






'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     str   		=			KandinskyInpaintPipeline
       snake_case__    :     Optional[Any]   		=			['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
       snake_case__    :     Dict   		=			[
           'prompt',
           'negative_prompt',
           'image_embeds',
           'negative_image_embeds',
           'image',
           'mask_image',
       ]
       snake_case__    :     List[Any]   		=			[
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'negative_prompt',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       snake_case__    :     List[str]   		=			False




       @property
       def 					a_					(					self      : Any					):


            """simple docstring"""







            return 32




       @property
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            return 32




       @property
       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            return self.time_input_dim




       @property
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            return self.time_input_dim * 4




       @property
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            return 100




       @property
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
            return tokenizer




       @property
       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            torch.manual_seed(0					)
            __lowerCamelCase						:       int				   =				MCLIPConfig(
                numDims=self.cross_attention_dim	,    transformerDimensions=self.text_embedder_hidden_size	,    hidden_size=self.text_embedder_hidden_size	,    intermediate_size=37	,    num_attention_heads=4	,    num_hidden_layers=5	,    vocab_size=1005	,    )

            __lowerCamelCase						:       Optional[int]				   =				MultilingualCLIP(_UpperCAmelCase					)
            __lowerCamelCase						:       Any				   =				text_encoder.eval()

            return text_encoder




       @property
       def 					a_					(					self      : Any					):


            """simple docstring"""







            torch.manual_seed(0					)

            __lowerCamelCase						:       Optional[Any]				   =				{
                """in_channels""": 9,
                # Out channels is double in channels because predicts mean and variance
                """out_channels""": 8,
                """addition_embed_type""": """text_image""",
                """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
                """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
                """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
                """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
                """layers_per_block""": 1,
                """encoder_hid_dim""": self.text_embedder_hidden_size,
                """encoder_hid_dim_type""": """text_image_proj""",
                """cross_attention_dim""": self.cross_attention_dim,
                """attention_head_dim""": 4,
                """resnet_time_scale_shift""": """scale_shift""",
                """class_embed_type""": None,
            }

            __lowerCamelCase						:       int				   =				UNetaDConditionModel(**_UpperCAmelCase					)
            return model




       @property
       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            return {
                "block_out_channels": [32, 64],
                "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                "in_channels": 3,
                "latent_channels": 4,
                "layers_per_block": 1,
                "norm_num_groups": 8,
                "norm_type": "spatial",
                "num_vq_embeddings": 12,
                "out_channels": 3,
                "up_block_types": [
                    "AttnUpDecoderBlock2D",
                    "UpDecoderBlock2D",
                ],
                "vq_embed_dim": 4,
            }




       @property
       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            torch.manual_seed(0					)
            __lowerCamelCase						:       List[str]				   =				VQModel(**self.dummy_movq_kwargs					)
            return model




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				self.dummy_text_encoder
            __lowerCamelCase						:       Optional[int]				   =				self.dummy_tokenizer
            __lowerCamelCase						:       int				   =				self.dummy_unet
            __lowerCamelCase						:       List[str]				   =				self.dummy_movq

            __lowerCamelCase						:       Union[str, Any]				   =				DDIMScheduler(
                num_train_timesteps=1000	,    beta_schedule="""linear"""	,    beta_start=0.0_0085	,    beta_end=0.012	,    clip_sample=_UpperCAmelCase	,    set_alpha_to_one=_UpperCAmelCase	,    steps_offset=1	,    prediction_type="""epsilon"""	,    thresholding=_UpperCAmelCase	,    )

            __lowerCamelCase						:       Optional[Any]				   =				{
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
                """unet""": unet,
                """scheduler""": scheduler,
                """movq""": movq,
            }

            return components




       def 					a_					(					self      : List[str]	,    A__      : Any	,    A__      : int=0					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				floats_tensor((1, self.cross_attention_dim)	,    rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)
            __lowerCamelCase						:       Union[str, Any]				   =				floats_tensor((1, self.cross_attention_dim)	,    rng=random.Random(seed + 1					)					).to(_UpperCAmelCase					)
            # create init_image
            __lowerCamelCase						:       List[Any]				   =				floats_tensor((1, 3, 64, 64)	,    rng=random.Random(_UpperCAmelCase					)					).to(_UpperCAmelCase					)
            __lowerCamelCase						:       Any				   =				image.cpu().permute(0	,    2	,    3	,    1					)[0]
            __lowerCamelCase						:       Dict				   =				Image.fromarray(np.uinta(_UpperCAmelCase					)					).convert("""RGB"""					).resize((256, 256)					)
            # create mask
            __lowerCamelCase						:       int				   =				np.ones((64, 64)	,    dtype=np.floataa					)
            __lowerCamelCase						:       List[Any]				   =				0

