'''simple docstring'''

import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						return (data["data"], data["target"])



def 							UpperCamelCase__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=XGBRegressor(verbosity=0			,      random_state=4_2      )
						xgb.fit(a__			,      a__      )
						# Predict target for test data
						_lowerCAmelCase										=xgb.predict(a__      )
						_lowerCAmelCase										=predictions.reshape(len(a__      )			,      1      )
						return predictions



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=fetch_california_housing()
						_lowerCAmelCase						,				_lowerCAmelCase										=data_handling(a__      )
						_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=train_test_split(
						    a__			,      a__			,      test_size=0.25			,      random_state=1      )
						_lowerCAmelCase										=xgboost(a__			,      a__			,      a__      )
						# Error printing
						print(F'''Mean Absolute Error : {mean_absolute_error(a__			,      a__      )}'''      )
						print(F'''Mean Square Error  : {mean_squared_error(a__			,      a__      )}'''      )


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)
							main()






'''simple docstring'''

import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      List[Any]       =							IFPipeline
		lowercase   :      Tuple       =							TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
		lowercase   :      Union[str, Any]       =							TEXT_TO_IMAGE_BATCH_PARAMS
		lowercase   :      int       =							PipelineTesterMixin.required_optional_params - {'latents'}






		def 		UpperCamelCase__       (   self				) ->     str:
								return self._get_dummy_components()






		def 		UpperCamelCase__       (   self		,						__A		,						__A=0				) ->     int:
								if str(__A				).startswith('mps'				):
														_lowerCAmelCase										=torch.manual_seed(__A				)
								else:
														_lowerCAmelCase										=torch.Generator(device=__A				).manual_seed(__A				)

								_lowerCAmelCase										={
								    'prompt': 'A painting of a squirrel eating a burger',
								    'generator': generator,
								    'num_inference_steps': 2,
								    'output_type': 'numpy',
								}

								return inputs






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






		@unittest.skipIf(torch_device != 'cuda'		,						reason='float16 requires CUDA'				)
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1				)






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)






		def 		UpperCamelCase__       (   self				) ->     str:
								self._test_save_load_local()






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2		,						)






		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()		,						reason='XFormers attention is only available with CUDA and `xformers` installed'		,						)
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)

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







		"""simple docstring"""






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






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

								_lowerCAmelCase										=IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'		,						variant='fp16'		,						torch_dtype=torch.floataa				)

								_lowerCAmelCase										=IFSuperResolutionPipeline.from_pretrained(
								    'DeepFloyd/IF-II-L-v1.0'		,						variant='fp16'		,						torch_dtype=torch.floataa		,						text_encoder=__A		,						tokenizer=__A				)

								# pre compute text embeddings and remove T5 to save memory

								pipe_a.text_encoder.to('cuda'				)

								_lowerCAmelCase						,				_lowerCAmelCase										=pipe_a.encode_prompt('anime turtle'		,						device='cuda'				)

								del pipe_a.tokenizer
								del pipe_a.text_encoder
								gc.collect()

								_lowerCAmelCase										=None
								_lowerCAmelCase										=None

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

								self._test_if(__A		,						__A		,						__A		,						__A				)

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# img2img

								_lowerCAmelCase										=IFImgaImgPipeline(**pipe_a.components				)
								_lowerCAmelCase										=IFImgaImgSuperResolutionPipeline(**pipe_a.components				)

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

								self._test_if_imgaimg(__A		,						__A		,						__A		,						__A				)

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# inpainting

								_lowerCAmelCase										=IFInpaintingPipeline(**pipe_a.components				)
								_lowerCAmelCase										=IFInpaintingSuperResolutionPipeline(**pipe_a.components				)

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()				)

								self._test_if_inpainting(__A		,						__A		,						__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A				) ->     str:
								# pipeline 1

								_start_torch_memory_measurement()

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)
								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						num_inference_steps=2		,						generator=__A		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

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

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 13 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)

								# pipeline 2

								_start_torch_memory_measurement()

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)

								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)

								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						generator=__A		,						num_inference_steps=2		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (256, 256, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								# pipeline 1

								_start_torch_memory_measurement()

								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)

								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						num_inference_steps=2		,						generator=__A		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

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

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)

								# pipeline 2

								_start_torch_memory_measurement()

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)

								_lowerCAmelCase										=floats_tensor((1, 3, 256, 256)		,						rng=random.Random(0				)				).to(__A				)
								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)

								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						original_image=__A		,						generator=__A		,						num_inference_steps=2		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (256, 256, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A				) ->     Dict:
								# pipeline 1

								_start_torch_memory_measurement()

								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)
								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(1				)				).to(__A				)

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)
								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						mask_image=__A		,						num_inference_steps=2		,						generator=__A		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

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

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)

								# pipeline 2

								_start_torch_memory_measurement()

								_lowerCAmelCase										=torch.Generator(device='cpu'				).manual_seed(0				)

								_lowerCAmelCase										=floats_tensor((1, 3, 64, 64)		,						rng=random.Random(0				)				).to(__A				)
								_lowerCAmelCase										=floats_tensor((1, 3, 256, 256)		,						rng=random.Random(0				)				).to(__A				)
								_lowerCAmelCase										=floats_tensor((1, 3, 256, 256)		,						rng=random.Random(1				)				).to(__A				)

								_lowerCAmelCase										=pipe_a(
								    prompt_embeds=__A		,						negative_prompt_embeds=__A		,						image=__A		,						mask_image=__A		,						original_image=__A		,						generator=__A		,						num_inference_steps=2		,						output_type='np'		,						)

								_lowerCAmelCase										=output.images[0]

								assert image.shape == (256, 256, 3)

								_lowerCAmelCase										=torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								_lowerCAmelCase										=load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'				)
								assert_mean_pixel_difference(__A		,						__A				)



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()





