




"""simple docstring"""

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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):
				__lowerCAmelCase			:			Tuple     				=      CycleDiffusionPipeline
				__lowerCAmelCase			:			Tuple     				=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    '''negative_prompt''',
				    '''height''',
				    '''width''',
				    '''negative_prompt_embeds''',
				}
				__lowerCAmelCase			:			Optional[int]     				=      PipelineTesterMixin.required_optional_params - {'''latents'''}
				__lowerCAmelCase			:			Union[str, Any]     				=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""})
				__lowerCAmelCase			:			str     				=      IMAGE_TO_IMAGE_IMAGE_PARAMS
				__lowerCAmelCase			:			Optional[int]     				=      IMAGE_TO_IMAGE_IMAGE_PARAMS







				def     a_     (							self      :    List[str]				):



					"""simple docstring"""

					torch.manual_seed(0				)
					A_     : Union[str, Any]		      =						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					,   )
					A_     : List[str]		      =						DDIMScheduler(
					    beta_start=0.00085					,   beta_end=0.012					,   beta_schedule='''scaled_linear'''					,   num_train_timesteps=10_00					,   clip_sample=__a					,   set_alpha_to_one=__a					,   )
					torch.manual_seed(0				)
					A_     : List[Any]		      =						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					,   )
					torch.manual_seed(0				)
					A_     : Optional[int]		      =						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=10_00					,   )
					A_     : Any		      =						CLIPTextModel(__a				)
					A_     : str		      =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

					A_     : str		      =						{
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """vae""": vae,
					    """text_encoder""": text_encoder,
					    """tokenizer""": tokenizer,
					    """safety_checker""": None,
					    """feature_extractor""": None,
					}
					return components







				def     a_     (							self      :    str					,   _lowerCamelCase      :    Any					,   _lowerCamelCase      :    Optional[int]=0				):



					"""simple docstring"""

					A_     : str		      =						floats_tensor((1, 3, 32, 32)					,   rng=random.Random(__a				)				).to(__a				)
					A_     : str		      =						image / 2 + 0.5
					if str(__a				).startswith('''mps'''				):
						A_     : Optional[int]		      =						torch.manual_seed(__a				)
					else:
						A_     : List[Any]		      =						torch.Generator(device=__a				).manual_seed(__a				)
					A_     : List[Any]		      =						{
					    """prompt""": """An astronaut riding an elephant""",
					    """source_prompt""": """An astronaut riding a horse""",
					    """image""": image,
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """eta""": 0.1,
					    """strength""": 0.8,
					    """guidance_scale""": 3,
					    """source_guidance_scale""": 1,
					    """output_type""": """numpy""",
					}
					return inputs







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



					"""simple docstring"""

					A_     : Union[str, Any]		      =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

					A_     : int		      =						self.get_dummy_components()
					A_     : Optional[Any]		      =						CycleDiffusionPipeline(**__a				)
					A_     : List[Any]		      =						pipe.to(__a				)
					pipe.set_progress_bar_config(disable=__a				)

					A_     : List[str]		      =						self.get_dummy_inputs(__a				)
					A_     : Optional[Any]		      =						pipe(**__a				)
					A_     : int		      =						output.images

					A_     : str		      =						images[0, -3:, -3:, -1]

					assert images.shape == (1, 32, 32, 3)
					A_     : List[Any]		      =						np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]				)

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







				@unittest.skipIf(torch_device != '''cuda'''					,   '''This test requires a GPU'''				)
				def     a_     (							self      :    int				):



					"""simple docstring"""

					A_     : Union[str, Any]		      =						self.get_dummy_components()
					for name, module in components.items():
						if hasattr(__a					,   '''half'''				):
							A_     : Any		      =						module.half()
					A_     : List[str]		      =						CycleDiffusionPipeline(**__a				)
					A_     : Tuple		      =						pipe.to(__a				)
					pipe.set_progress_bar_config(disable=__a				)

					A_     : Optional[int]		      =						self.get_dummy_inputs(__a				)
					A_     : List[Any]		      =						pipe(**__a				)
					A_     : Union[str, Any]		      =						output.images

					A_     : Union[str, Any]		      =						images[0, -3:, -3:, -1]

					assert images.shape == (1, 32, 32, 3)
					A_     : Optional[int]		      =						np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]				)

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







				@skip_mps
				def     a_     (							self      :    List[str]				):



					"""simple docstring"""

					return super().test_save_load_local()







				@unittest.skip('''non-deterministic pipeline'''				)
				def     a_     (							self      :    List[str]				):



					"""simple docstring"""

					return super().test_inference_batch_single_identical()







				@skip_mps
				def     a_     (							self      :    List[str]				):



					"""simple docstring"""

					return super().test_dict_tuple_outputs_equivalent()







				@skip_mps
				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

					return super().test_save_load_optional_components()







				@skip_mps
				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

					return super().test_attention_slicing_forward_pass()

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







				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

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







				def     a_     (							self      :    Any				):



					"""simple docstring"""

