




'''simple docstring'''







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 OwlViTImageProcessor





class      lowerCamelCase_							(       unittest.TestCase							):




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase=7,  __UpperCamelCase=3,  __UpperCamelCase=18,  __UpperCamelCase=30,  __UpperCamelCase=400,  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],  __UpperCamelCase=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],  __UpperCamelCase=True,  ):
							_snake_case	       =       parent
							_snake_case	       =       batch_size
							_snake_case	       =       num_channels
							_snake_case	       =       image_size
							_snake_case	       =       min_resolution
							_snake_case	       =       max_resolution
							_snake_case	       =       do_resize
							_snake_case	       =       size if size is not None else {"height": 18, "width": 18}
							_snake_case	       =       do_center_crop
							_snake_case	       =       crop_size if crop_size is not None else {"height": 18, "width": 18}
							_snake_case	       =       do_normalize
							_snake_case	       =       image_mean
							_snake_case	       =       image_std
							_snake_case	       =       do_convert_rgb






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_convert_rgb": self.do_convert_rgb,
							}





@require_torch
@require_vision
class      lowerCamelCase_							(       __lowerCamelCase,       unittest.TestCase							):




					'''simple docstring'''






					lowercase__    = OwlViTImageProcessor if is_vision_available() else None






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       OwlViTImageProcessingTester(self )






					@property
					def 				__SCREAMING_SNAKE_CASE	(    self ):
							return self.image_processor_tester.prepare_image_processor_dict()






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.image_processing_class(**self.image_processor_dict )
							self.assertTrue(hasattr(_UpperCamelCase,  "do_resize" ) )
							self.assertTrue(hasattr(_UpperCamelCase,  "size" ) )
							self.assertTrue(hasattr(_UpperCamelCase,  "do_center_crop" ) )
							self.assertTrue(hasattr(_UpperCamelCase,  "center_crop" ) )
							self.assertTrue(hasattr(_UpperCamelCase,  "do_normalize" ) )
							self.assertTrue(hasattr(_UpperCamelCase,  "image_mean" ) )
							self.assertTrue(hasattr(_UpperCamelCase,  "image_std" ) )
							self.assertTrue(hasattr(_UpperCamelCase,  "do_convert_rgb" ) )






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.image_processing_class.from_dict(self.image_processor_dict )
							self.assertEqual(image_processor.size,  {"height": 18, "width": 18} )
							self.assertEqual(image_processor.crop_size,  {"height": 18, "width": 18} )

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






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.image_processing_class(**self.image_processor_dict )
							# create random PIL images
							_snake_case	       =       prepare_image_inputs(self.image_processor_tester,  equal_resolution=_UpperCamelCase )

							for image in image_inputs:
									self.assertIsInstance(_UpperCamelCase,  Image.Image )

							# Test not batched input
							_snake_case	       =       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
							_snake_case	       =       image_processing(_UpperCamelCase,  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 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.image_processing_class(**self.image_processor_dict )
							# create random numpy tensors
							_snake_case	       =       prepare_image_inputs(self.image_processor_tester,  equal_resolution=_UpperCamelCase,  numpify=_UpperCamelCase )
							for image in image_inputs:
									self.assertIsInstance(_UpperCamelCase,  np.ndarray )

							# Test not batched input
							_snake_case	       =       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
							_snake_case	       =       image_processing(_UpperCamelCase,  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 				__SCREAMING_SNAKE_CASE	(    self ):
							_snake_case	       =       self.image_processing_class(**self.image_processor_dict )
							# create random PyTorch tensors
							_snake_case	       =       prepare_image_inputs(self.image_processor_tester,  equal_resolution=_UpperCamelCase,  torchify=_UpperCamelCase )
							for image in image_inputs:
									self.assertIsInstance(_UpperCamelCase,  torch.Tensor )

							# Test not batched input
							_snake_case	       =       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
							_snake_case	       =       image_processing(_UpperCamelCase,  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"],
							    ),  )











from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase_  						=   TypeVar("T")
class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: int				,_UpperCamelCase			: T						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=data  # Assign data
         SCREAMING_SNAKE_CASE__								=None  # Initialize next as null
         SCREAMING_SNAKE_CASE__								=None  # Initialize prev as null






class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Tuple						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None




     def   __A				(	self			: Dict				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         if self.head is None:
             SCREAMING_SNAKE_CASE__								=Node(_UpperCamelCase						)
         else:
             SCREAMING_SNAKE_CASE__								=Node(_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=new_node
             SCREAMING_SNAKE_CASE__								=self.head
             SCREAMING_SNAKE_CASE__								=None
             SCREAMING_SNAKE_CASE__								=new_node




     def   __A				(	self			: List[str]						)		->   T | None:


         '''simple docstring'''





         if self.head is None:
             return None
         else:
             assert self.head is not None
             SCREAMING_SNAKE_CASE__								=self.head.data
             SCREAMING_SNAKE_CASE__								=self.head.next
             if self.head is not None:
                 SCREAMING_SNAKE_CASE__								=None
             return temp




     def   __A				(	self			: List[str]						)		->   T | None:


         '''simple docstring'''





         return self.head.data if self.head is not None else None




     def __len__(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.head
         SCREAMING_SNAKE_CASE__								=0
         while temp is not None:
             count += 1
             SCREAMING_SNAKE_CASE__								=temp.next
         return count




     def   __A				(	self			: List[Any]						)		->   bool:


         '''simple docstring'''





         return self.head is None




     def   __A				(	self			: int						)		->   None:


         '''simple docstring'''





         print("""stack elements are:"""						)
         SCREAMING_SNAKE_CASE__								=self.head
         while temp is not None:
             print(temp.data				,end="""->"""						)
             SCREAMING_SNAKE_CASE__								=temp.next


# Code execution starts here
if __name__ == "__main__":
    # Start with the empty stack
    lowerCamelCase_  						=   Stack()

    # Insert 4 at the beginning. So stack becomes 4->None
    print("Stack operations using Doubly LinkedList")
    stack.push(4)

    # Insert 5 at the beginning. So stack becomes 4->5->None
    stack.push(5)

    # Insert 6 at the beginning. So stack becomes 4->5->6->None
    stack.push(6)

    # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
    stack.push(7)

    # Print the stack
    stack.print_stack()

    # Print the top element
    print("\nTop element is ", stack.top())

    # Print the stack size
    print("Size of the stack is ", len(stack))

    # pop the top element
    stack.pop()

    # pop the top element
    stack.pop()

    # two elements have now been popped off
    stack.print_stack()

    # Print True if the stack is empty else False
    print("\nstack is empty:", stack.is_empty())






