




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 EfficientNetImageProcessor






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     def __init__(			self     :      Dict		,     A__     :      str		,     A__     :      Any=1_3		,     A__     :      List[Any]=3		,     A__     :      Tuple=1_8		,     A__     :      Optional[int]=3_0		,     A__     :      str=4_0_0		,     A__     :      Union[str, Any]=True		,     A__     :      Optional[Any]=None		,     A__     :      Optional[Any]=True		,     A__     :      List[Any]=[0.5, 0.5, 0.5]		,     A__     :      Dict=[0.5, 0.5, 0.5]		,     ) ->				Tuple:
           lowerCamelCase__					     = size if size is not None else {'height': 1_8, 'width': 1_8}
           lowerCamelCase__					     = parent
           lowerCamelCase__					     = batch_size
           lowerCamelCase__					     = num_channels
           lowerCamelCase__					     = image_size
           lowerCamelCase__					     = min_resolution
           lowerCamelCase__					     = max_resolution
           lowerCamelCase__					     = do_resize
           lowerCamelCase__					     = size
           lowerCamelCase__					     = do_normalize
           lowerCamelCase__					     = image_mean
           lowerCamelCase__					     = image_std




     def        __snake_case      (			self     :      int	) ->				Any:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }






@require_torch
@require_vision
class 	A	(						_lowerCAmelCase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Dict   =EfficientNetImageProcessor if is_vision_available() else None




     def        __snake_case      (			self     :      Optional[Any]	) ->				List[str]:
           lowerCamelCase__					     = EfficientNetImageProcessorTester(self	)




     @property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return self.image_processor_tester.prepare_image_processor_dict()




     def        __snake_case      (			self     :      Optional[Any]	) ->				List[Any]:
           lowerCamelCase__					     = self.image_processing_class(**self.image_processor_dict	)
           self.assertTrue(hasattr(A__		,     'image_mean'	)	)
           self.assertTrue(hasattr(A__		,     'image_std'	)	)
           self.assertTrue(hasattr(A__		,     'do_normalize'	)	)
           self.assertTrue(hasattr(A__		,     'do_resize'	)	)
           self.assertTrue(hasattr(A__		,     'size'	)	)




     def        __snake_case      (			self     :      str	) ->				Tuple:
           lowerCamelCase__					     = self.image_processing_class.from_dict(self.image_processor_dict	)
           self.assertEqual(image_processor.size		,     {'height': 1_8, 'width': 1_8}	)

           lowerCamelCase__					     = self.image_processing_class.from_dict(self.image_processor_dict		,     size=4_2	)
           self.assertEqual(image_processor.size		,     {'height': 4_2, 'width': 4_2}	)




     def        __snake_case      (			self     :      List[Any]	) ->				int:
           # Initialize image_processing
           lowerCamelCase__					     = self.image_processing_class(**self.image_processor_dict	)
           # create random PIL images
           lowerCamelCase__					     = prepare_image_inputs(self.image_processor_tester		,     equal_resolution=A__	)
           for image in image_inputs:
                 self.assertIsInstance(A__		,     Image.Image	)

           # Test not batched input
           lowerCamelCase__					     = 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.size['height'],
                   self.image_processor_tester.size['width'],
               )		,     )

           # Test batched
           lowerCamelCase__					     = image_processing(A__		,     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.size['height'],
                   self.image_processor_tester.size['width'],
               )		,     )




     def        __snake_case      (			self     :      int	) ->				Union[str, Any]:
           # Initialize image_processing
           lowerCamelCase__					     = self.image_processing_class(**self.image_processor_dict	)
           # create random numpy tensors
           lowerCamelCase__					     = prepare_image_inputs(self.image_processor_tester		,     equal_resolution=A__		,     numpify=A__	)
           for image in image_inputs:
                 self.assertIsInstance(A__		,     np.ndarray	)

           # Test not batched input
           lowerCamelCase__					     = 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.size['height'],
                   self.image_processor_tester.size['width'],
               )		,     )

           # Test batched
           lowerCamelCase__					     = image_processing(A__		,     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.size['height'],
                   self.image_processor_tester.size['width'],
               )		,     )




     def        __snake_case      (			self     :      Union[str, Any]	) ->				Dict:
           # Initialize image_processing
           lowerCamelCase__					     = self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           lowerCamelCase__					     = prepare_image_inputs(self.image_processor_tester		,     equal_resolution=A__		,     torchify=A__	)
           for image in image_inputs:
                 self.assertIsInstance(A__		,     torch.Tensor	)

           # Test not batched input
           lowerCamelCase__					     = 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.size['height'],
                   self.image_processor_tester.size['width'],
               )		,     )

           # Test batched
           lowerCamelCase__					     = image_processing(A__		,     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.size['height'],
                   self.image_processor_tester.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())






