



'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class 							lowercase__	(							unittest.TestCase   ):
	lowercase__  				=			StableDiffusionLDMaDPipeline
	lowercase__  				=			TEXT_TO_IMAGE_PARAMS
	lowercase__  				=			TEXT_TO_IMAGE_BATCH_PARAMS
	lowercase__  				=			TEXT_TO_IMAGE_IMAGE_PARAMS





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							torch.manual_seed(0 )
							_UpperCamelCase					:		str 				=   UNetaDConditionModel(
							    block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,cross_attention_dim=32 ,)
							_UpperCamelCase					:		List[str] 				=   DDIMScheduler(
							    beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,beta_schedule='scaled_linear' ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
							torch.manual_seed(0 )
							_UpperCamelCase					:		Dict 				=   AutoencoderKL(
							    block_out_channels=[32, 64] ,in_channels=6 ,out_channels=6 ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,latent_channels=4 ,)
							torch.manual_seed(0 )
							_UpperCamelCase					:		List[Any] 				=   CLIPTextConfig(
							    bos_token_id=0 ,eos_token_id=2 ,hidden_size=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=1000 ,)
							_UpperCamelCase					:		Tuple 				=   CLIPTextModel(lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

							_UpperCamelCase					:		Dict 				=   {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components





	def 	UpperCamelCase_  ( self					:				List[str] ,lowerCamelCase__					:				List[str] ,lowerCamelCase__					:				Tuple=0 ):







							'''simple docstring'''
							if str(lowerCamelCase__ ).startswith('mps' ):
													_UpperCamelCase					:		Optional[int] 				=   torch.manual_seed(lowerCamelCase__ )
							else:
													_UpperCamelCase					:		List[Any] 				=   torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}
							return inputs





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   'cpu'  # ensure determinism for the device-dependent torch.Generator

							_UpperCamelCase					:		List[str] 				=   self.get_dummy_components()
							_UpperCamelCase					:		Optional[int] 				=   StableDiffusionLDMaDPipeline(**lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   ldmad_pipe.to(lowerCamelCase__ )
							ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		str 				=   self.get_dummy_inputs(lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   ldmad_pipe(**lowerCamelCase__ )
							_UpperCamelCase				,			_UpperCamelCase					:		Tuple 				=   output.rgb, output.depth

							_UpperCamelCase					:		Optional[int] 				=   rgb[0, -3:, -3:, -1]
							_UpperCamelCase					:		Union[str, Any] 				=   depth[0, -3:, -1]

							assert rgb.shape == (1, 64, 64, 3)
							assert depth.shape == (1, 64, 64)

							_UpperCamelCase					:		str 				=   np.array(
							    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
							_UpperCamelCase					:		Dict 				=   np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

							assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
							assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   self.get_dummy_components()
							_UpperCamelCase					:		str 				=   StableDiffusionLDMaDPipeline(**lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   ldmad_pipe.to(lowerCamelCase__ )
							ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Optional[Any] 				=   self.get_dummy_inputs(lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   3 * [inputs['prompt']]

							# forward
							_UpperCamelCase					:		int 				=   ldmad_pipe(**lowerCamelCase__ )
							_UpperCamelCase				,			_UpperCamelCase					:		Dict 				=   output.rgb, output.depth
							_UpperCamelCase					:		Optional[Any] 				=   rgb_slice_a[0, -3:, -3:, -1]
							_UpperCamelCase					:		List[str] 				=   depth_slice_a[0, -3:, -1]

							_UpperCamelCase					:		Optional[int] 				=   self.get_dummy_inputs(lowerCamelCase__ )
							_UpperCamelCase					:		str 				=   3 * [inputs.pop('prompt' )]

							_UpperCamelCase					:		str 				=   ldmad_pipe.tokenizer(
							    lowerCamelCase__ ,padding='max_length' ,max_length=ldmad_pipe.tokenizer.model_max_length ,truncation=lowerCamelCase__ ,return_tensors='pt' ,)
							_UpperCamelCase					:		str 				=   text_inputs['input_ids'].to(lowerCamelCase__ )

							_UpperCamelCase					:		Optional[Any] 				=   ldmad_pipe.text_encoder(lowerCamelCase__ )[0]

							_UpperCamelCase					:		Union[str, Any] 				=   prompt_embeds

							# forward
							_UpperCamelCase					:		Union[str, Any] 				=   ldmad_pipe(**lowerCamelCase__ )
							_UpperCamelCase				,			_UpperCamelCase					:		Optional[int] 				=   output.rgb, output.depth
							_UpperCamelCase					:		Union[str, Any] 				=   rgb_slice_a[0, -3:, -3:, -1]
							_UpperCamelCase					:		Union[str, Any] 				=   depth_slice_a[0, -3:, -1]

							assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
							assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   'cpu'  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase					:		Dict 				=   self.get_dummy_components()
							_UpperCamelCase					:		Optional[int] 				=   PNDMScheduler(skip_prk_steps=lowerCamelCase__ )
							_UpperCamelCase					:		Dict 				=   StableDiffusionLDMaDPipeline(**lowerCamelCase__ )
							_UpperCamelCase					:		Dict 				=   ldmad_pipe.to(lowerCamelCase__ )
							ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Optional[Any] 				=   self.get_dummy_inputs(lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   'french fries'
							_UpperCamelCase					:		str 				=   ldmad_pipe(**lowerCamelCase__ ,negative_prompt=lowerCamelCase__ )

