


from typing import List

from .keymap import KEYMAP, get_character






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			def decorator(SCREAMING_SNAKE_CASE_   :		Any	):
						UpperCamelCase         =					getattr(_A       ,  """handle_key"""       ,  []	)
						handle += [key]
						setattr(_A       ,  """handle_key"""       ,  _A	)
						return func

			return decorator






def 							_lowercase    (		*SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			def decorator(SCREAMING_SNAKE_CASE_   :		Union[str, Any]	):
						UpperCamelCase         =					getattr(_A       ,  """handle_key"""       ,  []	)
						handle += keys
						setattr(_A       ,  """handle_key"""       ,  _A	)
						return func

			return decorator




class    UpperCAmelCase					(       lowercase__    ):




			def __new__(						cls							:		Tuple       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					super().__new__(cls       ,       __lowerCamelCase       ,       __lowerCamelCase       ,       __lowerCamelCase						)
						if not hasattr(__lowerCamelCase       ,       """key_handler"""						):
									setattr(__lowerCamelCase       ,       """key_handler"""       ,       {}						)
						setattr(__lowerCamelCase       ,       """handle_input"""       ,       KeyHandler.handle_input						)

						for value in attrs.values():
									UpperCamelCase         =					getattr(__lowerCamelCase       ,       """handle_key"""       ,       []						)
									for key in handled_keys:
												UpperCamelCase         =					value
						return new_cls





			@staticmethod
			def   lowerCamelCase_					(						cls							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					get_character()
						if char != KEYMAP["undefined"]:
									UpperCamelCase         =					ord(__lowerCamelCase						)
						UpperCamelCase         =					cls.key_handler.get(__lowerCamelCase						)
						if handler:
									UpperCamelCase         =					char
									return handler(cls						)
						else:
									return None






def 							_lowercase    (		cls   :		int	):





			"""simple docstring"""




			return KeyHandler(cls.__name__       ,  cls.__bases__       ,  cls.__dict__.copy()	)








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    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= KandinskyInpaintPipeline
			lowercase						= ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowercase						= [
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowercase						= [
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowercase						= False




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







						"""simple docstring"""







						return 3_2




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						return 3_2




			@property
			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						return self.time_input_dim




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







						"""simple docstring"""







						return self.time_input_dim * 4




			@property
			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						return 1_0_0




			@property
			def   lowerCamelCase_					(						self							:		Optional[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
						return tokenizer




			@property
			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					MCLIPConfig(
						    numDims=self.cross_attention_dim       ,       transformerDimensions=self.text_embedder_hidden_size       ,       hidden_size=self.text_embedder_hidden_size       ,       intermediate_size=3_7       ,       num_attention_heads=4       ,       num_hidden_layers=5       ,       vocab_size=1_0_0_5       ,       )

						UpperCamelCase         =					MultilingualCLIP(__magic_name__						)
						UpperCamelCase         =					text_encoder.eval()

						return text_encoder




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







						"""simple docstring"""







						torch.manual_seed(0						)

						UpperCamelCase         =					{
						    """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,
						}

						UpperCamelCase         =					UNetaDConditionModel(**__magic_name__						)
						return model




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







						"""simple docstring"""







						return {
						    "block_out_channels": [3_2, 6_4],
						    "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": 1_2,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}




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







						"""simple docstring"""







						torch.manual_seed(0						)
						UpperCamelCase         =					VQModel(**self.dummy_movq_kwargs						)
						return model




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					self.dummy_text_encoder
						UpperCamelCase         =					self.dummy_tokenizer
						UpperCamelCase         =					self.dummy_unet
						UpperCamelCase         =					self.dummy_movq

						UpperCamelCase         =					DDIMScheduler(
						    num_train_timesteps=1_0_0_0       ,       beta_schedule="""linear"""       ,       beta_start=0.00_085       ,       beta_end=0.012       ,       clip_sample=__magic_name__       ,       set_alpha_to_one=__magic_name__       ,       steps_offset=1       ,       prediction_type="""epsilon"""       ,       thresholding=__magic_name__       ,       )

