






import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	lowercase  							=					False

if is_vision_available():
	from PIL import Image

	from transformers import PixaStructImageProcessor




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=7       ,			a=3       ,			a=18       ,			a=30       ,			a=4_00       ,			a=None       ,			a=True       ,			a=True       ,			a=None       ,			)       ->    Union[str, Any]:
								snake_case_				=							size if size is not None else {'height': 20, 'width': 20}
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							num_channels
								snake_case_				=							image_size
								snake_case_				=							min_resolution
								snake_case_				=							max_resolution
								snake_case_				=							size
								snake_case_				=							do_normalize
								snake_case_				=							do_convert_rgb
								snake_case_				=							[5_12, 10_24, 20_48, 40_96]
								snake_case_				=							patch_size if patch_size is not None else {'height': 16, 'width': 16}




	def 							_UpperCamelCase			(       self					)       ->    str:
								return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
								snake_case_				=							Image.open(requests.get(a       ,			stream=a					).raw					).convert('RGB'					)
								return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,					reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''       ,					)
@require_torch
@require_vision
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= PixaStructImageProcessor if is_vision_available() else None




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							PixaStructImageProcessingTester(self					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(a       ,			'do_normalize'					)					)
								self.assertTrue(hasattr(a       ,			'do_convert_rgb'					)					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							self.image_processor_tester.prepare_dummy_image()

								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								snake_case_				=							20_48

								snake_case_				=							image_processor(a       ,			return_tensors='pt'       ,			max_patches=a					)
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,			torch.tensor(0.0_606					)       ,			atol=1E-3       ,			rtol=1E-3					)					)




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								# Initialize image_processor
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								snake_case_				=							prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=a					)
								for image in image_inputs:
															self.assertIsInstance(a       ,			Image.Image					)

								# Test not batched input
								snake_case_				=							(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															snake_case_				=							image_processor(
															    image_inputs[0]       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(1, max_patch, expected_hidden_dim)       ,			)

															# Test batched
															snake_case_				=							image_processor(
															    a       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,			)




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								# Initialize image_processor
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								snake_case_				=							prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=a					)
								for image in image_inputs:
															self.assertIsInstance(a       ,			Image.Image					)

								# Test not batched input
								snake_case_				=							(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								snake_case_				=							True

								for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															with self.assertRaises(a					):
																						snake_case_				=							image_processor(
																						    image_inputs[0]       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches

															snake_case_				=							'Hello'

															snake_case_				=							image_processor(
															    image_inputs[0]       ,			return_tensors='pt'       ,			max_patches=a       ,			header_text=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(1, max_patch, expected_hidden_dim)       ,			)

															# Test batched
															snake_case_				=							image_processor(
															    a       ,			return_tensors='pt'       ,			max_patches=a       ,			header_text=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,			)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								# Initialize image_processor
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								snake_case_				=							prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=a       ,			numpify=a					)
								for image in image_inputs:
															self.assertIsInstance(a       ,			np.ndarray					)

								snake_case_				=							(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															snake_case_				=							image_processor(
															    image_inputs[0]       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(1, max_patch, expected_hidden_dim)       ,			)

															# Test batched
															snake_case_				=							image_processor(
															    a       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,			)






	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								# Initialize image_processor
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								snake_case_				=							prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=a       ,			torchify=a					)
								for image in image_inputs:
															self.assertIsInstance(a       ,			torch.Tensor					)

								# Test not batched input
								snake_case_				=							(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															snake_case_				=							image_processor(
															    image_inputs[0]       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(1, max_patch, expected_hidden_dim)       ,			)

															# Test batched
															snake_case_				=							image_processor(
															    a       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,			)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11       ,					reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''       ,					)
@require_torch
@require_vision
class  UpperCamelCase_      (			snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= PixaStructImageProcessor if is_vision_available() else None




	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							PixaStructImageProcessingTester(self       ,			num_channels=4					)
								snake_case_				=							3




	@property
	def 							_UpperCamelCase			(       self					)       ->    Any:
								return self.image_processor_tester.prepare_image_processor_dict()




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(a       ,			'do_normalize'					)					)
								self.assertTrue(hasattr(a       ,			'do_convert_rgb'					)					)






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								# Initialize image_processor
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								snake_case_				=							prepare_image_inputs(self.image_processor_tester       ,			equal_resolution=a					)
								for image in image_inputs:
															self.assertIsInstance(a       ,			Image.Image					)

								# Test not batched input
								snake_case_				=							(
								    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
								    * (self.image_processor_tester.num_channels - 1)
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
															# Test not batched input
															snake_case_				=							image_processor(
															    image_inputs[0]       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(1, max_patch, expected_hidden_dim)       ,			)

															# Test batched
															snake_case_				=							image_processor(
															    a       ,			return_tensors='pt'       ,			max_patches=a					).flattened_patches
															self.assertEqual(
															    encoded_images.shape       ,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,			)













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





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							XGBRegressor(verbosity=0							,     random_state=42)
							xgb.fit(a_							,     a_)
							# Predict target for test data
							snake_case_				=							xgb.predict(a_)
							snake_case_				=							predictions.reshape(len(a_)							,     1)
							return predictions





def 						__UpperCAmelCase			(     ):
							snake_case_				=							fetch_california_housing()
							snake_case_			,		snake_case_				=							data_handling(a_)
							snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							train_test_split(
							    a_							,     a_							,     test_size=0.25							,     random_state=1)
							snake_case_				=							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()





