import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor


class 	lowerCamelCase_     (   unittest.TestCase ):






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase=7 , __lowercase=3 , __lowercase=18 , __lowercase=30 , __lowercase=400 , __lowercase=True , __lowercase=None , __lowercase=True , __lowercase=None , __lowercase=True , )       ->	Dict:
        __UpperCamelCase     :Union[str, Any]			  =		size if size is not None else {'''shortest_edge''': 20}
        __UpperCamelCase     :List[str]			  =		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
        __UpperCamelCase     :int			  =		parent
        __UpperCamelCase     :Union[str, Any]			  =		batch_size
        __UpperCamelCase     :Tuple			  =		num_channels
        __UpperCamelCase     :Tuple			  =		image_size
        __UpperCamelCase     :List[Any]			  =		min_resolution
        __UpperCamelCase     :Tuple			  =		max_resolution
        __UpperCamelCase     :Tuple			  =		do_resize
        __UpperCamelCase     :Any			  =		size
        __UpperCamelCase     :Optional[int]			  =		do_center_crop
        __UpperCamelCase     :int			  =		crop_size
        __UpperCamelCase     :Optional[int]			  =		do_flip_channel_order







    def 			UpperCamelCase__   (	self)       ->	Any:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_flip_channel_order": self.do_flip_channel_order,
        }


@require_torch
@require_vision
class 	lowerCamelCase_     (   UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Optional[int]						=      MobileViTImageProcessor if is_vision_available() else None







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :int			  =		MobileViTImageProcessingTester(self)







    @property
    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Union[str, Any]			  =		self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(__lowercase , '''do_resize'''))
        self.assertTrue(hasattr(__lowercase , '''size'''))
        self.assertTrue(hasattr(__lowercase , '''do_center_crop'''))
        self.assertTrue(hasattr(__lowercase , '''center_crop'''))
        self.assertTrue(hasattr(__lowercase , '''do_flip_channel_order'''))







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[int]			  =		self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size , {'''shortest_edge''': 20})
        self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18})

        __UpperCamelCase     :Optional[Any]			  =		self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84)
        self.assertEqual(image_processor.size , {'''shortest_edge''': 42})
        self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84})







    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        pass







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        # Initialize image_processing
        __UpperCamelCase     :List[str]			  =		self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        __UpperCamelCase     :List[str]			  =		prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase , Image.Image)

        # Test not batched input
        __UpperCamelCase     :int			  =		image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape , (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            ) , )

        # Test batched
        __UpperCamelCase     :str			  =		image_processing(__lowercase , return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            ) , )







    def 			UpperCamelCase__   (	self)       ->	Dict:
        # Initialize image_processing
        __UpperCamelCase     :Union[str, Any]			  =		self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        __UpperCamelCase     :List[str]			  =		prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase , np.ndarray)

        # Test not batched input
        __UpperCamelCase     :List[Any]			  =		image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape , (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            ) , )

        # Test batched
        __UpperCamelCase     :Optional[int]			  =		image_processing(__lowercase , return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            ) , )







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        # Initialize image_processing
        __UpperCamelCase     :List[Any]			  =		self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        __UpperCamelCase     :Union[str, Any]			  =		prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase , torch.Tensor)

        # Test not batched input
        __UpperCamelCase     :Optional[Any]			  =		image_processing(image_inputs[0] , return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape , (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            ) , )

        # Test batched
        __UpperCamelCase     :Optional[int]			  =		image_processing(__lowercase , return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            ) , )






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 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						int						=      StableUnCLIPImgaImgPipeline
    a__	:						Optional[int]						=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__	:						Union[str, Any]						=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__	:						Optional[Any]						=      frozenset(
        [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__	:						int						=      frozenset([] )







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Tuple			  =		32
        __UpperCamelCase     :Optional[int]			  =		embedder_hidden_size

        # image encoding components

        __UpperCamelCase     :Union[str, Any]			  =		CLIPImageProcessor(crop_size=32 , size=32)

        torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase , projection_dim=__lowercase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase     :str			  =		StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase     :Optional[int]			  =		DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase     :Union[str, Any]			  =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase     :Dict			  =		CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0 , eos_token_id=2 , hidden_size=__lowercase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ))

        torch.manual_seed(0)
        __UpperCamelCase     :List[Any]			  =		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=__lowercase , layers_per_block=1 , upcast_attention=__lowercase , use_linear_projection=__lowercase , )

        torch.manual_seed(0)
        __UpperCamelCase     :Tuple			  =		DDIMScheduler(
            beta_schedule='''scaled_linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , prediction_type='''v_prediction''' , set_alpha_to_one=__lowercase , steps_offset=1 , )

        torch.manual_seed(0)
        __UpperCamelCase     :List[str]			  =		AutoencoderKL()

        __UpperCamelCase     :Tuple			  =		{
            # 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 			UpperCamelCase__   (	self , __lowercase , __lowercase=0 , __lowercase=True)       ->	str:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase     :Union[str, Any]			  =		torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase     :int			  =		torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase     :int			  =		floats_tensor((1, 3, 32, 32) , rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase     :List[Any]			  =		input_image * 0.5 + 0.5
            __UpperCamelCase     :Optional[Any]			  =		input_image.clamp(0 , 1)
            __UpperCamelCase     :int			  =		input_image.cpu().permute(0 , 2 , 3 , 1).float().numpy()
            __UpperCamelCase     :Optional[Any]			  =		DiffusionPipeline.numpy_to_pil(__lowercase)[0]

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







    @skip_mps
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :Dict			  =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase     :Tuple			  =		self.get_dummy_components()
        __UpperCamelCase     :Any			  =		StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

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

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase     :List[Any]			  =		np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

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







    def 			UpperCamelCase__   (	self)       ->	str:
        __UpperCamelCase     :Optional[Any]			  =		torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[Any]			  =		torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







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


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






    '''simple docstring'''






    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        __UpperCamelCase     :int			  =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase     :Any			  =		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''')

        __UpperCamelCase     :List[Any]			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img''' , torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # 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()

        __UpperCamelCase     :int			  =		torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase     :Dict			  =		pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')

        __UpperCamelCase     :Dict			  =		output.images[0]

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

        assert_mean_pixel_difference(__lowercase , __lowercase)







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

        __UpperCamelCase     :Any			  =		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''')

        __UpperCamelCase     :Any			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # 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()

        __UpperCamelCase     :int			  =		torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase     :Optional[int]			  =		pipe(__lowercase , '''anime turle''' , generator=__lowercase , output_type='''np''')

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

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

        assert_mean_pixel_difference(__lowercase , __lowercase)







    def 			UpperCamelCase__   (	self)       ->	List[str]:
        __UpperCamelCase     :Dict			  =		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()

        __UpperCamelCase     :List[Any]			  =		StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img''' , torch_dtype=torch.floataa)
        __UpperCamelCase     :Union[str, Any]			  =		pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        __UpperCamelCase     :Optional[Any]			  =		pipe(
            __lowercase , '''anime turtle''' , num_inference_steps=2 , output_type='''np''' , )

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