					A_     : List[str]		      =						load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/cycle-diffusion/black_colored_car.png'''				)
					A_     : Dict		      =						load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
					A_     : Optional[int]		      =						init_image.resize((5_12, 5_12)				)

					A_     : List[str]		      =						"""CompVis/stable-diffusion-v1-4"""
					A_     : int		      =						DDIMScheduler.from_pretrained(__a					,   subfolder='''scheduler'''				)
					A_     : List[str]		      =						CycleDiffusionPipeline.from_pretrained(
					    __a					,   scheduler=__a					,   safety_checker=__a					,   torch_dtype=torch.floataa					,   revision='''fp16'''				)

					pipe.to(__a				)
					pipe.set_progress_bar_config(disable=__a				)
					pipe.enable_attention_slicing()

					A_     : Dict		      =						"""A black colored car"""
					A_     : Optional[Any]		      =						"""A blue colored car"""

					A_     : List[str]		      =						torch.manual_seed(0				)
					A_     : List[Any]		      =						pipe(
					    prompt=__a					,   source_prompt=__a					,   image=__a					,   num_inference_steps=1_00					,   eta=0.1					,   strength=0.85					,   guidance_scale=3					,   source_guidance_scale=1					,   generator=__a					,   output_type='''np'''					,   )
					A_     : Tuple		      =						output.images

					# the values aren't exactly equal, but the images look the same visually
					assert np.abs(image - expected_image				).max() < 5E-1







				def     a_     (							self      :    Optional[Any]				):



					"""simple docstring"""

					A_     : Optional[int]		      =						load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/cycle-diffusion/black_colored_car.png'''				)
					A_     : Optional[int]		      =						load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
					A_     : Optional[int]		      =						init_image.resize((5_12, 5_12)				)

					A_     : Optional[Any]		      =						"""CompVis/stable-diffusion-v1-4"""
					A_     : Dict		      =						DDIMScheduler.from_pretrained(__a					,   subfolder='''scheduler'''				)
					A_     : Optional[int]		      =						CycleDiffusionPipeline.from_pretrained(__a					,   scheduler=__a					,   safety_checker=__a				)

					pipe.to(__a				)
					pipe.set_progress_bar_config(disable=__a				)
					pipe.enable_attention_slicing()

					A_     : Tuple		      =						"""A black colored car"""
					A_     : Any		      =						"""A blue colored car"""

					A_     : List[str]		      =						torch.manual_seed(0				)
					A_     : List[str]		      =						pipe(
					    prompt=__a					,   source_prompt=__a					,   image=__a					,   num_inference_steps=1_00					,   eta=0.1					,   strength=0.85					,   guidance_scale=3					,   source_guidance_scale=1					,   generator=__a					,   output_type='''np'''					,   )
					A_     : Optional[Any]		      =						output.images

					assert np.abs(image - expected_image				).max() < 2E-2













import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def 					snake_case_						(   lowerCAmelCase_				:  int = 8					):
							__lowercase					:							str			=   ascii_letters + digits + punctuation
							return "".join(secrets.choice(lowerCAmelCase_					) for _ in range(lowerCAmelCase_					)					)
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int					):
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   i // 3
							__lowercase					:							int			=   i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							__lowercase					:							str			=   (
							    chars_incl
							    + random(lowerCAmelCase_				,				quotient + remainder					)
							    + random(lowerCAmelCase_				,				lowerCAmelCase_					)
							    + random(lowerCAmelCase_				,				lowerCAmelCase_					)
							)
							__lowercase					:							int			=   list(lowerCAmelCase_					)
							shuffle(lowerCAmelCase_					)
							return "".join(lowerCAmelCase_					)

							# random is a generalised function for letters, characters and numbers
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int					):
							return "".join(secrets.choice(lowerCAmelCase_					) for _ in range(lowerCAmelCase_					)					)
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  int					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  Union[str, Any]				,				lowerCAmelCase_				:  List[Any]					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Optional[int]					):
							pass  # Put your code here...
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  int = 8					):

							if len(lowerCAmelCase_					) < min_length:
														# Your Password must be at least 8 characters long
														return False

							__lowercase					:							Tuple			=   any(char in ascii_uppercase for char in password					)
							__lowercase					:							Union[str, Any]			=   any(char in ascii_lowercase for char in password					)
							__lowercase					:							Dict			=   any(char in digits for char in password					)
							__lowercase					:							Tuple			=   any(char in punctuation for char in password					)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters
def 					snake_case_						(   ):
							__lowercase					:							Union[str, Any]			=   int(input("""Please indicate the max length of your password: """					).strip()					)
							__lowercase					:							List[str]			=   input(
							    """Please indicate the characters that must be in your password: """					).strip()
							print("""Password generated:"""				,				password_generator(lowerCAmelCase_					)					)
							print(
							    """Alternative Password generated:"""				,				alternative_password_generator(lowerCAmelCase_				,				lowerCAmelCase_					)				,				)
							print("""[If you are thinking of using this passsword, You better save it.]"""					)


if __name__ == "__main__":
	main()