
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					(     lowerCamelCase__				,						lowerCamelCase__				,						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 			SCREAMING_SNAKE_CASE    (				self		):
				torch.manual_seed(0		)
				__magic_name__		:		Optional[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						,   )
				__magic_name__		:		str	   =     DDIMScheduler(
				    beta_start=0.0_00_85						,   beta_end=0.0_12						,   beta_schedule="scaled_linear"						,   num_train_timesteps=1_000						,   clip_sample=lowercase__						,   set_alpha_to_one=lowercase__						,   )
				torch.manual_seed(0		)
				__magic_name__		:		Optional[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		)
				__magic_name__		:		Any	   =     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=1_000						,   )
				__magic_name__		:		Tuple	   =     CLIPTextModel(lowercase__		)
				__magic_name__		:		int	   =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				__magic_name__		:		List[Any]	   =     floats_tensor((1, 3, 32, 32)						,   rng=random.Random(lowercase__		)		).to(lowercase__		)
				__magic_name__		:		Any	   =     image / 2 + 0.5
				if str(lowercase__		).startswith("mps"		):
					__magic_name__		:		List[Any]	   =     torch.manual_seed(lowercase__		)
				else:
					__magic_name__		:		Tuple	   =     torch.Generator(device=lowercase__		).manual_seed(lowercase__		)
				__magic_name__		:		Union[str, 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 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[int]	   =     "cpu"  # ensure determinism for the device-dependent torch.Generator

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

				__magic_name__		:		List[Any]	   =     self.get_dummy_inputs(lowercase__		)
				__magic_name__		:		Union[str, Any]	   =     pipe(**lowercase__		)
				__magic_name__		:		List[str]	   =     output.images

				__magic_name__		:		Optional[int]	   =     images[0, -3:, -3:, -1]

				assert images.shape == (1, 32, 32, 3)
				__magic_name__		:		List[str]	   =     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 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     self.get_dummy_components()
				for name, module in components.items():
					if hasattr(lowercase__						,   "half"		):
						__magic_name__		:		Optional[Any]	   =     module.half()
				__magic_name__		:		Optional[int]	   =     CycleDiffusionPipeline(**lowercase__		)
				__magic_name__		:		List[str]	   =     pipe.to(lowercase__		)
				pipe.set_progress_bar_config(disable=lowercase__		)

				__magic_name__		:		List[str]	   =     self.get_dummy_inputs(lowercase__		)
				__magic_name__		:		List[Any]	   =     pipe(**lowercase__		)
				__magic_name__		:		Union[str, Any]	   =     output.images

				__magic_name__		:		Any	   =     images[0, -3:, -3:, -1]

				assert images.shape == (1, 32, 32, 3)
				__magic_name__		:		List[Any]	   =     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 			SCREAMING_SNAKE_CASE    (				self		):
				return super().test_save_load_local()







			@unittest.skip("non-deterministic pipeline"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return super().test_inference_batch_single_identical()







			@skip_mps
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return super().test_dict_tuple_outputs_equivalent()







			@skip_mps
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return super().test_save_load_optional_components()







			@skip_mps
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return super().test_attention_slicing_forward_pass()







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







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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/cycle-diffusion/black_colored_car.png"		)
				__magic_name__		:		str	   =     load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"		)
				__magic_name__		:		Optional[Any]	   =     init_image.resize((512, 512)		)

				__magic_name__		:		Any	   =     "CompVis/stable-diffusion-v1-4"
				__magic_name__		:		List[Any]	   =     DDIMScheduler.from_pretrained(lowercase__						,   subfolder="scheduler"		)
				__magic_name__		:		Optional[Any]	   =     CycleDiffusionPipeline.from_pretrained(
				    lowercase__						,   scheduler=lowercase__						,   safety_checker=lowercase__						,   torch_dtype=torch.floataa						,   revision="fp16"		)

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

				__magic_name__		:		str	   =     "A black colored car"
				__magic_name__		:		str	   =     "A blue colored car"

				__magic_name__		:		List[str]	   =     torch.manual_seed(0		)
				__magic_name__		:		str	   =     pipe(
				    prompt=lowercase__						,   source_prompt=lowercase__						,   image=lowercase__						,   num_inference_steps=100						,   eta=0.1						,   strength=0.85						,   guidance_scale=3						,   source_guidance_scale=1						,   generator=lowercase__						,   output_type="np"						,   )
				__magic_name__		:		str	   =     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 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/cycle-diffusion/black_colored_car.png"		)
				__magic_name__		:		Dict	   =     load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"		)
				__magic_name__		:		Optional[Any]	   =     init_image.resize((512, 512)		)

				__magic_name__		:		Dict	   =     "CompVis/stable-diffusion-v1-4"
				__magic_name__		:		Tuple	   =     DDIMScheduler.from_pretrained(lowercase__						,   subfolder="scheduler"		)
				__magic_name__		:		Optional[Any]	   =     CycleDiffusionPipeline.from_pretrained(lowercase__						,   scheduler=lowercase__						,   safety_checker=lowercase__		)

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

				__magic_name__		:		List[Any]	   =     "A black colored car"
				__magic_name__		:		List[Any]	   =     "A blue colored car"

				__magic_name__		:		Tuple	   =     torch.manual_seed(0		)
				__magic_name__		:		Tuple	   =     pipe(
				    prompt=lowercase__						,   source_prompt=lowercase__						,   image=lowercase__						,   num_inference_steps=100						,   eta=0.1						,   strength=0.85						,   guidance_scale=3						,   source_guidance_scale=1						,   generator=lowercase__						,   output_type="np"						,   )
				__magic_name__		:		Tuple	   =     output.images

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



'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__											=	logging.get_logger(__name__)

lowerCAmelCase__											=	{
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[str]							=			'convnextv2'






  def __init__(      self   :     Tuple					,lowercase__   :     Dict=3					,lowercase__   :     int=4					,lowercase__   :     Optional[int]=4					,lowercase__   :     Optional[Any]=None					,lowercase__   :     List[str]=None					,lowercase__   :     Tuple="gelu"					,lowercase__   :     Dict=0.0_2					,lowercase__   :     List[Any]=1e-1_2					,lowercase__   :     Optional[int]=0.0					,lowercase__   :     int=2_2_4					,lowercase__   :     int=None					,lowercase__   :     Tuple=None					,**lowercase__   :     List[str]					,):
   super().__init__(**lowercase__			)

   __lowercase							  = num_channels
   __lowercase							  = patch_size
   __lowercase							  = num_stages
   __lowercase							  = [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
   __lowercase							  = [3, 3, 9, 3] if depths is None else depths
   __lowercase							  = hidden_act
   __lowercase							  = initializer_range
   __lowercase							  = layer_norm_eps
   __lowercase							  = drop_path_rate
   __lowercase							  = image_size
   __lowercase							  = ['''stem'''] + [F"stage{idx}" for idx in range(1					,len(self.depths			) + 1			)]
   __lowercase  ,	__lowercase							  = get_aligned_output_features_output_indices(
       out_features=lowercase__					,out_indices=lowercase__					,stage_names=self.stage_names			)

