




"""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 ConvNextImageProcessor
class 							lowerCamelCase		( unittest.TestCase				):







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=7    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=18    ,      SCREAMING_SNAKE_CASE_=30    ,      SCREAMING_SNAKE_CASE_=400    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=0.875    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]    ,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]    ,      ):
       UpperCamelCase				:   Tuple          =							size if size is not None else {'''shortest_edge''': 20}
       UpperCamelCase				:   List[Any]          =							parent
       UpperCamelCase				:   Optional[int]          =							batch_size
       UpperCamelCase				:   str          =							num_channels
       UpperCamelCase				:   Union[str, Any]          =							image_size
       UpperCamelCase				:   Optional[Any]          =							min_resolution
       UpperCamelCase				:   Union[str, Any]          =							max_resolution
       UpperCamelCase				:   Tuple          =							do_resize
       UpperCamelCase				:   List[Any]          =							size
       UpperCamelCase				:   Optional[int]          =							crop_pct
       UpperCamelCase				:   Any          =							do_normalize
       UpperCamelCase				:   Optional[int]          =							image_mean
       UpperCamelCase				:   Union[str, Any]          =							image_std







   def       a_							(							self ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }







@require_torch
@require_vision
class 							lowerCamelCase		( __UpperCAmelCase	,  unittest.TestCase				):
   lowercase       :  List[Any]      =				ConvNextImageProcessor if is_vision_available() else None







   def       a_							(							self ):
       UpperCamelCase				:   List[Any]          =							ConvNextImageProcessingTester(self )







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







   def       a_							(							self ):
       UpperCamelCase				:   List[Any]          =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowercase_    ,      """do_resize""" ) )
       self.assertTrue(hasattr(lowercase_    ,      """size""" ) )
       self.assertTrue(hasattr(lowercase_    ,      """crop_pct""" ) )
       self.assertTrue(hasattr(lowercase_    ,      """do_normalize""" ) )
       self.assertTrue(hasattr(lowercase_    ,      """image_mean""" ) )
       self.assertTrue(hasattr(lowercase_    ,      """image_std""" ) )







   def       a_							(							self ):
       UpperCamelCase				:   int          =							self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size    ,      {"""shortest_edge""": 20} )

       UpperCamelCase				:   Tuple          =							self.image_processing_class.from_dict(self.image_processor_dict    ,      size=42 )
       self.assertEqual(image_processor.size    ,      {"""shortest_edge""": 42} )







   def       a_							(							self ):
       pass







   def       a_							(							self ):
       UpperCamelCase				:   Tuple          =							self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       UpperCamelCase				:   List[str]          =							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=lowercase_ )
       for image in image_inputs:
           self.assertIsInstance(lowercase_    ,      Image.Image )

       # Test not batched input
       UpperCamelCase				:   List[str]          =							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["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )    ,      )

       # Test batched
       UpperCamelCase				:   int          =							image_processing(lowercase_    ,      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["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )    ,      )







   def       a_							(							self ):
       UpperCamelCase				:   Tuple          =							self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       UpperCamelCase				:   Tuple          =							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=lowercase_    ,      numpify=lowercase_ )
       for image in image_inputs:
           self.assertIsInstance(lowercase_    ,      np.ndarray )

       # Test not batched input
       UpperCamelCase				:   List[str]          =							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["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )    ,      )

       # Test batched
       UpperCamelCase				:   Union[str, Any]          =							image_processing(lowercase_    ,      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["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )    ,      )







   def       a_							(							self ):
       UpperCamelCase				:   Union[str, Any]          =							self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       UpperCamelCase				:   Dict          =							prepare_image_inputs(self.image_processor_tester    ,      equal_resolution=lowercase_    ,      torchify=lowercase_ )
       for image in image_inputs:
           self.assertIsInstance(lowercase_    ,      torch.Tensor )

       # Test not batched input
       UpperCamelCase				:   Union[str, Any]          =							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["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )    ,      )

       # Test batched
       UpperCamelCase				:   List[Any]          =							image_processing(lowercase_    ,      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["""shortest_edge"""],
               self.image_processor_tester.size["""shortest_edge"""],
           )    ,      )












"""simple docstring"""




class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None ):
       UpperCamelCase				:   Optional[Any]          =							data
       UpperCamelCase				:   str          =							None







   def __repr__(							self ):
       UpperCamelCase				:   str          =							""""""
       UpperCamelCase				:   Any          =							self
       while temp:
           string_rep += f'<{temp.data}> ---> '
           UpperCamelCase				:   Optional[int]          =							temp.next
       string_rep += "<END>"
       return string_rep







def 					A_			(		snake_case_     :       Tuple    ):







    '''simple docstring'''

    # if elements_list is empty
    if not elements_list:
        raise Exception("""The Elements List is empty"""    )

    # Set first element as Head
    UpperCamelCase				:   List[Any]          =							Node(elements_list[0]    )
    UpperCamelCase				:   str          =							head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase				:   Optional[int]          =							Node(snake_case_    )
        UpperCamelCase				:   Optional[Any]          =							current.next
    return head


__A :		List[Any]      							=    [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('''Creating Linked List from List.''')
__A :		int      							=    make_linked_list(list_data)
print('''Linked List:''')


print(linked_list)




