




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     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							a    =			tempfile.mkdtemp()
							a    =			SamImageProcessor()
							a    =			SamProcessor(__lowerCamelCase							)
							processor.save_pretrained(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      **__lowerCamelCase				:     Dict							)					->     str:
							return AutoProcessor.from_pretrained(self.tmpdirname					,      **__lowerCamelCase							).image_processor





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							shutil.rmtree(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     str:
							a    =			[np.random.randint(2_55					,      size=(3, 30, 4_00)					,      dtype=np.uinta							)]

							a    =			[Image.fromarray(np.moveaxis(__lowerCamelCase					,      0					,      -1							)							) for x in image_inputs]

							return image_inputs





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     str:
							a    =			SamProcessor(image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							a    =			self.get_image_processor(do_normalize=__lowerCamelCase					,      padding_value=1.0							)

							a    =			SamProcessor.from_pretrained(self.tmpdirname					,      do_normalize=__lowerCamelCase					,      padding_value=1.0							)

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





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Any:
							a    =			self.get_image_processor()

							a    =			SamProcessor(image_processor=__lowerCamelCase							)

							a    =			self.prepare_image_inputs()

							a    =			image_processor(__lowerCamelCase					,      return_tensors="np"							)
							a    =			processor(images=__lowerCamelCase					,      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 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[int]:
							a    =			self.get_image_processor()

							a    =			SamProcessor(image_processor=__lowerCamelCase							)
							a    =			[torch.ones((1, 3, 5, 5)							)]

							a    =			[[17_64, 26_46]]

							a    =			[[6_83, 10_24]]
							a    =			processor.post_process_masks(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
							self.assertEqual(masks[0].shape					,      (1, 3, 17_64, 26_46)							)

							a    =			processor.post_process_masks(
							    __lowerCamelCase					,      torch.tensor(__lowerCamelCase							)					,      torch.tensor(__lowerCamelCase							)							)
							self.assertEqual(masks[0].shape					,      (1, 3, 17_64, 26_46)							)

							# should also work with np
							a    =			[np.ones((1, 3, 5, 5)							)]
							a    =			processor.post_process_masks(__lowerCamelCase					,      np.array(__lowerCamelCase							)					,      np.array(__lowerCamelCase							)							)

							self.assertEqual(masks[0].shape					,      (1, 3, 17_64, 26_46)							)

							a    =			[[1, 0], [0, 1]]
							with self.assertRaises(__lowerCamelCase							):
										a    =			processor.post_process_masks(__lowerCamelCase					,      np.array(__lowerCamelCase							)					,      np.array(__lowerCamelCase							)							)




@require_vision
@require_tf
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     str:
							a    =			tempfile.mkdtemp()
							a    =			SamImageProcessor()
							a    =			SamProcessor(__lowerCamelCase							)
							processor.save_pretrained(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      **__lowerCamelCase				:     Optional[Any]							)					->     Dict:
							return AutoProcessor.from_pretrained(self.tmpdirname					,      **__lowerCamelCase							).image_processor





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Tuple:
							shutil.rmtree(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     str:
							a    =			[np.random.randint(2_55					,      size=(3, 30, 4_00)					,      dtype=np.uinta							)]

							a    =			[Image.fromarray(np.moveaxis(__lowerCamelCase					,      0					,      -1							)							) for x in image_inputs]

							return image_inputs





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							a    =			SamProcessor(image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							a    =			self.get_image_processor(do_normalize=__lowerCamelCase					,      padding_value=1.0							)

							a    =			SamProcessor.from_pretrained(self.tmpdirname					,      do_normalize=__lowerCamelCase					,      padding_value=1.0							)

