"""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     UpperCamelCase__(			__A		,						__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Union[str, Any]													=		CycleDiffusionPipeline
						lowerCAmelCase__  :						Union[str, Any]													=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    'negative_prompt',
						    'height',
						    'width',
						    'negative_prompt_embeds',
						}
						lowerCAmelCase__  :						List[Any]													=		PipelineTesterMixin.required_optional_params - {'latents'}
						lowerCAmelCase__  :						Union[str, Any]													=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'} )
						lowerCAmelCase__  :						List[str]													=		IMAGE_TO_IMAGE_IMAGE_PARAMS
						lowerCAmelCase__  :						int													=		IMAGE_TO_IMAGE_IMAGE_PARAMS





						def 				snake_case__	(       self     )					->				str:
													torch.manual_seed(0     )
													A__	     =							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__	     =							DDIMScheduler(
													    beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,beta_schedule='scaled_linear'		,num_train_timesteps=10_00		,clip_sample=__UpperCAmelCase		,set_alpha_to_one=__UpperCAmelCase		,)
													torch.manual_seed(0     )
													A__	     =							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__	     =							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__	     =							CLIPTextModel(__UpperCAmelCase     )
													A__	     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

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





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=0     )					->				Union[str, Any]:
													A__	     =							floats_tensor((1, 3, 32, 32)		,rng=random.Random(__UpperCAmelCase     )     ).to(__UpperCAmelCase     )
													A__	     =							image / 2 + 0.5
													if str(__UpperCAmelCase     ).startswith('mps'     ):
																				A__	     =							torch.manual_seed(__UpperCAmelCase     )
													else:
																				A__	     =							torch.Generator(device=__UpperCAmelCase     ).manual_seed(__UpperCAmelCase     )
													A__	     =							{
													    '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 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							'cpu'  # ensure determinism for the device-dependent torch.Generator

													A__	     =							self.get_dummy_components()
													A__	     =							CycleDiffusionPipeline(**__UpperCAmelCase     )
													A__	     =							pipe.to(__UpperCAmelCase     )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							self.get_dummy_inputs(__UpperCAmelCase     )
													A__	     =							pipe(**__UpperCAmelCase     )
													A__	     =							output.images

													A__	     =							images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													A__	     =							np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]     )

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





						@unittest.skipIf(torch_device != 'cuda'		,'This test requires a GPU'     )
						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(__UpperCAmelCase		,'half'     ):
																											A__	     =							module.half()
													A__	     =							CycleDiffusionPipeline(**__UpperCAmelCase     )
													A__	     =							pipe.to(__UpperCAmelCase     )
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							self.get_dummy_inputs(__UpperCAmelCase     )
													A__	     =							pipe(**__UpperCAmelCase     )
													A__	     =							output.images

													A__	     =							images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													A__	     =							np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]     )

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





						@skip_mps
						def 				snake_case__	(       self     )					->				List[Any]:
													return super().test_save_load_local()





						@unittest.skip('non-deterministic pipeline'     )
						def 				snake_case__	(       self     )					->				List[str]:
													return super().test_inference_batch_single_identical()





						@skip_mps
						def 				snake_case__	(       self     )					->				List[str]:
													return super().test_dict_tuple_outputs_equivalent()





						@skip_mps
						def 				snake_case__	(       self     )					->				Optional[Any]:
													return super().test_save_load_optional_components()





						@skip_mps
						def 				snake_case__	(       self     )					->				Optional[int]:
													return super().test_attention_slicing_forward_pass()






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





						def 				snake_case__	(       self     )					->				Any:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def 				snake_case__	(       self     )					->				int:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/cycle-diffusion/black_colored_car.png'     )
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'     )
													A__	     =							init_image.resize((5_12, 5_12)     )

													A__	     =							'CompVis/stable-diffusion-v1-4'
													A__	     =							DDIMScheduler.from_pretrained(__UpperCAmelCase		,subfolder='scheduler'     )
													A__	     =							CycleDiffusionPipeline.from_pretrained(
													    __UpperCAmelCase		,scheduler=__UpperCAmelCase		,safety_checker=__UpperCAmelCase		,torch_dtype=torch.floataa		,revision='fp16'     )

