



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase				:				List[str]      		=		{
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				str      		=		["OwlViTFeatureExtractor"]
		_lowercase				:				Dict      		=		["OwlViTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Union[str, Any]      		=		[
		    "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OwlViTModel",
		    "OwlViTPreTrainedModel",
		    "OwlViTTextModel",
		    "OwlViTVisionModel",
		    "OwlViTForObjectDetection",
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		_lowercase				:				Optional[int]      		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							StableUnCLIPImgaImgPipeline
					_a         =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS
					_a         =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					_a         =							frozenset(
					    []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					_a         =							frozenset([]     )



					def 	snake_case ( self							:						List[str]  )->      str:
								lowerCamelCase__	:	Dict				=32
								lowerCamelCase__	:	Optional[Any]				=embedder_hidden_size

								# image encoding components

								lowerCamelCase__	:	Dict				=CLIPImageProcessor(crop_size=32,						size=32  )

								torch.manual_seed(0  )
								lowerCamelCase__	:	List[Any]				=CLIPVisionModelWithProjection(
								    CLIPVisionConfig(
								        hidden_size=lowerCamelCase,						projection_dim=lowerCamelCase,						num_hidden_layers=5,						num_attention_heads=4,						image_size=32,						intermediate_size=37,						patch_size=1,						)  )

								# regular denoising components

								torch.manual_seed(0  )
								lowerCamelCase__	:	Optional[int]				=StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase  )
								lowerCamelCase__	:	Dict				=DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''  )

								torch.manual_seed(0  )
								lowerCamelCase__	:	Optional[int]				=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								torch.manual_seed(0  )
								lowerCamelCase__	:	Tuple				=CLIPTextModel(
								    CLIPTextConfig(
								        bos_token_id=0,						eos_token_id=2,						hidden_size=lowerCamelCase,						projection_dim=32,						intermediate_size=37,						layer_norm_eps=1E-05,						num_attention_heads=4,						num_hidden_layers=5,						pad_token_id=1,						vocab_size=1000,						)  )

								torch.manual_seed(0  )
								lowerCamelCase__	:	Dict				=UNetaDConditionModel(
								    sample_size=32,						in_channels=4,						out_channels=4,						down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D'''),						up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D'''),						block_out_channels=(32, 64),						attention_head_dim=(2, 4),						class_embed_type='''projection''',						projection_class_embeddings_input_dim=embedder_projection_dim * 2,						cross_attention_dim=lowerCamelCase,						layers_per_block=1,						upcast_attention=lowerCamelCase,						use_linear_projection=lowerCamelCase,						)

								torch.manual_seed(0  )
								lowerCamelCase__	:	Union[str, Any]				=DDIMScheduler(
								    beta_schedule='''scaled_linear''',						beta_start=0.00_085,						beta_end=0.012,						prediction_type='''v_prediction''',						set_alpha_to_one=lowerCamelCase,						steps_offset=1,						)

								torch.manual_seed(0  )
								lowerCamelCase__	:	Optional[int]				=AutoencoderKL()

								lowerCamelCase__	:	int				={
								    # image encoding components
								    '''feature_extractor''': feature_extractor,
								    '''image_encoder''': image_encoder.eval(),
								    # image noising components
								    '''image_normalizer''': image_normalizer.eval(),
								    '''image_noising_scheduler''': image_noising_scheduler,
								    # regular denoising components
								    '''tokenizer''': tokenizer,
								    '''text_encoder''': text_encoder.eval(),
								    '''unet''': unet.eval(),
								    '''scheduler''': scheduler,
								    '''vae''': vae.eval(),
								}

								return components



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Any=0,						lowerCamelCase							:						str=True  )->      List[str]:
								if str(lowerCamelCase  ).startswith('''mps'''  ):
											lowerCamelCase__	:	List[Any]				=torch.manual_seed(lowerCamelCase  )
								else:
											lowerCamelCase__	:	Any				=torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

								lowerCamelCase__	:	Dict				=floats_tensor((1, 3, 32, 32),						rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )

								if pil_image:
											lowerCamelCase__	:	int				=input_image * 0.5 + 0.5
											lowerCamelCase__	:	Dict				=input_image.clamp(0,						1  )
											lowerCamelCase__	:	List[str]				=input_image.cpu().permute(0,						2,						3,						1  ).float().numpy()
											lowerCamelCase__	:	Dict				=DiffusionPipeline.numpy_to_pil(lowerCamelCase  )[0]

								return {
								    "prompt": "An anime racoon running a marathon",
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "np",
								}