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





				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     Optional[Any]:
							a    =			self.get_image_processor()

							a    =			SamProcessor(image_processor=__lowerCamelCase							)

							a    =			self.prepare_image_inputs()

							a    =			image_processor(__lowerCamelCase					,      return_tensors="np"							)
							a    =			processor(images=__lowerCamelCase					,      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 							__UpperCAmelCase							(      self				:     List[Any]							)					->     str:
							a    =			self.get_image_processor()

							a    =			SamProcessor(image_processor=__lowerCamelCase							)
							a    =			[tf.ones((1, 3, 5, 5)							)]

							a    =			[[17_64, 26_46]]

							a    =			[[6_83, 10_24]]
							a    =			processor.post_process_masks(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      return_tensors="tf"							)
							self.assertEqual(masks[0].shape					,      (1, 3, 17_64, 26_46)							)

							a    =			processor.post_process_masks(
							    __lowerCamelCase					,      tf.convert_to_tensor(__lowerCamelCase							)					,      tf.convert_to_tensor(__lowerCamelCase							)					,      return_tensors="tf"					,      )
							self.assertEqual(masks[0].shape					,      (1, 3, 17_64, 26_46)							)

							# should also work with np
							a    =			[np.ones((1, 3, 5, 5)							)]
							a    =			processor.post_process_masks(
							    __lowerCamelCase					,      np.array(__lowerCamelCase							)					,      np.array(__lowerCamelCase							)					,      return_tensors="tf"							)

							self.assertEqual(masks[0].shape					,      (1, 3, 17_64, 26_46)							)

							a    =			[[1, 0], [0, 1]]
							with self.assertRaises(tf.errors.InvalidArgumentError							):
										a    =			processor.post_process_masks(
										    __lowerCamelCase					,      np.array(__lowerCamelCase							)					,      np.array(__lowerCamelCase							)					,      return_tensors="tf"							)




@require_vision
@require_torchvision
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Any							)					->     str:
							a    =			tempfile.mkdtemp()
							a    =			SamImageProcessor()
							a    =			SamProcessor(__lowerCamelCase							)
							processor.save_pretrained(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      **__lowerCamelCase				:     Optional[int]							)					->     Dict:
							return AutoProcessor.from_pretrained(self.tmpdirname					,      **__lowerCamelCase							).image_processor





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[Any]:
							shutil.rmtree(self.tmpdirname							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Union[str, Any]:
							a    =			[np.random.randint(2_55					,      size=(3, 30, 4_00)					,      dtype=np.uinta							)]

							a    =			[Image.fromarray(np.moveaxis(__lowerCamelCase					,      0					,      -1							)							) for x in image_inputs]

							return image_inputs





				@is_pt_tf_cross_test
				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Dict:
							a    =			self.get_image_processor()

							a    =			SamProcessor(image_processor=__lowerCamelCase							)
							a    =			np.random.randint(0					,      2					,      size=(1, 3, 5, 5)							).astype(np.floataa							)
							a    =			[tf.convert_to_tensor(__lowerCamelCase							)]
							a    =			[torch.tensor(__lowerCamelCase							)]

							a    =			[[17_64, 26_46]]

							a    =			[[6_83, 10_24]]
							a    =			processor.post_process_masks(
							    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      return_tensors="tf"							)
							a    =			processor.post_process_masks(
							    __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      return_tensors="pt"							)

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





				@is_pt_tf_cross_test
				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     str:
							a    =			self.get_image_processor()

							a    =			SamProcessor(image_processor=__lowerCamelCase							)

							a    =			self.prepare_image_inputs()

							a    =			image_processor(__lowerCamelCase					,      return_tensors="pt"							)["pixel_values"].numpy()
							a    =			processor(images=__lowerCamelCase					,      return_tensors="pt"							)["pixel_values"].numpy()

							a    =			image_processor(__lowerCamelCase					,      return_tensors="tf"							)["pixel_values"].numpy()
							a    =			processor(images=__lowerCamelCase					,      return_tensors="tf"							)["pixel_values"].numpy()

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












from bisect import bisect
from itertools import accumulate




def 			__magic_name__				( A		:					Optional[Any],  A		:					List[str],  A		:					Tuple,  A		:					Optional[Any]   ):






			'''simple docstring'''







			a    =			sorted(zip(A,  A   ),  key=lambda A   : x[0] / x[1],  reverse=A   )
			a    ,		a    =			[i[0] for i in r], [i[1] for i in r]
			a    =			list(accumulate(A   )   )
			a    =			bisect(A,  A   )
			return (
			    0
			    if k == 0
			    else sum(vl[:k]   ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
			    if k != n
			    else sum(vl[:k]   )
			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






