soup.find_all('''td'''				,      attrs='''titleColumn'''     )
					UpperCamelCase__     :Union[str, Any]							      =				soup.find_all('''td'''				,      class_='''ratingColumn imdbRating'''     )
					return {
					    title.a.text: float(rating.strong.text     )
					    for title, rating in zip(__a				,      __a     )
					}


def     a   (    __a = "IMDb_Top_250_Movies.csv"     )						->			None:



					'''simple docstring'''






					UpperCamelCase__     :Optional[Any]							      =				get_imdb_top_aaa_movies()
					with open(__a				,      '''w'''				,      newline=''''''     ) as out_file:
										UpperCamelCase__     :int							      =				csv.writer(__a     )
										writer.writerow(['''Movie title''', '''IMDb rating''']     )
										for title, rating in movies.items():
															writer.writerow([title, rating]     )


if __name__ == "__main__":
		write_movies()