





import unittest

import numpy as np

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

if is_vision_available():
    from PIL import Image

    from transformers import PoolFormerImageProcessor

class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 def __init__(  self,		A_,		A_=7,		A_=3,		A_=30,		A_=400,		A_=True,		A_=None,		A_=0.9,		A_=None,		A_=True,		A_=[0.5, 0.5, 0.5],		A_=[0.5, 0.5, 0.5],		)	->       Optional[Any]:
    UpperCAmelCase__						=size if size is not None else {"""shortest_edge""": 30}
    UpperCAmelCase__						=crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
    UpperCAmelCase__						=parent
    UpperCAmelCase__						=batch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=min_resolution
    UpperCAmelCase__						=max_resolution
    UpperCAmelCase__						=do_resize_and_center_crop
    UpperCAmelCase__						=size
    UpperCAmelCase__						=crop_pct
    UpperCAmelCase__						=crop_size
    UpperCAmelCase__						=do_normalize
    UpperCAmelCase__						=image_mean
    UpperCAmelCase__						=image_std






 def  __UpperCAmelCase						(  self		)	->       Dict:
    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }

@require_torch
@require_vision
class     snake_case_		(   _SCREAMING_SNAKE_CASE,		unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      PoolFormerImageProcessor if is_vision_available() else None

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						=PoolFormerImageProcessingTester(self		)

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

 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    self.assertTrue(hasattr(lowerCAmelCase_,		"do_resize_and_center_crop"		)		)
    self.assertTrue(hasattr(lowerCAmelCase_,		"size"		)		)
    self.assertTrue(hasattr(lowerCAmelCase_,		"crop_pct"		)		)
    self.assertTrue(hasattr(lowerCAmelCase_,		"do_normalize"		)		)
    self.assertTrue(hasattr(lowerCAmelCase_,		"image_mean"		)		)
    self.assertTrue(hasattr(lowerCAmelCase_,		"image_std"		)		)

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=self.image_processing_class.from_dict(self.image_processor_dict		)
    self.assertEqual(image_processor.size,		{"shortest_edge": 30}		)
    self.assertEqual(image_processor.crop_size,		{"height": 30, "width": 30}		)

    UpperCAmelCase__						=self.image_processing_class.from_dict(self.image_processor_dict,		size=42,		crop_size=84		)
    self.assertEqual(image_processor.size,		{"shortest_edge": 42}		)
    self.assertEqual(image_processor.crop_size,		{"height": 84, "width": 84}		)

 def  __UpperCAmelCase						(  self		)	->       int:
    pass

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    # Initialize image_processing
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    # create random PIL images
    UpperCAmelCase__						=prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCAmelCase_		)
    for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase_,		Image.Image		)

    # Test not batched input
    UpperCAmelCase__						=image_processing(image_inputs[0],		return_tensors="pt"		).pixel_values

    self.assertEqual(
        encoded_images.shape,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),		)

    # Test batched
    UpperCAmelCase__						=image_processing(lowerCAmelCase_,		return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),		)

 def  __UpperCAmelCase						(  self		)	->       int:
    # Initialize image_processing
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    # create random numpy tensors
    UpperCAmelCase__						=prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCAmelCase_,		numpify=lowerCAmelCase_		)
    for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase_,		np.ndarray		)

    # Test not batched input
    UpperCAmelCase__						=image_processing(image_inputs[0],		return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),		)

    # Test batched
    UpperCAmelCase__						=image_processing(lowerCAmelCase_,		return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),		)






 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    # Initialize image_processing
    UpperCAmelCase__						=self.image_processing_class(**self.image_processor_dict		)
    # create random PyTorch tensors
    UpperCAmelCase__						=prepare_image_inputs(self.image_processor_tester,		equal_resolution=lowerCAmelCase_,		torchify=lowerCAmelCase_		)
    for image in image_inputs:
       self.assertIsInstance(lowerCAmelCase_,		torch.Tensor		)

    # Test not batched input
    UpperCAmelCase__						=image_processing(image_inputs[0],		return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape,		(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),		)

    # Test batched
    UpperCAmelCase__						=image_processing(lowerCAmelCase_,		return_tensors="pt"		).pixel_values
    self.assertEqual(
        encoded_images.shape,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),		)











"""simple docstring"""
from __future__ import annotations






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	if len(snake_case__       ) == 0:
		return False
	A_		:    Union[str, Any]						     = len(snake_case__       ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint] , snake_case__       )
	else:
		return binary_search(a_list[midpoint + 1 :] , snake_case__       )


if __name__ == "__main__":
							_lowerCAmelCase       =				input("Enter numbers separated by comma:\n").strip()
							_lowerCAmelCase       =				[int(item.strip()) for item in user_input.split(",")]
							_lowerCAmelCase       =				int(input("Enter the number to be found in the list:\n").strip())
							_lowerCAmelCase       =				"" if binary_search(sequence, target) else "not "
							print(F'{target} was {not_str}found in {sequence}')

