
'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any


class 					a_					:
       __lowerCAmelCase      :			str      = ("""_front""", """_back""", """_len""")


       @dataclass
       class 					a_					:
              __lowerCAmelCase      :			Any      = None
              __lowerCAmelCase      :			Deque._Node | None      = None
              __lowerCAmelCase      :			Deque._Node | None      = None



       class 					a_					:
              __lowerCAmelCase      :			Optional[Any]      = ("""_cur""",)




              def __init__(       self				,				snake_case_					):
                _lowerCAmelCase    :					List[str]							=   cur




              def __iter__(       self					):
                return self







              def 							__UpperCamelCase    (       self					):
                if self._cur is None:
                  # finished iterating
                  raise StopIteration
                _lowerCAmelCase    :					Tuple							=   self._cur.val
                _lowerCAmelCase    :					List[str]							=   self._cur.next_node

                return val




       def __init__(       self				,				snake_case_ = None					):
         _lowerCAmelCase    :					Any							=   None
         _lowerCAmelCase    :					Any							=   None
         _lowerCAmelCase    :					int							=   0

         if iterable is not None:
           # append every value to the deque
           for val in iterable:
             self.append(snake_case_					)




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					Optional[Any]							=   self._Node(snake_case_				,				snake_case_				,				snake_case_					)
         if self.is_empty():
           # front = back
           _lowerCAmelCase    :					str							=   node
           _lowerCAmelCase    :					Any							=   1
         else:
           # connect nodes
           _lowerCAmelCase    :					List[str]							=   node
           _lowerCAmelCase    :					Dict							=   self._back
           _lowerCAmelCase    :					Tuple							=   node  # assign new back to the new node

           self._len += 1

           # make sure there were no errors
           assert not self.is_empty(), "Error on appending value."




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					int							=   self._Node(snake_case_				,				snake_case_				,				snake_case_					)
         if self.is_empty():
           # front = back
           _lowerCAmelCase    :					str							=   node
           _lowerCAmelCase    :					Tuple							=   1
         else:
           # connect nodes
           _lowerCAmelCase    :					Tuple							=   self._front
           _lowerCAmelCase    :					List[Any]							=   node
           _lowerCAmelCase    :					int							=   node  # assign new front to the new node

           self._len += 1

           # make sure there were no errors
           assert not self.is_empty(), "Error on appending value."




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         for val in iterable:
           self.append(snake_case_					)




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         for val in iterable:
           self.appendleft(snake_case_					)




       def 							__UpperCamelCase    (       self					):
         assert not self.is_empty(), "Deque is empty."

         _lowerCAmelCase    :					Optional[int]							=   self._back
         _lowerCAmelCase    :					Optional[int]							=   self._back.prev_node  # set new back
         # drop the last node - python will deallocate memory automatically
         _lowerCAmelCase    :					str							=   None

         self._len -= 1

         return topop.val




       def 							__UpperCamelCase    (       self					):
         assert not self.is_empty(), "Deque is empty."

         _lowerCAmelCase    :					Optional[Any]							=   self._front
         _lowerCAmelCase    :					str							=   self._front.next_node  # set new front and drop the first node
         _lowerCAmelCase    :					List[str]							=   None

         self._len -= 1

         return topop.val




       def 							__UpperCamelCase    (       self					):
         return self._front is None




       def __len__(       self					):
         return self._len




       def __eq__(       self				,				snake_case_					):

         if not isinstance(snake_case_				,				snake_case_					):
           return NotImplemented

         _lowerCAmelCase    :					str							=   self._front
         _lowerCAmelCase    :					List[Any]							=   other._front

         # if the length of the dequeues are not the same, they are not equal
         if len(self					) != len(snake_case_					):
           return False

         while me is not None and oth is not None:
           # compare every value
           if me.val != oth.val:
             return False
           _lowerCAmelCase    :					Dict							=   me.next_node
           _lowerCAmelCase    :					Tuple							=   oth.next_node

         return True




       def __iter__(       self					):
         return Deque._Iterator(self._front					)







       def __repr__(       self					):
         _lowerCAmelCase    :					Tuple							=   []
         _lowerCAmelCase    :					int							=   self._front
         while aux is not None:
           # append the values in a list to display
           values_list.append(aux.val					)
           _lowerCAmelCase    :					Optional[int]							=   aux.next_node

         return f'[{", ".join(repr(snake_case_					) for val in values_list					)}]'


if __name__ == "__main__":
     import doctest

     doctest.testmod()




'''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 					a_					(unittest.TestCase      ):




       def __init__(       self				,				snake_case_				,				snake_case_=7				,				snake_case_=3				,				snake_case_=1_8				,				snake_case_=3_0				,				snake_case_=4_0_0				,				snake_case_=True				,				snake_case_=None				,				snake_case_=0.875				,				snake_case_=True				,				snake_case_=[0.5, 0.5, 0.5]				,				snake_case_=[0.5, 0.5, 0.5]				,				):
         _lowerCAmelCase    :					List[str]							=   size if size is not None else {"""shortest_edge""": 2_0}
         _lowerCAmelCase    :					Tuple							=   parent
         _lowerCAmelCase    :					List[str]							=   batch_size
         _lowerCAmelCase    :					List[str]							=   num_channels
         _lowerCAmelCase    :					Tuple							=   image_size
         _lowerCAmelCase    :					Union[str, Any]							=   min_resolution
         _lowerCAmelCase    :					Tuple							=   max_resolution
         _lowerCAmelCase    :					List[Any]							=   do_resize
         _lowerCAmelCase    :					Dict							=   size
         _lowerCAmelCase    :					str							=   crop_pct
         _lowerCAmelCase    :					List[str]							=   do_normalize
         _lowerCAmelCase    :					Any							=   image_mean
         _lowerCAmelCase    :					Any							=   image_std







       def 							__UpperCamelCase    (       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 					a_					(_a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Dict      = ConvNextImageProcessor if is_vision_available() else None




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					int							=   ConvNextImageProcessingTester(self					)




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




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




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Any							=   self.image_processing_class.from_dict(self.image_processor_dict					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 2_0}					)

         _lowerCAmelCase    :					List[Any]							=   self.image_processing_class.from_dict(self.image_processor_dict				,				size=4_2					)
         self.assertEqual(image_processor.size				,				{"""shortest_edge""": 4_2}					)




       def 							__UpperCamelCase    (       self					):
         pass




       def 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					str							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         _lowerCAmelCase    :					str							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				Image.Image					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[int]							=   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
         _lowerCAmelCase    :					Optional[Any]							=   image_processing(snake_case_				,				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 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					int							=   self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         _lowerCAmelCase    :					Any							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				numpify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				np.ndarray					)

         # Test not batched input
         _lowerCAmelCase    :					Optional[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
         _lowerCAmelCase    :					List[str]							=   image_processing(snake_case_				,				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 							__UpperCamelCase    (       self					):
         # Initialize image_processing
         _lowerCAmelCase    :					Tuple							=   self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         _lowerCAmelCase    :					List[str]							=   prepare_image_inputs(self.image_processor_tester				,				equal_resolution=snake_case_				,				torchify=snake_case_					)
         for image in image_inputs:
           self.assertIsInstance(snake_case_				,				torch.Tensor					)

         # Test not batched input
         _lowerCAmelCase    :					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
         _lowerCAmelCase    :					Optional[int]							=   image_processing(snake_case_				,				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"""],
             )				,				)


