

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 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  str										=				CycleDiffusionPipeline
							_UpperCAmelCase   :  Any										=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    '''negative_prompt''',
							    '''height''',
							    '''width''',
							    '''negative_prompt_embeds''',
							}
							_UpperCAmelCase   :  Any										=				PipelineTesterMixin.required_optional_params - {'''latents'''}
							_UpperCAmelCase   :  str										=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}    )
							_UpperCAmelCase   :  int										=				IMAGE_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase   :  Tuple										=				IMAGE_TO_IMAGE_IMAGE_PARAMS
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								torch.manual_seed(0)
								__lowerCamelCase    :    List[str] 	=			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')       ,cross_attention_dim=3_2       ,)
								__lowerCamelCase    :    Union[str, Any] 	=			DDIMScheduler(
								    beta_start=0.00085       ,beta_end=0.012       ,beta_schedule='scaled_linear'       ,num_train_timesteps=1_0_0_0       ,clip_sample=SCREAMING_SNAKE_CASE__       ,set_alpha_to_one=SCREAMING_SNAKE_CASE__       ,)
								torch.manual_seed(0)
								__lowerCamelCase    :    List[str] 	=			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       ,)
								torch.manual_seed(0)
								__lowerCamelCase    :    Tuple 	=			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       ,)
								__lowerCamelCase    :    Dict 	=			CLIPTextModel(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

								__lowerCamelCase    :    int 	=			{
								    'unet': unet,
								    'scheduler': scheduler,
								    'vae': vae,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'safety_checker': None,
								    'feature_extractor': None,
								}
								return components
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      List[str]=0):
								__lowerCamelCase    :    Union[str, Any] 	=			floats_tensor((1, 3, 3_2, 3_2)       ,rng=random.Random(SCREAMING_SNAKE_CASE__)).to(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			image / 2 + 0.5
								if str(SCREAMING_SNAKE_CASE__).startswith('mps'):
									__lowerCamelCase    :    Dict 	=			torch.manual_seed(SCREAMING_SNAKE_CASE__)
								else:
									__lowerCamelCase    :    str 	=			torch.Generator(device=SCREAMING_SNAKE_CASE__).manual_seed(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			{
								    '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    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    List[str] 	=			'cpu'  # ensure determinism for the device-dependent torch.Generator

								__lowerCamelCase    :    Union[str, Any] 	=			self.get_dummy_components()
								__lowerCamelCase    :    List[Any] 	=			CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Any 	=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			pipe(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			output.images

								__lowerCamelCase    :    List[Any] 	=			images[0, -3:, -3:, -1]

								assert images.shape == (1, 3_2, 3_2, 3)
								__lowerCamelCase    :    Dict 	=			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    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    List[Any] 	=			self.get_dummy_components()
								for name, module in components.items():
									if hasattr(SCREAMING_SNAKE_CASE__       ,'half'):
										__lowerCamelCase    :    Dict 	=			module.half()
								__lowerCamelCase    :    Dict 	=			CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			pipe.to(SCREAMING_SNAKE_CASE__)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[int] 	=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[str] 	=			pipe(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			output.images

								__lowerCamelCase    :    Tuple 	=			images[0, -3:, -3:, -1]

								assert images.shape == (1, 3_2, 3_2, 3)
								__lowerCamelCase    :    Any 	=			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    lowerCAmelCase					(		self		:      List[Any]):
								return super().test_save_load_local()
							@unittest.skip('non-deterministic pipeline')
							def    lowerCAmelCase					(		self		:      List[str]):
								return super().test_inference_batch_single_identical()
							@skip_mps
							def    lowerCAmelCase					(		self		:      int):
								return super().test_dict_tuple_outputs_equivalent()
							@skip_mps
							def    lowerCAmelCase					(		self		:      Any):
								return super().test_save_load_optional_components()


							@skip_mps
							def    lowerCAmelCase					(		self		:      Optional[int]):
								return super().test_attention_slicing_forward_pass()






