
import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
					import torch

if is_tf_available():
					import tensorflow as tf







@require_vision
@require_torchvision
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							tempfile.mkdtemp()
					A__  :	Optional[Any]         =							SamImageProcessor()
					A__  :	List[str]         =							SamProcessor(UpperCamelCase__		)
					processor.save_pretrained(self.tmpdirname		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return AutoProcessor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		).image_processor





	def   __snake_case				(			self		):
					shutil.rmtree(self.tmpdirname		)





	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							[np.random.randint(255				,       size=(3, 30, 400)				,       dtype=np.uinta		)]

					A__  :	Optional[int]         =							[Image.fromarray(np.moveaxis(UpperCamelCase__				,       0				,       -1		)		) for x in image_inputs]

					return image_inputs





	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							SamProcessor(image_processor=self.get_image_processor()		)
					processor.save_pretrained(self.tmpdirname		)

					A__  :	str         =							self.get_image_processor(do_normalize=UpperCamelCase__				,       padding_value=1.0		)

					A__  :	List[str]         =							SamProcessor.from_pretrained(self.tmpdirname				,       do_normalize=UpperCamelCase__				,       padding_value=1.0		)

					self.assertEqual(processor.image_processor.to_json_string()				,       image_processor_add_kwargs.to_json_string()		)
					self.assertIsInstance(processor.image_processor				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	int         =							self.get_image_processor()

					A__  :	Optional[Any]         =							SamProcessor(image_processor=UpperCamelCase__		)

					A__  :	str         =							self.prepare_image_inputs()

					A__  :	Dict         =							image_processor(UpperCamelCase__				,       return_tensors='''np'''		)
					A__  :	Dict         =							processor(images=UpperCamelCase__				,       return_tensors='''np'''		)

					input_feat_extract.pop('''original_sizes'''		)  # pop original_sizes as it is popped in the processor
					input_feat_extract.pop('''reshaped_input_sizes'''		)  # pop original_sizes as it is popped in the processor

					for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()				,       input_processor[key].sum()				,       delta=1e-2		)





	@require_torch
	def   __snake_case				(			self		):
					A__  :	str         =							self.get_image_processor()

					A__  :	Tuple         =							SamProcessor(image_processor=UpperCamelCase__		)
					A__  :	Any         =							[torch.ones((1, 3, 5, 5)		)]

					A__  :	Union[str, Any]         =							[[1764, 2646]]

					A__  :	Optional[int]         =							[[683, 1024]]
					A__  :	int         =							processor.post_process_masks(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		)
					self.assertEqual(masks[0].shape				,       (1, 3, 1764, 2646)		)

					A__  :	List[Any]         =							processor.post_process_masks(
					    UpperCamelCase__				,       torch.tensor(UpperCamelCase__		)				,       torch.tensor(UpperCamelCase__		)		)
					self.assertEqual(masks[0].shape				,       (1, 3, 1764, 2646)		)

					# should also work with np
					A__  :	Optional[Any]         =							[np.ones((1, 3, 5, 5)		)]
					A__  :	Optional[int]         =							processor.post_process_masks(UpperCamelCase__				,       np.array(UpperCamelCase__		)				,       np.array(UpperCamelCase__		)		)

					self.assertEqual(masks[0].shape				,       (1, 3, 1764, 2646)		)

					A__  :	List[str]         =							[[1, 0], [0, 1]]
					with self.assertRaises(UpperCamelCase__		):
									A__  :	int         =							processor.post_process_masks(UpperCamelCase__				,       np.array(UpperCamelCase__		)				,       np.array(UpperCamelCase__		)		)







@require_vision
@require_tf
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							tempfile.mkdtemp()
					A__  :	List[str]         =							SamImageProcessor()
					A__  :	str         =							SamProcessor(UpperCamelCase__		)
					processor.save_pretrained(self.tmpdirname		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return AutoProcessor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		).image_processor





	def   __snake_case				(			self		):
					shutil.rmtree(self.tmpdirname		)





	def   __snake_case				(			self		):
					A__  :	Tuple         =							[np.random.randint(255				,       size=(3, 30, 400)				,       dtype=np.uinta		)]

					A__  :	Any         =							[Image.fromarray(np.moveaxis(UpperCamelCase__				,       0				,       -1		)		) for x in image_inputs]

					return image_inputs





	def   __snake_case				(			self		):
					A__  :	Tuple         =							SamProcessor(image_processor=self.get_image_processor()		)
					processor.save_pretrained(self.tmpdirname		)

					A__  :	Any         =							self.get_image_processor(do_normalize=UpperCamelCase__				,       padding_value=1.0		)

					A__  :	List[Any]         =							SamProcessor.from_pretrained(self.tmpdirname				,       do_normalize=UpperCamelCase__				,       padding_value=1.0		)

					self.assertEqual(processor.image_processor.to_json_string()				,       image_processor_add_kwargs.to_json_string()		)
					self.assertIsInstance(processor.image_processor				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.get_image_processor()

					A__  :	Optional[int]         =							SamProcessor(image_processor=UpperCamelCase__		)

					A__  :	Dict         =							self.prepare_image_inputs()

