
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				(					UpperCamelCase__     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						str					      = KandinskyInpaintPipeline
			__UpperCamelCase      :						Union[str, Any]					      = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			__UpperCamelCase      :						Any					      = [
			    """prompt""",
			    """negative_prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			__UpperCamelCase      :						Dict					      = [
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			__UpperCamelCase      :						List[str]					      = False

			@property
			def       lowerCAmelCase__	(       self :       Dict      ):
							return 32

			@property
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							return 32

			@property
			def       lowerCAmelCase__	(       self :       List[str]      ):
							return self.time_input_dim

			@property
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							return self.time_input_dim * 4

			@property
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							return 100

			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Dict	=						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""      )
							return tokenizer

			@property
			def       lowerCAmelCase__	(       self :       List[str]      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Optional[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						,	)

							UpperCamelCase_:							Any	=						MultilingualCLIP(__A      )
							UpperCamelCase_:							Tuple	=						text_encoder.eval()

							return text_encoder

			@property
			def       lowerCAmelCase__	(       self :       List[str]      ):
							torch.manual_seed(0      )

							UpperCamelCase_:							str	=						{
							    """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_:							Optional[int]	=						UNetaDConditionModel(**__A      )
							return model

			@property
			def       lowerCAmelCase__	(       self :       Any      ):
							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       lowerCAmelCase__	(       self :       str      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							List[str]	=						VQModel(**self.dummy_movq_kwargs      )
							return model

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							str	=						self.dummy_text_encoder
							UpperCamelCase_:							List[Any]	=						self.dummy_tokenizer
							UpperCamelCase_:							int	=						self.dummy_unet
							UpperCamelCase_:							List[str]	=						self.dummy_movq

							UpperCamelCase_:							List[str]	=						DDIMScheduler(
							    num_train_timesteps=1000						,	beta_schedule="""linear"""						,	beta_start=0.0_0085						,	beta_end=0.012						,	clip_sample=__A						,	set_alpha_to_one=__A						,	steps_offset=1						,	prediction_type="""epsilon"""						,	thresholding=__A						,	)

							UpperCamelCase_:							Optional[int]	=						{
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components

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

							if str(__A      ).startswith("""mps"""      ):
											UpperCamelCase_:							str	=						torch.manual_seed(__A      )
							else:
											UpperCamelCase_:							int	=						torch.Generator(device=__A      ).manual_seed(__A      )
							UpperCamelCase_:							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       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Dict	=						"""cpu"""

							UpperCamelCase_:							List[str]	=						self.get_dummy_components()

							UpperCamelCase_:							Any	=						self.pipeline_class(**__A      )
							UpperCamelCase_:							Optional[int]	=						pipe.to(__A      )

							pipe.set_progress_bar_config(disable=__A      )

							UpperCamelCase_:							List[Any]	=						pipe(**self.get_dummy_inputs(__A      )      )
							UpperCamelCase_:							List[Any]	=						output.images

							UpperCamelCase_:							Tuple	=						pipe(
							    **self.get_dummy_inputs(__A      )						,	return_dict=__A						,	)[0]

							UpperCamelCase_:							Tuple	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Optional[int]	=						image_from_tuple[0, -3:, -3:, -1]

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

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

							UpperCamelCase_:							Tuple	=						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       lowerCAmelCase__	(       self :       Dict      ):
							super().test_inference_batch_single_identical(expected_max_diff=3e-3      )




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






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Any      ):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							Optional[int]	=						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""      )

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

							UpperCamelCase_:							Optional[Any]	=						"""a hat"""

							UpperCamelCase_:							str	=						KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""						,	torch_dtype=torch.floataa      )
							pipe_prior.to(__A      )

							UpperCamelCase_:							str	=						KandinskyInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-inpaint"""						,	torch_dtype=torch.floataa      )
							UpperCamelCase_:							Optional[int]	=						pipeline.to(__A      )
							pipeline.set_progress_bar_config(disable=__A      )

							UpperCamelCase_:							Optional[int]	=						torch.Generator(device="""cpu"""      ).manual_seed(0      )
							UpperCamelCase_,   UpperCamelCase_:							Tuple	=						pipe_prior(
							    __A						,	generator=__A						,	num_inference_steps=5						,	negative_prompt=""""""						,	).to_tuple()

							UpperCamelCase_:							Tuple	=						pipeline(
							    __A						,	image=__A						,	mask_image=__A						,	image_embeds=__A						,	negative_image_embeds=__A						,	generator=__A						,	num_inference_steps=100						,	height=768						,	width=768						,	output_type="""np"""						,	)

							UpperCamelCase_:							Any	=						output.images[0]

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

							assert_mean_pixel_difference(__A						,	__A      )








def        A__					(						lowerCamelCase = 50						)  ->	int:
				UpperCamelCase_:							List[Any]	=						[[0] * 3 for _ in range(length + 1						)]

				for row_length in range(length + 1						):
								for tile_length in range(2     ,    5						):
												for tile_start in range(row_length - tile_length + 1						):
																different_colour_ways_number[row_length][tile_length - 2] += (
																    different_colour_ways_number[row_length - tile_start - tile_length][
																        tile_length - 2
																    ]
																    + 1
																)

				return sum(different_colour_ways_number[length]						)


if __name__ == "__main__":
							print(F"""{solution() = }""")