							_UpperCamelCase				,			_UpperCamelCase					:		str 				=   output.rgb, output.depth
							_UpperCamelCase					:		Optional[int] 				=   rgb[0, -3:, -3:, -1]
							_UpperCamelCase					:		Union[str, Any] 				=   depth[0, -3:, -1]

							assert rgb.shape == (1, 64, 64, 3)
							assert depth.shape == (1, 64, 64)

							_UpperCamelCase					:		List[str] 				=   np.array(
							    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
							_UpperCamelCase					:		Any 				=   np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
							assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
							assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2






@slow
@require_torch_gpu
class 							lowercase__	(							unittest.TestCase   ):





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def 	UpperCamelCase_  ( self					:				Dict ,lowerCamelCase__					:				Any ,lowerCamelCase__					:				List[str]="cpu" ,lowerCamelCase__					:				int=torch.floataa ,lowerCamelCase__					:				Optional[Any]=0 ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
							_UpperCamelCase					:		Tuple 				=   np.random.RandomState(lowerCamelCase__ ).standard_normal((1, 4, 64, 64) )
							_UpperCamelCase					:		int 				=   torch.from_numpy(lowerCamelCase__ ).to(device=lowerCamelCase__ ,dtype=lowerCamelCase__ )
							_UpperCamelCase					:		Tuple 				=   {
							    'prompt': 'a photograph of an astronaut riding a horse',
							    'latents': latents,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' )
							_UpperCamelCase					:		str 				=   ldmad_pipe.to(lowerCamelCase__ )
							ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		List[Any] 				=   self.get_inputs(lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   ldmad_pipe(**lowerCamelCase__ )
							_UpperCamelCase				,			_UpperCamelCase					:		Optional[int] 				=   output.rgb, output.depth
							_UpperCamelCase					:		List[Any] 				=   rgb[0, -3:, -3:, -1].flatten()
							_UpperCamelCase					:		Optional[Any] 				=   rgb[0, -3:, -1].flatten()

							assert rgb.shape == (1, 512, 512, 3)
							assert depth.shape == (1, 512, 512)

							_UpperCamelCase					:		Optional[int] 				=   np.array(
							    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
							_UpperCamelCase					:		Optional[Any] 				=   np.array(
							    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
							assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
							assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3






@nightly
@require_torch_gpu
class 							lowercase__	(							unittest.TestCase   ):





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				str ,lowerCamelCase__					:				Dict="cpu" ,lowerCamelCase__					:				List[Any]=torch.floataa ,lowerCamelCase__					:				Tuple=0 ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
							_UpperCamelCase					:		List[str] 				=   np.random.RandomState(lowerCamelCase__ ).standard_normal((1, 4, 64, 64) )
							_UpperCamelCase					:		Optional[int] 				=   torch.from_numpy(lowerCamelCase__ ).to(device=lowerCamelCase__ ,dtype=lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   {
							    'prompt': 'a photograph of an astronaut riding a horse',
							    'latents': latents,
							    'generator': generator,
							    'num_inference_steps': 50,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' ).to(lowerCamelCase__ )
							ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   self.get_inputs(lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   ldmad_pipe(**lowerCamelCase__ )
							_UpperCamelCase				,			_UpperCamelCase					:		Dict 				=   output.rgb, output.depth

							_UpperCamelCase					:		Optional[int] 				=   0.4_9_5_5_8_6
							_UpperCamelCase					:		Optional[Any] 				=   0.3_3_7_9_5_5_1_5
							_UpperCamelCase					:		Dict 				=   1_1_2.4_8_5_1_8
							_UpperCamelCase					:		Optional[int] 				=   9_8.4_8_9_7_4_6
							assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
							assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
							assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
							assert np.abs(expected_depth_std - depth.std() ) < 1E-3





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c' ).to(lowerCamelCase__ )
							ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Optional[Any] 				=   self.get_inputs(lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   ldmad_pipe(**lowerCamelCase__ )
							_UpperCamelCase				,			_UpperCamelCase					:		List[Any] 				=   output.rgb, output.depth

							_UpperCamelCase					:		int 				=   0.4_1_9_4_1_2_7
							_UpperCamelCase					:		List[Any] 				=   0.3_5_3_7_5_5_8_6
							_UpperCamelCase					:		int 				=   0.5_6_3_8_5_0_2
							_UpperCamelCase					:		Tuple 				=   0.3_4_6_8_6_1_0_3

							assert rgb.shape == (1, 512, 512, 3)
							assert depth.shape == (1, 512, 512, 1)
							assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
							assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
							assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
							assert np.abs(expected_depth_std - depth.std() ) < 1E-3






from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str = ""				):
     __UpperCamelCase    							=url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
     __UpperCamelCase    							=BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__				).text			,     'html.parser'				)
     __UpperCamelCase    							=soup.find_all('td'			,     attrs='titleColumn'				)
     __UpperCamelCase    							=soup.find_all('td'			,     class_='ratingColumn imdbRating'				)
     return {
         title.a.text: float(rating.strong.text				)
         for title, rating in zip(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     }

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str = "IMDb_Top_250_Movies.csv"				):
     __UpperCamelCase    							=get_imdb_top_aaa_movies()
     with open(SCREAMING_SNAKE_CASE__			,     'w'			,     newline=''				) as out_file:
          __UpperCamelCase    							=csv.writer(SCREAMING_SNAKE_CASE__				)
          writer.writerow(['Movie title', 'IMDb rating']				)
          for title, rating in movies.items():
               writer.writerow([title, rating]				)


if __name__ == "__main__":
   write_movies()