@slow
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      List[str]):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Any 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/cycle-diffusion/black_colored_car.png')
								__lowerCamelCase    :    Optional[int] 	=			load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy')
								__lowerCamelCase    :    Tuple 	=			init_image.resize((5_1_2, 5_1_2))

								__lowerCamelCase    :    str 	=			'CompVis/stable-diffusion-v1-4'
								__lowerCamelCase    :    int 	=			DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__       ,subfolder='scheduler')
								__lowerCamelCase    :    Optional[int] 	=			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()

								__lowerCamelCase    :    str 	=			'A black colored car'
								__lowerCamelCase    :    Optional[int] 	=			'A blue colored car'

								__lowerCamelCase    :    Optional[int] 	=			torch.manual_seed(0)
								__lowerCamelCase    :    Union[str, Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,source_prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,num_inference_steps=1_0_0       ,eta=0.1       ,strength=0.85       ,guidance_scale=3       ,source_guidance_scale=1       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)
								__lowerCamelCase    :    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    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    List[str] 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/cycle-diffusion/black_colored_car.png')
								__lowerCamelCase    :    List[Any] 	=			load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy')
								__lowerCamelCase    :    List[str] 	=			init_image.resize((5_1_2, 5_1_2))

								__lowerCamelCase    :    List[Any] 	=			'CompVis/stable-diffusion-v1-4'
								__lowerCamelCase    :    Dict 	=			DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__       ,subfolder='scheduler')
								__lowerCamelCase    :    Dict 	=			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()

								__lowerCamelCase    :    Optional[Any] 	=			'A black colored car'
								__lowerCamelCase    :    Dict 	=			'A blue colored car'

								__lowerCamelCase    :    Any 	=			torch.manual_seed(0)
								__lowerCamelCase    :    Optional[Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,source_prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,num_inference_steps=1_0_0       ,eta=0.1       ,strength=0.85       ,guidance_scale=3       ,source_guidance_scale=1       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)
								__lowerCamelCase    :    Optional[int] 	=			output.images

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







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					List[Any]:

	# A local function to see if a dot lands in the circle.
	def is_in_circle(lowerCamelCase__     ,			lowerCamelCase__      ) -> bool:
		__lowerCamelCase    :    Dict 	=			sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	__lowerCamelCase    :    Optional[int] 	=			mean(
	    int(is_in_circle(uniform(-1.0     ,			1.0      )     ,			uniform(-1.0     ,			1.0      )      )      )
	    for _ in range(lowerCamelCase__      )      )
	# The ratio of the area for circle to square is pi/4.
	__lowerCamelCase    :    Any 	=			proportion * 4
	print(F"The estimated value of pi is {pi_estimate}"      )
	print(F"The numpy value of pi is {pi}"      )
	print(F"The total error is {abs(pi - pi_estimate      )}"      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0     ,			)  ->					float:
	return mean(
	    function_to_integrate(uniform(lowerCamelCase__     ,			lowerCamelCase__      )      ) for _ in range(lowerCamelCase__      )      ) * (max_value - min_value)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__ = 0.0     ,			lowerCamelCase__ = 1.0      )  ->					None:

	def identity_function(lowerCamelCase__      ) -> float:
		return x

	__lowerCamelCase    :    str 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			(max_value * max_value - min_value * min_value) / 2

	print('******************'      )
	print(F"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(F"Estimated value is {estimated_value}"      )
	print(F"Expected value is {expected_value}"      )
	print(F"Total error is {abs(estimated_value - expected_value      )}"      )
	print('******************'      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					None:

	def function_to_integrate(lowerCamelCase__      ) -> float:
		return sqrt(4.0 - x * x      )

	__lowerCamelCase    :    Any 	=			area_under_curve_estimator(
	    lowerCamelCase__     ,			lowerCamelCase__     ,			0.0     ,			2.0      )

	print('******************'      )
	print('Estimating pi using area_under_curve_estimator'      )
	print(F"Estimated value is {estimated_value}"      )
	print(F"Expected value is {pi}"      )
	print(F"Total error is {abs(estimated_value - pi      )}"      )
	print('******************'      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




