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 						SCREAMING_SNAKE_CASE				( __lowerCamelCase  ,	__lowerCamelCase  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					CycleDiffusionPipeline
							A_						  =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    "negative_prompt",
							    "height",
							    "width",
							    "negative_prompt_embeds",
							}
							A_						  =					PipelineTesterMixin.required_optional_params - {"latents"}
							A_						  =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}					)
							A_						  =					IMAGE_TO_IMAGE_IMAGE_PARAMS
							A_						  =					IMAGE_TO_IMAGE_IMAGE_PARAMS

							def       __A (  self:   Optional[Any]   )    ->				Tuple:
												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=0.00_085		,     beta_end=0.012		,     beta_schedule='''scaled_linear'''		,     num_train_timesteps=10_00		,     clip_sample=SCREAMING_SNAKE_CASE_		,     set_alpha_to_one=SCREAMING_SNAKE_CASE_		,     )
												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(SCREAMING_SNAKE_CASE_   )
												_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       __A (  self:   str		,     __A:   Any		,     __A:   int=0   )    ->				List[Any]:
												_A								= floats_tensor((1, 3, 32, 32)		,     rng=random.Random(SCREAMING_SNAKE_CASE_   )   ).to(SCREAMING_SNAKE_CASE_   )
												_A								= image / 2 + 0.5
												if str(SCREAMING_SNAKE_CASE_   ).startswith('''mps'''   ):
																	_A								= torch.manual_seed(SCREAMING_SNAKE_CASE_   )
												else:
																	_A								= torch.Generator(device=SCREAMING_SNAKE_CASE_   ).manual_seed(SCREAMING_SNAKE_CASE_   )
												_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       __A (  self:   Optional[int]   )    ->				Optional[int]:
												_A								= '''cpu'''  # ensure determinism for the device-dependent torch.Generator

												_A								= self.get_dummy_components()
												_A								= CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_   )
												_A								= pipe.to(SCREAMING_SNAKE_CASE_   )
												pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

												_A								= self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
												_A								= pipe(**SCREAMING_SNAKE_CASE_   )
												_A								= output.images

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

												assert images.shape == (1, 32, 32, 3)
												_A								= np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]   )

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

							@unittest.skipIf(torch_device != '''cuda'''		,     '''This test requires a GPU'''   )
							def       __A (  self:   Tuple   )    ->				List[str]:
												_A								= self.get_dummy_components()
												for name, module in components.items():
																	if hasattr(SCREAMING_SNAKE_CASE_		,     '''half'''   ):
																						_A								= module.half()
												_A								= CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_   )
												_A								= pipe.to(SCREAMING_SNAKE_CASE_   )
												pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

												_A								= self.get_dummy_inputs(SCREAMING_SNAKE_CASE_   )
												_A								= pipe(**SCREAMING_SNAKE_CASE_   )
												_A								= output.images

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

												assert images.shape == (1, 32, 32, 3)
												_A								= np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]   )

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

							@skip_mps
							def       __A (  self:   Optional[int]   )    ->				str:
												return super().test_save_load_local()

							@unittest.skip('''non-deterministic pipeline'''   )
							def       __A (  self:   Optional[Any]   )    ->				Optional[int]:
												return super().test_inference_batch_single_identical()

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

							@skip_mps
							def       __A (  self:   Union[str, Any]   )    ->				List[Any]:
												return super().test_save_load_optional_components()



							@skip_mps
							def       __A (  self:   Union[str, Any]   )    ->				Optional[int]:
												return super().test_attention_slicing_forward_pass()





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





							"""simple docstring"""

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

							def       __A (  self:   Optional[int]   )    ->				str:
												_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(SCREAMING_SNAKE_CASE_		,     subfolder='''scheduler'''   )
												_A								= CycleDiffusionPipeline.from_pretrained(
												    SCREAMING_SNAKE_CASE_		,     scheduler=SCREAMING_SNAKE_CASE_		,     safety_checker=SCREAMING_SNAKE_CASE_		,     torch_dtype=torch.floataa		,     revision='''fp16'''   )

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

												_A								= '''A black colored car'''
												_A								= '''A blue colored car'''

												_A								= torch.manual_seed(0   )
												_A								= pipe(
												    prompt=SCREAMING_SNAKE_CASE_		,     source_prompt=SCREAMING_SNAKE_CASE_		,     image=SCREAMING_SNAKE_CASE_		,     num_inference_steps=1_00		,     eta=0.1		,     strength=0.85		,     guidance_scale=3		,     source_guidance_scale=1		,     generator=SCREAMING_SNAKE_CASE_		,     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       __A (  self:   Tuple   )    ->				Tuple:
												_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(SCREAMING_SNAKE_CASE_		,     subfolder='''scheduler'''   )
												_A								= CycleDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_		,     scheduler=SCREAMING_SNAKE_CASE_		,     safety_checker=SCREAMING_SNAKE_CASE_   )

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

												_A								= '''A black colored car'''
												_A								= '''A blue colored car'''

												_A								= torch.manual_seed(0   )
												_A								= pipe(
												    prompt=SCREAMING_SNAKE_CASE_		,     source_prompt=SCREAMING_SNAKE_CASE_		,     image=SCREAMING_SNAKE_CASE_		,     num_inference_steps=1_00		,     eta=0.1		,     strength=0.85		,     guidance_scale=3		,     source_guidance_scale=1		,     generator=SCREAMING_SNAKE_CASE_		,     output_type='''np'''		,     )
												_A								= output.images

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











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A							,_A								= len(_lowercase ), len(grid[0] )
					if (
					    min(_lowercase					,			_lowercase ) < 0
					    or row == row_length
					    or col == col_length
					    or (row, col) in visit
					    or grid[row][col] == 1
					):
										return 0
					if row == row_length - 1 and col == col_length - 1:
										return 1

					visit.add((row, col) )

					_A								= 0
					count += depth_first_search(_lowercase					,			row + 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			row - 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col + 1					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col - 1					,			_lowercase )

					visit.remove((row, col) )
					return count


if __name__ == "__main__":
				import doctest

				doctest.testmod()



