

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     ,     _UpperCAmelCase     ,     _UpperCAmelCase=7     ,     _UpperCAmelCase=3     ,     _UpperCAmelCase=18     ,     _UpperCAmelCase=30     ,     _UpperCAmelCase=400     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=0.8_7_5     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=[0.5, 0.5, 0.5]     ,     _UpperCAmelCase=[0.5, 0.5, 0.5]     ,     ):
   __UpperCAmelCase  :     Tuple		=    size if size is not None else {'shortest_edge': 20}
   __UpperCAmelCase  :     Tuple		=    parent
   __UpperCAmelCase  :     Optional[int]		=    batch_size
   __UpperCAmelCase  :     Dict		=    num_channels
   __UpperCAmelCase  :     Union[str, Any]		=    image_size
   __UpperCAmelCase  :     str		=    min_resolution
   __UpperCAmelCase  :     int		=    max_resolution
   __UpperCAmelCase  :     List[Any]		=    do_resize
   __UpperCAmelCase  :     Tuple		=    size
   __UpperCAmelCase  :     str		=    crop_pct
   __UpperCAmelCase  :     List[str]		=    do_normalize
   __UpperCAmelCase  :     List[str]		=    image_mean
   __UpperCAmelCase  :     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_   ( snake_case__  ,							unittest.TestCase):
 _lowerCAmelCase       		=  ConvNextImageProcessor if is_vision_available() else None







 def 						a					( self       ):
   __UpperCAmelCase  :     Union[str, Any]		=    ConvNextImageProcessingTester(self       )







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







 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'do_resize'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'size'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'crop_pct'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'do_normalize'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'image_mean'       )       )
   self.assertTrue(hasattr(_UpperCAmelCase     ,     'image_std'       )       )







 def 						a					( self       ):
   __UpperCAmelCase  :     List[Any]		=    self.image_processing_class.from_dict(self.image_processor_dict       )
   self.assertEqual(image_processor.size     ,     {'shortest_edge': 20}       )

   __UpperCAmelCase  :     List[str]		=    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       ):
   # Initialize image_processing
   __UpperCAmelCase  :     Tuple		=    self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __UpperCAmelCase  :     List[Any]		=    prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=_UpperCAmelCase       )
   for image in image_inputs:
     self.assertIsInstance(_UpperCAmelCase     ,     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  :     Optional[Any]		=    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.size['shortest_edge'],
           self.image_processor_tester.size['shortest_edge'],
       )     ,     )







 def 						a					( self       ):
   # Initialize image_processing
   __UpperCAmelCase  :     Optional[int]		=    self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __UpperCAmelCase  :     str		=    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
   __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  :     Tuple		=    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.size['shortest_edge'],
           self.image_processor_tester.size['shortest_edge'],
       )     ,     )







 def 						a					( self       ):
   # Initialize image_processing
   __UpperCAmelCase  :     int		=    self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __UpperCAmelCase  :     Optional[int]		=    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
   __UpperCAmelCase  :     Tuple		=    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  :     Any		=    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.size['shortest_edge'],
           self.image_processor_tester.size['shortest_edge'],
       )     ,     )








from __future__ import annotations

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


class     lowerCamelCase_   :
 _lowerCAmelCase       		=  ('''_front''', '''_back''', '''_len''')


 @dataclass
 class     lowerCamelCase_   :
  _lowerCAmelCase       		=  None
  _lowerCAmelCase       		=  None
  _lowerCAmelCase       		=  None



 class     lowerCamelCase_   :
  _lowerCAmelCase       		=  ('''_cur''',)




  def __init__( self     ,     _UpperCAmelCase       ):
    __UpperCAmelCase  :     Tuple		=    cur




  def __iter__( self       ):
    return self




  def 						a					( self       ):
    if self._cur is None:
      # finished iterating
      raise StopIteration
    __UpperCAmelCase  :     str		=    self._cur.val
    __UpperCAmelCase  :     Dict		=    self._cur.next_node

    return val




 def __init__( self     ,     _UpperCAmelCase = None       ):
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     int		=    0

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




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    self._Node(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
   if self.is_empty():
     # front = back
     __UpperCAmelCase  :     Dict		=    node
     __UpperCAmelCase  :     List[str]		=    1
   else:
     # connect nodes
     __UpperCAmelCase  :     Any		=    node
     __UpperCAmelCase  :     Tuple		=    self._back
     __UpperCAmelCase  :     Optional[Any]		=    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 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    self._Node(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
   if self.is_empty():
     # front = back
     __UpperCAmelCase  :     str		=    node
     __UpperCAmelCase  :     List[str]		=    1
   else:
     # connect nodes
     __UpperCAmelCase  :     List[str]		=    self._front
     __UpperCAmelCase  :     Any		=    node
     __UpperCAmelCase  :     Dict		=    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 						a					( self     ,     _UpperCAmelCase       ):
   for val in iterable:
     self.append(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   for val in iterable:
     self.appendleft(_UpperCAmelCase       )




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

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

   self._len -= 1

   return topop.val




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

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

   self._len -= 1

   return topop.val




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




 def __len__( self       ):
   return self._len




 def __eq__( self     ,     _UpperCAmelCase       ):

   if not isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
     return NotImplemented

   __UpperCAmelCase  :     List[str]		=    self._front
   __UpperCAmelCase  :     str		=    other._front

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

   while me is not None and oth is not None:
     # compare every value
     if me.val != oth.val:
       return False
     __UpperCAmelCase  :     Any		=    me.next_node
     __UpperCAmelCase  :     str		=    oth.next_node

   return True




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




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

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()