            if str(_UpperCAmelCase					).startswith("""mps"""					):
                 __lowerCamelCase						:       Union[str, Any]				   =				torch.manual_seed(_UpperCAmelCase					)
            else:
                 __lowerCamelCase						:       List[str]				   =				torch.Generator(device=_UpperCAmelCase					).manual_seed(_UpperCAmelCase					)
            __lowerCamelCase						:       List[str]				   =				{
                """prompt""": """horse""",
                """image""": init_image,
                """mask_image""": mask,
                """image_embeds""": image_embeds,
                """negative_image_embeds""": negative_image_embeds,
                """generator""": generator,
                """height""": 64,
                """width""": 64,
                """num_inference_steps""": 2,
                """guidance_scale""": 4.0,
                """output_type""": """np""",
            }
            return inputs




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				"""cpu"""

            __lowerCamelCase						:       List[Any]				   =				self.get_dummy_components()

            __lowerCamelCase						:       Optional[Any]				   =				self.pipeline_class(**_UpperCAmelCase					)
            __lowerCamelCase						:       Dict				   =				pipe.to(_UpperCAmelCase					)

            pipe.set_progress_bar_config(disable=_UpperCAmelCase					)

            __lowerCamelCase						:       List[Any]				   =				pipe(**self.get_dummy_inputs(_UpperCAmelCase					)					)
            __lowerCamelCase						:       Dict				   =				output.images

            __lowerCamelCase						:       Dict				   =				pipe(
                **self.get_dummy_inputs(_UpperCAmelCase					)	,    return_dict=_UpperCAmelCase	,    )[0]

            __lowerCamelCase						:       Any				   =				image[0, -3:, -3:, -1]
            __lowerCamelCase						:       Optional[int]				   =				image_from_tuple[0, -3:, -3:, -1]

            print(f"image.shape {image.shape}"					)

            assert image.shape == (1, 64, 64, 3)

            __lowerCamelCase						:       Any				   =				np.array(
                [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

            assert (
                np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
            assert (
                np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
            ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            super().test_inference_batch_single_identical(expected_max_diff=3e-3					)







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




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

            __lowerCamelCase						:       str				   =				load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
            __lowerCamelCase						:       int				   =				np.ones((768, 768)	,    dtype=np.floataa					)
            __lowerCamelCase						:       List[str]				   =				0

            __lowerCamelCase						:       int				   =				"""a hat"""

            __lowerCamelCase						:       int				   =				KandinskyPriorPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-1-prior"""	,    torch_dtype=torch.floataa					)
            pipe_prior.to(_UpperCAmelCase					)

            __lowerCamelCase						:       Dict				   =				KandinskyInpaintPipeline.from_pretrained(
                """kandinsky-community/kandinsky-2-1-inpaint"""	,    torch_dtype=torch.floataa					)
            __lowerCamelCase						:       Any				   =				pipeline.to(_UpperCAmelCase					)
            pipeline.set_progress_bar_config(disable=_UpperCAmelCase					)

            __lowerCamelCase						:       Optional[Any]				   =				torch.Generator(device="""cpu"""					).manual_seed(0					)
            __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				pipe_prior(
                _UpperCAmelCase	,    generator=_UpperCAmelCase	,    num_inference_steps=5	,    negative_prompt=""""""	,    ).to_tuple()

            __lowerCamelCase						:       Optional[int]				   =				pipeline(
                _UpperCAmelCase	,    image=_UpperCAmelCase	,    mask_image=_UpperCAmelCase	,    image_embeds=_UpperCAmelCase	,    negative_image_embeds=_UpperCAmelCase	,    generator=_UpperCAmelCase	,    num_inference_steps=100	,    height=768	,    width=768	,    output_type="""np"""	,    )

            __lowerCamelCase						:       int				   =				output.images[0]

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

            assert_mean_pixel_difference(_UpperCAmelCase	,    _UpperCAmelCase					)











'''simple docstring'''

from ..utils import DummyObject, requires_backends







class 	SCREAMING_SNAKE_CASE	(      metaclass=lowerCAmelCase_  ):
       snake_case__    :     Union[str, Any]   		=			['torch', 'torchsde']




       def __init__(					self      : int	,    *A__      : Optional[Any]	,    **A__      : Dict					):


            """simple docstring"""







            requires_backends(self	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Optional[Any]	,    *A__      : str	,    **A__      : Optional[Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Union[str, Any]	,    *A__      : Any	,    **A__      : Union[str, Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)



