
"""simple docstring"""







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 							__magic_name__     (		unittest.TestCase   ):





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       tempfile.mkdtemp()
												__snake_case     			=       SamImageProcessor()
												__snake_case     			=       SamProcessor(snake_case_    )
												processor.save_pretrained(self.tmpdirname    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						**snake_case_   :							Optional[int]    ):
												return AutoProcessor.from_pretrained(self.tmpdirname							,						**snake_case_    ).image_processor





					def 							lowerCAmelCase      (				self   :							int    ):
												shutil.rmtree(self.tmpdirname    )





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

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

												return image_inputs





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       SamProcessor(image_processor=self.get_image_processor()    )
												processor.save_pretrained(self.tmpdirname    )

												__snake_case     			=       self.get_image_processor(do_normalize=snake_case_							,						padding_value=1.0    )

												__snake_case     			=       SamProcessor.from_pretrained(self.tmpdirname							,						do_normalize=snake_case_							,						padding_value=1.0    )

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





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       self.get_image_processor()

												__snake_case     			=       SamProcessor(image_processor=snake_case_    )

												__snake_case     			=       self.prepare_image_inputs()

												__snake_case     			=       image_processor(snake_case_							,						return_tensors="np"    )
												__snake_case     			=       processor(images=snake_case_							,						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 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       self.get_image_processor()

												__snake_case     			=       SamProcessor(image_processor=snake_case_    )
												__snake_case     			=       [torch.ones((1, 3, 5, 5)    )]

												__snake_case     			=       [[1764, 2646]]

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

												__snake_case     			=       processor.post_process_masks(
												    snake_case_							,						torch.tensor(snake_case_    )							,						torch.tensor(snake_case_    )    )
												self.assertEqual(masks[0].shape							,						(1, 3, 1764, 2646)    )

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

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

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







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





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       tempfile.mkdtemp()
												__snake_case     			=       SamImageProcessor()
												__snake_case     			=       SamProcessor(snake_case_    )
												processor.save_pretrained(self.tmpdirname    )





					def 							lowerCAmelCase      (				self   :							int							,						**snake_case_   :							str    ):
												return AutoProcessor.from_pretrained(self.tmpdirname							,						**snake_case_    ).image_processor





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												shutil.rmtree(self.tmpdirname    )





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

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

												return image_inputs





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       SamProcessor(image_processor=self.get_image_processor()    )
												processor.save_pretrained(self.tmpdirname    )

												__snake_case     			=       self.get_image_processor(do_normalize=snake_case_							,						padding_value=1.0    )

												__snake_case     			=       SamProcessor.from_pretrained(self.tmpdirname							,						do_normalize=snake_case_							,						padding_value=1.0    )

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





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       self.get_image_processor()

												__snake_case     			=       SamProcessor(image_processor=snake_case_    )

												__snake_case     			=       self.prepare_image_inputs()

												__snake_case     			=       image_processor(snake_case_							,						return_tensors="np"    )
												__snake_case     			=       processor(images=snake_case_							,						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 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       self.get_image_processor()

												__snake_case     			=       SamProcessor(image_processor=snake_case_    )
												__snake_case     			=       [tf.ones((1, 3, 5, 5)    )]

												__snake_case     			=       [[1764, 2646]]

												__snake_case     			=       [[683, 1024]]
												__snake_case     			=       processor.post_process_masks(snake_case_							,						snake_case_							,						snake_case_							,						return_tensors="tf"    )
												self.assertEqual(masks[0].shape							,						(1, 3, 1764, 2646)    )

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

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

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

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







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





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       tempfile.mkdtemp()
												__snake_case     			=       SamImageProcessor()
												__snake_case     			=       SamProcessor(snake_case_    )
												processor.save_pretrained(self.tmpdirname    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						**snake_case_   :							Tuple    ):
												return AutoProcessor.from_pretrained(self.tmpdirname							,						**snake_case_    ).image_processor





					def 							lowerCAmelCase      (				self   :							Dict    ):
												shutil.rmtree(self.tmpdirname    )





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

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

												return image_inputs





					@is_pt_tf_cross_test
					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       self.get_image_processor()

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

												__snake_case     			=       [[1764, 2646]]

												__snake_case     			=       [[683, 1024]]
												__snake_case     			=       processor.post_process_masks(
												    snake_case_							,						snake_case_							,						snake_case_							,						return_tensors="tf"    )
												__snake_case     			=       processor.post_process_masks(
												    snake_case_							,						snake_case_							,						snake_case_							,						return_tensors="pt"    )

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





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

												__snake_case     			=       SamProcessor(image_processor=snake_case_    )

												__snake_case     			=       self.prepare_image_inputs()

												__snake_case     			=       image_processor(snake_case_							,						return_tensors="pt"    )["pixel_values"].numpy()
												__snake_case     			=       processor(images=snake_case_							,						return_tensors="pt"    )["pixel_values"].numpy()

												__snake_case     			=       image_processor(snake_case_							,						return_tensors="tf"    )["pixel_values"].numpy()
												__snake_case     			=       processor(images=snake_case_							,						return_tensors="tf"    )["pixel_values"].numpy()

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


"""simple docstring"""







def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = 10							,    SCREAMING_SNAKE_CASE = 10_00							,    SCREAMING_SNAKE_CASE = True )  ->     int:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							    and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), "Invalid type of value(s) specified to function!"

							if min_val > max_val:
														raise ValueError("Invalid value for min_val or max_val (min_value < max_value)" )
							return min_val if option else max_val
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							return int((number_a + number_a) / 2 )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     None:







							"""simple docstring"""


							assert (
							    isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) and isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							), 'argument values must be type of "int"'

							if lower > higher:
														raise ValueError("argument value for lower and higher must be(lower > higher)" )

							if not lower < to_guess < higher:
														raise ValueError(
														    "guess value must be within the range of lower and higher value" )

							def answer(SCREAMING_SNAKE_CASE ) -> str:
														if number > to_guess:
																					return "high"
														elif number < to_guess:
																					return "low"
														else:
																					return "same"

							print("started..." )

							__snake_case     			=       lower
							__snake_case     			=       higher

							__snake_case     			=       []

							while True:
														__snake_case     			=       get_avg(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
														last_numbers.append(SCREAMING_SNAKE_CASE )

														if answer(SCREAMING_SNAKE_CASE ) == "low":
																					__snake_case     			=       number
														elif answer(SCREAMING_SNAKE_CASE ) == "high":
																					__snake_case     			=       number
														else:
																					break

							print(F'''guess the number : {last_numbers[-1]}''' )
							print(F'''details : {last_numbers!s}''' )
def   __UpperCamelCase   (					)  ->     None:







							"""simple docstring"""


							__snake_case     			=       int(input("Enter lower value : " ).strip() )
							__snake_case     			=       int(input("Enter high value : " ).strip() )
							__snake_case     			=       int(input("Enter value to guess : " ).strip() )
							guess_the_number(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		main()
