

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    _a						(unittest.TestCase    ):


					'''simple docstring'''






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






					def   __A      (			self   ,	**A__      ):
						return AutoProcessor.from_pretrained(self.tmpdirname   ,	**A__      ).image_processor






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






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

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

						return image_inputs






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		SamProcessor(image_processor=self.get_image_processor()      )
						processor.save_pretrained(self.tmpdirname      )

						A__							:   List[str]					       =		self.get_image_processor(do_normalize=A__   ,	padding_value=1.0      )

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

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






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		self.get_image_processor()

						A__							:   Union[str, Any]					       =		SamProcessor(image_processor=A__      )

						A__							:   Tuple					       =		self.prepare_image_inputs()

						A__							:   str					       =		image_processor(A__   ,	return_tensors="""np"""      )
						A__							:   Any					       =		processor(images=A__   ,	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   __A      (			self      ):
						A__							:   Tuple					       =		self.get_image_processor()

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

						A__							:   Dict					       =		[[1764, 2646]]

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

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

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

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

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



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


					'''simple docstring'''






					def   __A      (			self      ):
						A__							:   List[str]					       =		tempfile.mkdtemp()
						A__							:   Optional[Any]					       =		SamImageProcessor()
						A__							:   Tuple					       =		SamProcessor(A__      )
						processor.save_pretrained(self.tmpdirname      )






					def   __A      (			self   ,	**A__      ):
						return AutoProcessor.from_pretrained(self.tmpdirname   ,	**A__      ).image_processor






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






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

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

						return image_inputs






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		SamProcessor(image_processor=self.get_image_processor()      )
						processor.save_pretrained(self.tmpdirname      )

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

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

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






					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		self.get_image_processor()

						A__							:   List[Any]					       =		SamProcessor(image_processor=A__      )

						A__							:   Dict					       =		self.prepare_image_inputs()

						A__							:   List[Any]					       =		image_processor(A__   ,	return_tensors="""np"""      )
						A__							:   str					       =		processor(images=A__   ,	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   __A      (			self      ):
						A__							:   Union[str, Any]					       =		self.get_image_processor()

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

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

						A__							:   List[Any]					       =		[[683, 1024]]
						A__							:   Tuple					       =		processor.post_process_masks(A__   ,	A__   ,	A__   ,	return_tensors="""tf"""      )
						self.assertEqual(masks[0].shape   ,	(1, 3, 1764, 2646)      )

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

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

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

						A__							:   Optional[int]					       =		[[1, 0], [0, 1]]
						with self.assertRaises(tf.errors.InvalidArgumentError      ):
							A__							:   str					       =		processor.post_process_masks(
							    A__   ,	np.array(A__      )   ,	np.array(A__      )   ,	return_tensors="""tf"""      )



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


					'''simple docstring'''






					def   __A      (			self      ):
						A__							:   str					       =		tempfile.mkdtemp()
						A__							:   str					       =		SamImageProcessor()
						A__							:   Optional[Any]					       =		SamProcessor(A__      )
						processor.save_pretrained(self.tmpdirname      )






					def   __A      (			self   ,	**A__      ):
						return AutoProcessor.from_pretrained(self.tmpdirname   ,	**A__      ).image_processor






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






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

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

						return image_inputs






					@is_pt_tf_cross_test
					def   __A      (			self      ):
						A__							:   str					       =		self.get_image_processor()

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

						A__							:   int					       =		[[1764, 2646]]

						A__							:   List[Any]					       =		[[683, 1024]]
						A__							:   str					       =		processor.post_process_masks(
						    A__   ,	A__   ,	A__   ,	return_tensors="""tf"""      )
						A__							:   List[Any]					       =		processor.post_process_masks(
						    A__   ,	A__   ,	A__   ,	return_tensors="""pt"""      )

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






					@is_pt_tf_cross_test
					def   __A      (			self      ):
						A__							:   List[Any]					       =		self.get_image_processor()

						A__							:   int					       =		SamProcessor(image_processor=A__      )

						A__							:   Tuple					       =		self.prepare_image_inputs()

						A__							:   Union[str, Any]					       =		image_processor(A__   ,	return_tensors="""pt"""      )["""pixel_values"""].numpy()
						A__							:   Optional[int]					       =		processor(images=A__   ,	return_tensors="""pt"""      )["""pixel_values"""].numpy()

						A__							:   List[str]					       =		image_processor(A__   ,	return_tensors="""tf"""      )["""pixel_values"""].numpy()
						A__							:   Optional[Any]					       =		processor(images=A__   ,	return_tensors="""tf"""      )["""pixel_values"""].numpy()

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






from __future__ import annotations

def 						UpperCamelCase (lowercase_:			list[int]   ,				lowercase_:			list[int]   ,				lowercase_:			int )					->				tuple[float, list[float]]:
	A__							:   Tuple					       =		list(range(len(lowercase_ ) ) )
	A__							:   Union[str, Any]					       =		[v / w for v, w in zip(lowercase_   ,				lowercase_ )]
	index.sort(key=lambda lowercase_ : ratio[i]   ,				reverse=lowercase_ )

	A__							:   float					       =		0
	A__							:   list[float]					       =		[0] * len(lowercase_ )
	for i in index:
		if weight[i] <= capacity:
			A__							:   Optional[int]					       =		1
			max_value += value[i]
			capacity -= weight[i]
		else:
			A__							:   Union[str, Any]					       =		capacity / weight[i]
			max_value += value[i] * capacity / weight[i]
			break

	return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()



