
import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        __lowercase		(       a_				,							a_				,							unittest.TestCase							):
					__magic_name__    :				List[str]  						=						StableDiffusionXLImgaImgPipeline
					__magic_name__    :				str  						=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
					__magic_name__    :				Optional[Any]  						=						PipelineTesterMixin.required_optional_params - {'''latents'''}
					__magic_name__    :				Dict  						=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					__magic_name__    :				Optional[int]  						=						IMAGE_TO_IMAGE_IMAGE_PARAMS
					__magic_name__    :				str  						=						IMAGE_TO_IMAGE_IMAGE_PARAMS


					def 							lowerCAmelCase_     (						self    )		->				Dict:





									'''simple docstring'''
									torch.manual_seed(0    )
									A_													=	UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)							,							layers_per_block=2							,							sample_size=3_2							,							in_channels=4							,							out_channels=4							,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,							attention_head_dim=(2, 4)							,							use_linear_projection=lowerCAmelCase_							,							addition_embed_type='''text_time'''							,							addition_time_embed_dim=8							,							transformer_layers_per_block=(1, 2)							,							projection_class_embeddings_input_dim=8_0							,							cross_attention_dim=6_4							,							)
									A_													=	EulerDiscreteScheduler(
									    beta_start=0.0_00_85							,							beta_end=0.0_12							,							steps_offset=1							,							beta_schedule='''scaled_linear'''							,							timestep_spacing='''leading'''							,							)
									torch.manual_seed(0    )
									A_													=	AutoencoderKL(
									    block_out_channels=[3_2, 6_4]							,							in_channels=3							,							out_channels=3							,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,							latent_channels=4							,							sample_size=1_2_8							,							)
									torch.manual_seed(0    )
									A_													=	CLIPTextConfig(
									    bos_token_id=0							,							eos_token_id=2							,							hidden_size=3_2							,							intermediate_size=3_7							,							layer_norm_eps=1E-05							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=1_0_0_0							,							hidden_act='''gelu'''							,							projection_dim=3_2							,							)
									A_													=	CLIPTextModel(lowerCAmelCase_    )
									A_													=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,							local_files_only=lowerCAmelCase_    )

									A_													=	CLIPTextModelWithProjection(lowerCAmelCase_    )
									A_													=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							,							local_files_only=lowerCAmelCase_    )

									A_													=	{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''text_encoder_2''': text_encoder_a,
									    '''tokenizer_2''': tokenizer_a,
									    # "safety_checker": None,
									    # "feature_extractor": None,
									}
									return components


					def 							lowerCAmelCase_     (						self							,							a__							,							a__=0    )		->				Optional[Any]:





									'''simple docstring'''
									A_													=	floats_tensor((1, 3, 3_2, 3_2)							,							rng=random.Random(lowerCAmelCase_    )    ).to(lowerCAmelCase_    )
									A_													=	image / 2 + 0.5
									if str(lowerCAmelCase_    ).startswith('''mps'''    ):
													A_													=	torch.manual_seed(lowerCAmelCase_    )
									else:
													A_													=	torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )
									A_													=	{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 5.0,
									    '''output_type''': '''numpy''',
									    '''strength''': 0.75,
									}
									return inputs


					def 							lowerCAmelCase_     (						self    )		->				Dict:





									'''simple docstring'''
									A_													=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									A_													=	self.get_dummy_components()
									A_													=	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_    )
									A_													=	sd_pipe.to(lowerCAmelCase_    )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									A_													=	self.get_dummy_inputs(lowerCAmelCase_    )
									A_													=	sd_pipe(**lowerCAmelCase_    ).images
									A_													=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 3_2, 3_2, 3)

									A_													=	np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2


					def 							lowerCAmelCase_     (						self    )		->				str:





									'''simple docstring'''
									super().test_attention_slicing_forward_pass(expected_max_diff=3E-3    )


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





									'''simple docstring'''
									super().test_inference_batch_single_identical(expected_max_diff=3E-3    )


					def 							lowerCAmelCase_     (						self    )		->				Dict:





									'''simple docstring'''
									pass


					def 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									A_													=	self.get_dummy_components()
									A_													=	StableDiffusionXLImgaImgPipeline(**lowerCAmelCase_    )
									A_													=	sd_pipe.to(lowerCAmelCase_    )
									A_													=	sd_pipe.to(lowerCAmelCase_    )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									# forward without prompt embeds
									A_													=	self.get_dummy_inputs(lowerCAmelCase_    )
									A_													=	3 * ['''this is a negative prompt''']
									A_													=	negative_prompt
									A_													=	3 * [inputs['''prompt''']]

									A_													=	sd_pipe(**lowerCAmelCase_    )
									A_													=	output.images[0, -3:, -3:, -1]

									# forward with prompt embeds
									A_													=	self.get_dummy_inputs(lowerCAmelCase_    )
									A_													=	3 * ['''this is a negative prompt''']
									A_													=	3 * [inputs.pop('''prompt'''    )]

									(
									    (
									    A_	
									)						,       (
									    A_	
									)						,       (
									    A_	
									)						,       (
									    A_	
									)						,       	
									)													=	sd_pipe.encode_prompt(lowerCAmelCase_							,							negative_prompt=lowerCAmelCase_    )

									A_													=	sd_pipe(
									    **lowerCAmelCase_							,							prompt_embeds=lowerCAmelCase_							,							negative_prompt_embeds=lowerCAmelCase_							,							pooled_prompt_embeds=lowerCAmelCase_							,							negative_pooled_prompt_embeds=lowerCAmelCase_							,							)
									A_													=	output.images[0, -3:, -3:, -1]

									# make sure that it's equal
									assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1E-4






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


					def 							lowerCAmelCase_     (						self    )		->				Any:





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


					def 							lowerCAmelCase_     (						self							,							a__							,							a__="cpu"							,							a__=torch.floataa							,							a__=0    )		->				str:





									'''simple docstring'''
									A_													=	torch.Generator(device=lowerCAmelCase_    ).manual_seed(lowerCAmelCase_    )
									A_													=	np.random.RandomState(lowerCAmelCase_    ).standard_normal((1, 4, 6_4, 6_4)    )
									A_													=	torch.from_numpy(lowerCAmelCase_    ).to(device=lowerCAmelCase_							,							dtype=lowerCAmelCase_    )
									A_													=	{
									    '''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 							lowerCAmelCase_     (						self    )		->				Tuple:





									'''simple docstring'''
									A_													=	DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''    )
									pipe.to(lowerCAmelCase_    )
									pipe.set_progress_bar_config(disable=lowerCAmelCase_    )

									A_													=	self.get_inputs(lowerCAmelCase_    )
									A_													=	pipe(**lowerCAmelCase_    ).images
									A_													=	image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									A_													=	np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]    )
									assert np.abs(image_slice - expected_slice    ).max() < 7E-3



'''simple docstring'''





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str = "" ):






			"""simple docstring"""




			__A=	url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
			__A=	BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text,'html.parser' )
			__A=	soup.find_all('td',attrs='titleColumn' )
			__A=	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" ):






			"""simple docstring"""




			__A=	get_imdb_top_aaa_movies()
			with open(_SCREAMING_SNAKE_CASE,'w',newline='' ) as out_file:
						__A=	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()



