



"""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  snake_case     (							__lowercase       ,			__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      CycleDiffusionPipeline
							UpperCAmelCase__						     =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    '''negative_prompt''',
							    '''height''',
							    '''width''',
							    '''negative_prompt_embeds''',
							}
							UpperCAmelCase__						     =      PipelineTesterMixin.required_optional_params - {'''latents'''}
							UpperCAmelCase__						     =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}   )
							UpperCAmelCase__						     =      IMAGE_TO_IMAGE_IMAGE_PARAMS
							UpperCAmelCase__						     =      IMAGE_TO_IMAGE_IMAGE_PARAMS

							def _lowercase					(self    ):

									"""simple docstring"""







									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''')       ,       cross_attention_dim=32       ,       )
									SCREAMING_SNAKE_CASE_						 = DDIMScheduler(
									    beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       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    )
									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       ,       )
									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-0_5       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       pad_token_id=1       ,       vocab_size=10_00       ,       )
									SCREAMING_SNAKE_CASE_						 = CLIPTextModel(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=0    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, 3, 32, 32)       ,       rng=random.Random(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = image / 2 + 0.5
									if str(SCREAMING_SNAKE_CASE_    ).startswith('''mps'''    ):
											SCREAMING_SNAKE_CASE_						 = torch.manual_seed(SCREAMING_SNAKE_CASE_    )
									else:
											SCREAMING_SNAKE_CASE_						 = torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = {
									    '''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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''cpu'''  # ensure determinism for the device-dependent torch.Generator

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

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

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

									assert images.shape == (1, 32, 32, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]    )

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

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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.get_dummy_components()
									for name, module in components.items():
											if hasattr(SCREAMING_SNAKE_CASE_       ,       '''half'''    ):
													SCREAMING_SNAKE_CASE_						 = module.half()
									SCREAMING_SNAKE_CASE_						 = CycleDiffusionPipeline(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = pipe.to(SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

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

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

									assert images.shape == (1, 32, 32, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]    )

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

							@skip_mps
							def _lowercase					(self    ):

									"""simple docstring"""







									return super().test_save_load_local()

							@unittest.skip('''non-deterministic pipeline'''    )
							def _lowercase					(self    ):

									"""simple docstring"""







									return super().test_inference_batch_single_identical()

							@skip_mps
							def _lowercase					(self    ):

									"""simple docstring"""







									return super().test_dict_tuple_outputs_equivalent()

							@skip_mps
							def _lowercase					(self    ):

									"""simple docstring"""







									return super().test_save_load_optional_components()







							@skip_mps
							def _lowercase					(self    ):

									"""simple docstring"""







									return super().test_attention_slicing_forward_pass()







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_						 = '''CompVis/stable-diffusion-v1-4'''
									SCREAMING_SNAKE_CASE_						 = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_       ,       subfolder='''scheduler'''    )
									SCREAMING_SNAKE_CASE_						 = 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()

									SCREAMING_SNAKE_CASE_						 = '''A black colored car'''
									SCREAMING_SNAKE_CASE_						 = '''A blue colored car'''

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = 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'''       ,       )
									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_						 = '''CompVis/stable-diffusion-v1-4'''
									SCREAMING_SNAKE_CASE_						 = DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE_       ,       subfolder='''scheduler'''    )
									SCREAMING_SNAKE_CASE_						 = 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()

									SCREAMING_SNAKE_CASE_						 = '''A black colored car'''
									SCREAMING_SNAKE_CASE_						 = '''A blue colored car'''

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = 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'''       ,       )
									SCREAMING_SNAKE_CASE_						 = output.images

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

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index