					A__  :	List[str]         =							image_processor(UpperCamelCase__				,       return_tensors='''np'''		)
					A__  :	Optional[int]         =							processor(images=UpperCamelCase__				,       return_tensors='''np'''		)

					input_feat_extract.pop('''original_sizes'''		)  # pop original_sizes as it is popped in the processor
					input_feat_extract.pop('''reshaped_input_sizes'''		)  # pop reshaped_input_sizes as it is popped in the processor

					for key in input_feat_extract.keys():
									self.assertAlmostEqual(input_feat_extract[key].sum()				,       input_processor[key].sum()				,       delta=1e-2		)





	@require_tf
	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							self.get_image_processor()

					A__  :	Optional[int]         =							SamProcessor(image_processor=UpperCamelCase__		)
					A__  :	str         =							[tf.ones((1, 3, 5, 5)		)]

					A__  :	List[Any]         =							[[1764, 2646]]

					A__  :	Tuple         =							[[683, 1024]]
					A__  :	Optional[int]         =							processor.post_process_masks(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       return_tensors='''tf'''		)
					self.assertEqual(masks[0].shape				,       (1, 3, 1764, 2646)		)

					A__  :	Dict         =							processor.post_process_masks(
					    UpperCamelCase__				,       tf.convert_to_tensor(UpperCamelCase__		)				,       tf.convert_to_tensor(UpperCamelCase__		)				,       return_tensors='''tf'''				,       )
					self.assertEqual(masks[0].shape				,       (1, 3, 1764, 2646)		)

					# should also work with np
					A__  :	str         =							[np.ones((1, 3, 5, 5)		)]
					A__  :	str         =							processor.post_process_masks(
					    UpperCamelCase__				,       np.array(UpperCamelCase__		)				,       np.array(UpperCamelCase__		)				,       return_tensors='''tf'''		)

					self.assertEqual(masks[0].shape				,       (1, 3, 1764, 2646)		)

					A__  :	Tuple         =							[[1, 0], [0, 1]]
					with self.assertRaises(tf.errors.InvalidArgumentError		):
									A__  :	str         =							processor.post_process_masks(
									    UpperCamelCase__				,       np.array(UpperCamelCase__		)				,       np.array(UpperCamelCase__		)				,       return_tensors='''tf'''		)







@require_vision
@require_torchvision
class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							tempfile.mkdtemp()
					A__  :	Any         =							SamImageProcessor()
					A__  :	Union[str, Any]         =							SamProcessor(UpperCamelCase__		)
					processor.save_pretrained(self.tmpdirname		)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					return AutoProcessor.from_pretrained(self.tmpdirname				,       **UpperCamelCase__		).image_processor





	def   __snake_case				(			self		):
					shutil.rmtree(self.tmpdirname		)





	def   __snake_case				(			self		):
					A__  :	str         =							[np.random.randint(255				,       size=(3, 30, 400)				,       dtype=np.uinta		)]

					A__  :	Dict         =							[Image.fromarray(np.moveaxis(UpperCamelCase__				,       0				,       -1		)		) for x in image_inputs]

					return image_inputs





	@is_pt_tf_cross_test
	def   __snake_case				(			self		):
					A__  :	Any         =							self.get_image_processor()

					A__  :	List[Any]         =							SamProcessor(image_processor=UpperCamelCase__		)
					A__  :	List[Any]         =							np.random.randint(0				,       2				,       size=(1, 3, 5, 5)		).astype(np.floataa		)
					A__  :	Tuple         =							[tf.convert_to_tensor(UpperCamelCase__		)]
					A__  :	Any         =							[torch.tensor(UpperCamelCase__		)]

					A__  :	Optional[int]         =							[[1764, 2646]]

					A__  :	Any         =							[[683, 1024]]
					A__  :	Optional[int]         =							processor.post_process_masks(
					    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       return_tensors='''tf'''		)
					A__  :	List[Any]         =							processor.post_process_masks(
					    UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       return_tensors='''pt'''		)

					self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()		)		)





	@is_pt_tf_cross_test
	def   __snake_case				(			self		):
					A__  :	Dict         =							self.get_image_processor()

					A__  :	Dict         =							SamProcessor(image_processor=UpperCamelCase__		)

					A__  :	Union[str, Any]         =							self.prepare_image_inputs()

					A__  :	Dict         =							image_processor(UpperCamelCase__				,       return_tensors='''pt'''		)['''pixel_values'''].numpy()
					A__  :	List[str]         =							processor(images=UpperCamelCase__				,       return_tensors='''pt'''		)['''pixel_values'''].numpy()

					A__  :	Union[str, Any]         =							image_processor(UpperCamelCase__				,       return_tensors='''tf'''		)['''pixel_values'''].numpy()
					A__  :	Union[str, Any]         =							processor(images=UpperCamelCase__				,       return_tensors='''tf'''		)['''pixel_values'''].numpy()

					self.assertTrue(np.allclose(UpperCamelCase__				,       UpperCamelCase__		)		)
					self.assertTrue(np.allclose(UpperCamelCase__				,       UpperCamelCase__		)		)
					self.assertTrue(np.allclose(UpperCamelCase__				,       UpperCamelCase__		)		)

from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


if __name__ == "__main__":
					# Load image
					with Image.open('image_data/lena.jpg') as img:
										# Change brightness to 100
										_SCREAMING_SNAKE_CASE    :      Dict          =			change_brightness(img, 1_0_0)
										brigt_img.save('image_data/lena_brightness.png', format='png')