

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 						lowercase_					(						unittest.TestCase  ):



      def __init__(    self   :       Optional[Any]    ,      A__   :       Optional[int]    ,      A__   :       List[Any]=13    ,      A__   :       List[str]=3    ,      A__   :       int=18    ,      A__   :       str=30    ,      A__   :       Optional[Any]=400    ,      A__   :       List[str]=True    ,      A__   :       Any=None    ,      A__   :       Tuple=True    ,      A__   :       str=[0.5, 0.5, 0.5]    ,      A__   :       Union[str, Any]=[0.5, 0.5, 0.5]    ,      )					->  int:
        _snake_case		=							size if size is not None else {'''height''': 18, '''width''': 18}
        _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
        _snake_case		=							do_normalize
        _snake_case		=							image_mean
        _snake_case		=							image_std







      def 		UpperCamelCase_	(    self   :       Any	)					->  int:
        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 						lowercase_					(						__lowercase     ,							unittest.TestCase  ):
      UpperCamelCase_    :	Any		   =       EfficientNetImageProcessor if is_vision_available() else None



      def 		UpperCamelCase_	(    self   :       Optional[int]	)					->  Any:
        _snake_case		=							EfficientNetImageProcessorTester(self	)



      @property
      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  Dict:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 		UpperCamelCase_	(    self   :       List[str]	)					->  Dict:
        _snake_case		=							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 		UpperCamelCase_	(    self   :       List[Any]	)					->  List[Any]:
        _snake_case		=							self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size    ,      {'''height''': 18, '''width''': 18}	)

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



      def 		UpperCamelCase_	(    self   :       str	)					->  Any:
        # Initialize image_processing
        _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=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							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 		UpperCamelCase_	(    self   :       int	)					->  Dict:
        # Initialize image_processing
        _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=A__    ,      numpify=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							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 		UpperCamelCase_	(    self   :       int	)					->  str:
        # Initialize image_processing
        _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=A__    ,      torchify=A__	)
        for image in image_inputs:
          self.assertIsInstance(A__    ,      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.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )    ,      )

        # Test batched
        _snake_case		=							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

__A    	=			TypeVar('''T''')



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       str    ,      A__   :       T	)					->  Union[str, Any]:
        _snake_case		=							data  # Assign data
        _snake_case		=							None  # Initialize next as null
        _snake_case		=							None  # Initialize prev as null



class 						lowercase_					(						Generic[T]  ):



      def __init__(    self   :       List[str]	)					->  None:
        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       T	)					->  None:
        if self.head is None:
          _snake_case		=							Node(A__	)
        else:
          _snake_case		=							Node(A__	)
          _snake_case		=							new_node
          _snake_case		=							self.head
          _snake_case		=							None
          _snake_case		=							new_node



      def 		UpperCamelCase_	(    self   :       Tuple	)					->  T | None:
        if self.head is None:
          return None
        else:
          assert self.head is not None
          _snake_case		=							self.head.data
          _snake_case		=							self.head.next
          if self.head is not None:
            _snake_case		=							None
          return temp



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  T | None:
        return self.head.data if self.head is not None else None



      def __len__(    self   :       Optional[int]	)					->  int:
        _snake_case		=							self.head
        _snake_case		=							0
        while temp is not None:
          count += 1
          _snake_case		=							temp.next
        return count



      def 		UpperCamelCase_	(    self   :       Union[str, Any]	)					->  bool:
        return self.head is None







      def 		UpperCamelCase_	(    self   :       Dict	)					->  None:
        print('''stack elements are:'''	)
        _snake_case		=							self.head
        while temp is not None:
          print(temp.data    ,      end='''->'''	)
          _snake_case		=							temp.next


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

