

'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class   UpperCamelCase__   (       lowercase__							,							unittest.TestCase       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Tuple       =							PaintByExamplePipeline
		SCREAMING_SNAKE_CASE__  :     Optional[Any]       =							IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
		SCREAMING_SNAKE_CASE__  :     Optional[Any]       =							IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		SCREAMING_SNAKE_CASE__  :     int       =							frozenset([]       )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







		def 				A_	(			self      ):


							'''simple docstring'''

							torch.manual_seed(0      )
							UpperCAmelCase		:							Any        =       UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)   ,      layers_per_block=2   ,      sample_size=3_2   ,      in_channels=9   ,      out_channels=4   ,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,      cross_attention_dim=3_2   ,      )
							UpperCAmelCase		:							Union[str, Any]        =       PNDMScheduler(skip_prk_steps=snake_case      )
							torch.manual_seed(0      )
							UpperCAmelCase		:							Any        =       AutoencoderKL(
							    block_out_channels=[3_2, 6_4]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,      latent_channels=4   ,      )
							torch.manual_seed(0      )
							UpperCAmelCase		:							Dict        =       CLIPVisionConfig(
							    hidden_size=3_2   ,      projection_dim=3_2   ,      intermediate_size=3_7   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      image_size=3_2   ,      patch_size=4   ,      )
							UpperCAmelCase		:							Optional[Any]        =       PaintByExampleImageEncoder(snake_case   ,      proj_size=3_2      )
							UpperCAmelCase		:							List[Any]        =       CLIPImageProcessor(crop_size=3_2   ,      size=3_2      )

							UpperCAmelCase		:							str        =       {
							    "unet": unet,
							    "scheduler": scheduler,
							    "vae": vae,
							    "image_encoder": image_encoder,
							    "safety_checker": None,
							    "feature_extractor": feature_extractor,
							}
							return components







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       np.array(image.convert("RGB"      )      )
							UpperCAmelCase		:							Optional[int]        =       image[None].transpose(0   ,      3   ,      1   ,      2      )
							UpperCAmelCase		:							Optional[int]        =       torch.from_numpy(snake_case      ).to(dtype=torch.floataa      ) / 127.5 - 1.0
							return image







		def 				A_	(			self   ,      snake_case="cpu"   ,      snake_case=0      ):


							'''simple docstring'''

							UpperCAmelCase		:							str        =       floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(snake_case      )      ).to(snake_case      )
							UpperCAmelCase		:							str        =       image.cpu().permute(0   ,      2   ,      3   ,      1      )[0]
							UpperCAmelCase		:							int        =       Image.fromarray(np.uinta(snake_case      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
							UpperCAmelCase		:							Union[str, Any]        =       Image.fromarray(np.uinta(image + 4      )      ).convert("RGB"      ).resize((6_4, 6_4)      )
							UpperCAmelCase		:							int        =       Image.fromarray(np.uinta(snake_case      )      ).convert("RGB"      ).resize((3_2, 3_2)      )

							if str(snake_case      ).startswith("mps"      ):
												UpperCAmelCase		:							Tuple        =       torch.manual_seed(snake_case      )
							else:
												UpperCAmelCase		:							Optional[Any]        =       torch.Generator(device=snake_case      ).manual_seed(snake_case      )
							UpperCAmelCase		:							Dict        =       {
							    "example_image": example_image,
							    "image": init_image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "guidance_scale": 6.0,
							    "output_type": "numpy",
							}
							return inputs







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							UpperCAmelCase		:							int        =       PaintByExamplePipeline(**snake_case      )
							UpperCAmelCase		:							int        =       pipe.to("cpu"      )
							pipe.set_progress_bar_config(disable=snake_case      )

							UpperCAmelCase		:							Union[str, Any]        =       self.get_dummy_inputs()
							UpperCAmelCase		:							Optional[int]        =       pipe(**snake_case      )
							UpperCAmelCase		:							Tuple        =       output.images

							UpperCAmelCase		:							Tuple        =       image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							UpperCAmelCase		:							List[Any]        =       np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       "cpu"
							UpperCAmelCase		:							str        =       self.get_dummy_inputs()
							inputs.pop("mask_image"      )
							UpperCAmelCase		:							Optional[Any]        =       self.convert_to_pt(inputs.pop("image"      )      )
							UpperCAmelCase		:							int        =       image.clamp(0   ,      1      ) / 2

							# make sure here that pndm scheduler skips prk
							UpperCAmelCase		:							Tuple        =       PaintByExamplePipeline(**self.get_dummy_components()      )
							UpperCAmelCase		:							Optional[Any]        =       pipe.to(snake_case      )
							pipe.set_progress_bar_config(disable=snake_case      )

							UpperCAmelCase		:							Union[str, Any]        =       pipe(image=snake_case   ,      mask_image=mask_image[:, 0]   ,      **snake_case      )
							UpperCAmelCase		:							Any        =       output.images

							UpperCAmelCase		:							Optional[int]        =       image.cpu().permute(0   ,      2   ,      3   ,      1      )[0]
							UpperCAmelCase		:							List[str]        =       mask_image.cpu().permute(0   ,      2   ,      3   ,      1      )[0]

							UpperCAmelCase		:							List[Any]        =       Image.fromarray(np.uinta(snake_case      )      ).convert("RGB"      )
							UpperCAmelCase		:							str        =       Image.fromarray(np.uinta(snake_case      )      ).convert("RGB"      )

							UpperCAmelCase		:							int        =       pipe(**self.get_dummy_inputs()      )
							UpperCAmelCase		:							Optional[int]        =       output.images

							assert out_a.shape == (1, 6_4, 6_4, 3)
							assert np.abs(out_a.flatten() - out_a.flatten()      ).max() < 5e-2







		def 				A_	(			self      ):


							'''simple docstring'''

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







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


		"""simple docstring"""

		def 				A_	(			self      ):


							'''simple docstring'''

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







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/dog_in_bucket.png"      )
							UpperCAmelCase		:							Union[str, Any]        =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/mask.png"      )
							UpperCAmelCase		:							Any        =       load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/paint_by_example/panda.jpg"      )

							UpperCAmelCase		:							Any        =       PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"      )
							UpperCAmelCase		:							Optional[Any]        =       pipe.to(snake_case      )
							pipe.set_progress_bar_config(disable=snake_case      )

							UpperCAmelCase		:							Dict        =       torch.manual_seed(3_2_1      )
							UpperCAmelCase		:							str        =       pipe(
							    image=snake_case   ,      mask_image=snake_case   ,      example_image=snake_case   ,      generator=snake_case   ,      guidance_scale=5.0   ,      num_inference_steps=5_0   ,      output_type="np"   ,      )

							UpperCAmelCase		:							int        =       output.images

							UpperCAmelCase		:							Optional[int]        =       image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							UpperCAmelCase		:							str        =       np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]      )

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2



'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a       :      List[Any]  	=			load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)