					@skip_mps
					def 	snake_case ( self							:						List[str]  )->      Optional[Any]:
								lowerCamelCase__	:	Dict				='''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase__	:	str				=self.get_dummy_components()
								lowerCamelCase__	:	int				=StableUnCLIPImgaImgPipeline(**lowerCamelCase  )
								lowerCamelCase__	:	Any				=sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	Dict				=self.get_dummy_inputs(lowerCamelCase  )
								inputs.update({'''image_embeds''': None}  )
								lowerCamelCase__	:	Any				=sd_pipe(**lowerCamelCase  ).images
								lowerCamelCase__	:	List[Any]				=image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCamelCase__	:	Union[str, Any]				=np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



					def 	snake_case ( self							:						int  )->      Tuple:
								lowerCamelCase__	:	Tuple				=torch_device in ['''cpu''', '''mps''']

								self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase  )



					def 	snake_case ( self							:						int  )->      Optional[Any]:
								lowerCamelCase__	:	List[Any]				=torch_device in ['''cpu''', '''mps''']

								self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase  )




					@unittest.skipIf(
					    torch_device != '''cuda''' or not is_xformers_available(),						reason='''XFormers attention is only available with CUDA and `xformers` installed''',						)
					def 	snake_case ( self							:						List[str]  )->      List[str]:
								self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase  )





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


					'''simple docstring'''





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



					def 	snake_case ( self							:						Optional[int]  )->      int:
								lowerCamelCase__	:	Tuple				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''  )

								lowerCamelCase__	:	Optional[int]				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'''  )

								lowerCamelCase__	:	Optional[Any]				=StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-l-img2img''',						torch_dtype=torch.floataa  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								# stable unclip will oom when integration tests are run on a V100,
								# so turn on memory savings
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase__	:	int				=torch.Generator(device='''cpu'''  ).manual_seed(0  )
								lowerCamelCase__	:	Any				=pipe(lowerCamelCase,						'''anime turle''',						generator=lowerCamelCase,						output_type='''np'''  )

								lowerCamelCase__	:	List[Any]				=output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								lowerCamelCase__	:	Any				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''  )

								lowerCamelCase__	:	str				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'''  )

								lowerCamelCase__	:	Optional[int]				=StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-h-img2img''',						torch_dtype=torch.floataa  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								# stable unclip will oom when integration tests are run on a V100,
								# so turn on memory savings
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase__	:	str				=torch.Generator(device='''cpu'''  ).manual_seed(0  )
								lowerCamelCase__	:	Tuple				=pipe(lowerCamelCase,						'''anime turle''',						generator=lowerCamelCase,						output_type='''np'''  )

								lowerCamelCase__	:	Tuple				=output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(lowerCamelCase,						lowerCamelCase  )




					def 	snake_case ( self							:						Optional[int]  )->      List[str]:
								lowerCamelCase__	:	int				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''  )

								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								lowerCamelCase__	:	Any				=StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-h-img2img''',						torch_dtype=torch.floataa  )
								lowerCamelCase__	:	Optional[Any]				=pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowerCamelCase__	:	List[Any]				=pipe(
								    lowerCamelCase,						'''anime turtle''',						num_inference_steps=2,						output_type='''np''',						)

								lowerCamelCase__	:	Optional[int]				=torch.cuda.max_memory_allocated()
								# make sure that less than 7 GB is allocated
								assert mem_bytes < 7 * 10**9