						UpperCamelCase         =					{
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		Optional[int]=0						):







						"""simple docstring"""







						UpperCamelCase         =					floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(seed + 1						)						).to(__magic_name__						)
						# create init_image
						UpperCamelCase         =					floats_tensor((1, 3, 6_4, 6_4)       ,       rng=random.Random(__magic_name__						)						).to(__magic_name__						)
						UpperCamelCase         =					image.cpu().permute(0       ,       2       ,       3       ,       1						)[0]
						UpperCamelCase         =					Image.fromarray(np.uinta(__magic_name__						)						).convert("""RGB"""						).resize((2_5_6, 2_5_6)						)
						# create mask
						UpperCamelCase         =					np.ones((6_4, 6_4)       ,       dtype=np.floataa						)
						UpperCamelCase         =					0

						if str(__magic_name__						).startswith("""mps"""						):
									UpperCamelCase         =					torch.manual_seed(__magic_name__						)
						else:
									UpperCamelCase         =					torch.Generator(device=__magic_name__						).manual_seed(__magic_name__						)
						UpperCamelCase         =					{
						    """prompt""": """horse""",
						    """image""": init_image,
						    """mask_image""": mask,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 6_4,
						    """width""": 6_4,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 4.0,
						    """output_type""": """np""",
						}
						return inputs




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







						"""simple docstring"""







						UpperCamelCase         =					"""cpu"""

						UpperCamelCase         =					self.get_dummy_components()

						UpperCamelCase         =					self.pipeline_class(**__magic_name__						)
						UpperCamelCase         =					pipe.to(__magic_name__						)

						pipe.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					pipe(**self.get_dummy_inputs(__magic_name__						)						)
						UpperCamelCase         =					output.images

						UpperCamelCase         =					pipe(
						    **self.get_dummy_inputs(__magic_name__						)       ,       return_dict=__magic_name__       ,       )[0]

						UpperCamelCase         =					image[0, -3:, -3:, -1]
						UpperCamelCase         =					image_from_tuple[0, -3:, -3:, -1]

						print(F'image.shape {image.shape}'						)

						assert image.shape == (1, 6_4, 6_4, 3)

						UpperCamelCase         =					np.array(
						    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]						)

						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   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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




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




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







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





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







						"""simple docstring"""







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

						UpperCamelCase         =					load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
						UpperCamelCase         =					np.ones((7_6_8, 7_6_8)       ,       dtype=np.floataa						)
						UpperCamelCase         =					0

						UpperCamelCase         =					"""a hat"""

						UpperCamelCase         =					KandinskyPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-prior"""       ,       torch_dtype=torch.floataa						)
						pipe_prior.to(__magic_name__						)

						UpperCamelCase         =					KandinskyInpaintPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-inpaint"""       ,       torch_dtype=torch.floataa						)
						UpperCamelCase         =					pipeline.to(__magic_name__						)
						pipeline.set_progress_bar_config(disable=__magic_name__						)

						UpperCamelCase         =					torch.Generator(device="""cpu"""						).manual_seed(0						)
						UpperCamelCase  ,  UpperCamelCase         =					pipe_prior(
						    __magic_name__       ,       generator=__magic_name__       ,       num_inference_steps=5       ,       negative_prompt=""""""       ,       ).to_tuple()

						UpperCamelCase         =					pipeline(
						    __magic_name__       ,       image=__magic_name__       ,       mask_image=__magic_name__       ,       image_embeds=__magic_name__       ,       negative_image_embeds=__magic_name__       ,       generator=__magic_name__       ,       num_inference_steps=1_0_0       ,       height=7_6_8       ,       width=7_6_8       ,       output_type="""np"""       ,       )

						UpperCamelCase         =					output.images[0]

						assert image.shape == (7_6_8, 7_6_8, 3)

						assert_mean_pixel_difference(__magic_name__       ,       __magic_name__						)




