






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 		_snake_case  (  A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    Optional[int]			     =							StableDiffusionXLImgaImgPipeline
			_lowercase		:    Union[str, Any]			     =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
			_lowercase		:    str			     =							PipelineTesterMixin.required_optional_params - {'''latents'''}
			_lowercase		:    Union[str, Any]			     =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			_lowercase		:    Tuple			     =							IMAGE_TO_IMAGE_IMAGE_PARAMS
			_lowercase		:    Tuple			     =							IMAGE_TO_IMAGE_IMAGE_PARAMS






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= 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')		,					attention_head_dim=(2, 4)		,					use_linear_projection=a		,					addition_embed_type='text_time'		,					addition_time_embed_dim=8		,					transformer_layers_per_block=(1, 2)		,					projection_class_embeddings_input_dim=80		,					cross_attention_dim=64		,					)
						SCREAMING_SNAKE_CASE						= 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)
						SCREAMING_SNAKE_CASE						= AutoencoderKL(
						    block_out_channels=[32, 64]		,					in_channels=3		,					out_channels=3		,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,					latent_channels=4		,					sample_size=128		,					)
						torch.manual_seed(0)
						SCREAMING_SNAKE_CASE						= 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		,					hidden_act='gelu'		,					projection_dim=32		,					)
						SCREAMING_SNAKE_CASE						= CLIPTextModel(a)
						SCREAMING_SNAKE_CASE						= CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		,					local_files_only=a)

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

						SCREAMING_SNAKE_CASE						= {
						    '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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=0)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= floats_tensor((1, 3, 32, 32)		,					rng=random.Random(a)).to(a)
						SCREAMING_SNAKE_CASE						= image / 2 + 0.5
						if str(a).startswith('mps'):
									SCREAMING_SNAKE_CASE						= torch.manual_seed(a)
						else:
									SCREAMING_SNAKE_CASE						= torch.Generator(device=a).manual_seed(a)
						SCREAMING_SNAKE_CASE						= {
						    '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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= 'cpu'  # ensure determinism for the device-dependent torch.Generator
						SCREAMING_SNAKE_CASE						= self.get_dummy_components()
						SCREAMING_SNAKE_CASE						= StableDiffusionXLImgaImgPipeline(**a)
						SCREAMING_SNAKE_CASE						= sd_pipe.to(a)
						sd_pipe.set_progress_bar_config(disable=a)

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

						assert image.shape == (1, 32, 32, 3)

						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						super().test_attention_slicing_forward_pass(expected_max_diff=3E-3)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						super().test_inference_batch_single_identical(expected_max_diff=3E-3)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						pass







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.get_dummy_components()
						SCREAMING_SNAKE_CASE						= StableDiffusionXLImgaImgPipeline(**a)
						SCREAMING_SNAKE_CASE						= sd_pipe.to(a)
						SCREAMING_SNAKE_CASE						= sd_pipe.to(a)
						sd_pipe.set_progress_bar_config(disable=a)

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

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

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

						(
						    (
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						
						)						= sd_pipe.encode_prompt(a		,					negative_prompt=a)

						SCREAMING_SNAKE_CASE						= sd_pipe(
						    **a		,					prompt_embeds=a		,					negative_prompt_embeds=a		,					pooled_prompt_embeds=a		,					negative_pooled_prompt_embeds=a		,					)
						SCREAMING_SNAKE_CASE						= 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 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a="cpu"		,					a=torch.floataa		,					a=0)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= torch.Generator(device=a).manual_seed(a)
						SCREAMING_SNAKE_CASE						= np.random.RandomState(a).standard_normal((1, 4, 64, 64))
						SCREAMING_SNAKE_CASE						= torch.from_numpy(a).to(device=a		,					dtype=a)
						SCREAMING_SNAKE_CASE						= {
						    '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    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base')
						pipe.to(a)
						pipe.set_progress_bar_config(disable=a)

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

						assert image.shape == (1, 512, 512, 3)
						SCREAMING_SNAKE_CASE						= 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












from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

a_		:						str             =    (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_		:						list[int]             =    [ord(letter) for letter in string.ascii_lowercase]
a_		:						set[int]             =    {ord(char) for char in VALID_CHARS}

a_		:						list[str]             =    ["the", "be", "to", "of", "and", "in", "that", "have"]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= ""
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42

			for keychar, cipherchar in zip(cycle(_UpperCAmelCase)							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(_UpperCAmelCase)

			return decoded






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= []
			for key in product(_UpperCAmelCase							,       repeat=3):
						SCREAMING_SNAKE_CASE						= try_key(_UpperCAmelCase							,       _UpperCAmelCase)
						if encoded is not None:
									possibles.append(_UpperCAmelCase)
			return possibles






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return [possible for possible in possibles if common_word in possible.lower()]






def 				lowerCamelCase__				(_UpperCAmelCase = "p059_cipher.txt"):
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= Path(_UpperCAmelCase).parent.joinpath(_UpperCAmelCase).read_text(encoding='utf-8')

			SCREAMING_SNAKE_CASE						= [int(_UpperCAmelCase) for number in data.strip().split(',')]

			SCREAMING_SNAKE_CASE						= filter_valid_chars(_UpperCAmelCase)
			for common_word in COMMON_WORDS:
						SCREAMING_SNAKE_CASE						= filter_common_word(_UpperCAmelCase							,       _UpperCAmelCase)
						if len(_UpperCAmelCase) == 1:
									break

			SCREAMING_SNAKE_CASE						= possibles[0]
			return sum(ord(_UpperCAmelCase) for char in decoded_text)


if __name__ == "__main__":
				print(f"""{solution() = }""")