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

													A__	     =							'A black colored car'
													A__	     =							'A blue colored car'

													A__	     =							torch.manual_seed(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,source_prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,num_inference_steps=1_00		,eta=0.1		,strength=0.8_5		,guidance_scale=3		,source_guidance_scale=1		,generator=__UpperCAmelCase		,output_type='np'		,)
													A__	     =							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 				snake_case__	(       self     )					->				Dict:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/cycle-diffusion/black_colored_car.png'     )
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'     )
													A__	     =							init_image.resize((5_12, 5_12)     )

													A__	     =							'CompVis/stable-diffusion-v1-4'
													A__	     =							DDIMScheduler.from_pretrained(__UpperCAmelCase		,subfolder='scheduler'     )
													A__	     =							CycleDiffusionPipeline.from_pretrained(__UpperCAmelCase		,scheduler=__UpperCAmelCase		,safety_checker=__UpperCAmelCase     )

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

													A__	     =							'A black colored car'
													A__	     =							'A blue colored car'

													A__	     =							torch.manual_seed(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,source_prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,num_inference_steps=1_00		,eta=0.1		,strength=0.8_5		,guidance_scale=3		,source_guidance_scale=1		,generator=__UpperCAmelCase		,output_type='np'		,)
													A__	     =							output.images

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






"""simple docstring"""




import numpy as np
from PIL import Image






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							np.array(UpperCamelCase__				)
							if arr.shape[0] != arr.shape[1]:
														raise ValueError('The input array is not a square matrix'				)
							A__	     =							0
							A__	     =							0
							A__	     =							0
							A__	     =							0

							# compute the shape of the output matrix
							A__	     =							(arr.shape[0] - size) // stride + 1
							# initialize the output matrix with zeros of shape maxpool_shape
							A__	     =							np.zeros((maxpool_shape, maxpool_shape)				)

							while i < arr.shape[0]:
														if i + size > arr.shape[0]:
																					# if the end of the matrix is reached, break
																					break
														while j < arr.shape[1]:
																					# if the end of the matrix is reached, break
																					if j + size > arr.shape[1]:
																												break
																					# compute the maximum of the pooling matrix
																					A__	     =							np.max(arr[i : i + size, j : j + size]				)
																					# shift the pooling matrix by stride of column pixels
																					j += stride
																					mat_j += 1

														# shift the pooling matrix by stride of row pixels
														i += stride
														mat_i += 1

														# reset the column index to 0
														A__	     =							0
														A__	     =							0

							return updated_arr






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							np.array(UpperCamelCase__				)
							if arr.shape[0] != arr.shape[1]:
														raise ValueError('The input array is not a square matrix'				)
							A__	     =							0
							A__	     =							0
							A__	     =							0
							A__	     =							0

							# compute the shape of the output matrix
							A__	     =							(arr.shape[0] - size) // stride + 1
							# initialize the output matrix with zeros of shape avgpool_shape
							A__	     =							np.zeros((avgpool_shape, avgpool_shape)				)

							while i < arr.shape[0]:
														# if the end of the matrix is reached, break
														if i + size > arr.shape[0]:
																					break
														while j < arr.shape[1]:
																					# if the end of the matrix is reached, break
																					if j + size > arr.shape[1]:
																												break
																					# compute the average of the pooling matrix
																					A__	     =							int(np.average(arr[i : i + size, j : j + size]				)				)
																					# shift the pooling matrix by stride of column pixels
																					j += stride
																					mat_j += 1

														# shift the pooling matrix by stride of row pixels
														i += stride
														mat_i += 1
														# reset the column index to 0
														A__	     =							0
														A__	     =							0

							return updated_arr


# Main Function
if __name__ == "__main__":
				from doctest import testmod

				testmod(name="avgpooling", verbose=True)

				# Loading the image
				__lowerCamelCase											=  Image.open("path_to_image")

				# Converting the image to numpy array and maxpooling, displaying the result
				# Ensure that the image is a square matrix

				Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

				# Converting the image to numpy array and averagepooling, displaying the result
				# Ensure that the image is a square matrix

				Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()





