



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 MobileNetVaImageProcessor



class 		_lowercase		(       unittest.TestCase	):

   def __init__(      self			:       Dict							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Any=7							,   __lowerCAmelCase			:       Optional[int]=3							,   __lowerCAmelCase			:       Any=18							,   __lowerCAmelCase			:       Tuple=30							,   __lowerCAmelCase			:       Dict=400							,   __lowerCAmelCase			:       Optional[Any]=True							,   __lowerCAmelCase			:       Optional[int]=None							,   __lowerCAmelCase			:       List[Any]=True							,   __lowerCAmelCase			:       Dict=None							,   )       ->				Any:


    """simple docstring"""






    a						=		size if size is not None else {"shortest_edge": 20}
    a						=		crop_size if crop_size is not None else {"height": 18, "width": 18}
    a						=		parent
    a						=		batch_size
    a						=		num_channels
    a						=		image_size
    a						=		min_resolution
    a						=		max_resolution
    a						=		do_resize
    a						=		size
    a						=		do_center_crop
    a						=		crop_size




   def 		A							(      self			:       str							)       ->				Optional[Any]:


    """simple docstring"""






    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
    }



@require_torch
@require_vision
class 		_lowercase		(       UpperCAmelCase__,   unittest.TestCase	):
   _UpperCAmelCase     	=			MobileNetVaImageProcessor if is_vision_available() else None

   def 		A							(      self			:       int							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		MobileNetVaImageProcessingTester(self							)

   @property
   def 		A							(      self			:       Tuple							)       ->				Tuple:


    """simple docstring"""






    return self.image_processor_tester.prepare_image_processor_dict()

   def 		A							(      self			:       List[Any]							)       ->				Any:


    """simple docstring"""






    a						=		self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(__lowerCAmelCase							,   "do_resize"							)							)
    self.assertTrue(hasattr(__lowerCAmelCase							,   "size"							)							)
    self.assertTrue(hasattr(__lowerCAmelCase							,   "do_center_crop"							)							)
    self.assertTrue(hasattr(__lowerCAmelCase							,   "crop_size"							)							)

   def 		A							(      self			:       Any							)       ->				Dict:


    """simple docstring"""






    a						=		self.image_processing_class.from_dict(self.image_processor_dict							)
    self.assertEqual(image_processor.size							,   {"shortest_edge": 20}							)
    self.assertEqual(image_processor.crop_size							,   {"height": 18, "width": 18}							)

    a						=		self.image_processing_class.from_dict(self.image_processor_dict							,   size=42							,   crop_size=84							)
    self.assertEqual(image_processor.size							,   {"shortest_edge": 42}							)
    self.assertEqual(image_processor.crop_size							,   {"height": 84, "width": 84}							)

   def 		A							(      self			:       List[Any]							)       ->				Any:


    """simple docstring"""






    pass

   def 		A							(      self			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    a						=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=__lowerCAmelCase							)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase							,   Image.Image							)

    # Test not batched input
    a						=		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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        )							,   )

    # Test batched
    a						=		image_processing(__lowerCAmelCase							,   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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        )							,   )

   def 		A							(      self			:       int							)       ->				int:


    """simple docstring"""






    a						=		self.image_processing_class(**self.image_processor_dict							)
    # create random numpy tensors
    a						=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=__lowerCAmelCase							,   numpify=__lowerCAmelCase							)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase							,   np.ndarray							)

    # Test not batched input
    a						=		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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        )							,   )

    # Test batched
    a						=		image_processing(__lowerCAmelCase							,   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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        )							,   )




   def 		A							(      self			:       Union[str, Any]							)       ->				Tuple:


    """simple docstring"""






    a						=		self.image_processing_class(**self.image_processor_dict							)
    # create random PyTorch tensors
    a						=		prepare_image_inputs(self.image_processor_tester							,   equal_resolution=__lowerCAmelCase							,   torchify=__lowerCAmelCase							)
    for image in image_inputs:
     self.assertIsInstance(__lowerCAmelCase							,   torch.Tensor							)

    # Test not batched input
    a						=		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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        )							,   )

    # Test batched
    a						=		image_processing(__lowerCAmelCase							,   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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        )							,   )






def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

 if not head:
  return True
 # split the list to two parts
 a		,  a						=		head.next, head
 while fast and fast.next:
  a						=		fast.next.next
  a						=		slow.next
 a						=		slow.next
 a						=		None  # Don't forget here! But forget still works!
 # reverse the second part
 a						=		None
 while second:
  a						=		second.next
  a						=		node
  a						=		second
  a						=		nxt
 # compare two parts
 # second part has the same or one less node
 while node:
  if node.val != head.val:
   return False
  a						=		node.next
  a						=		head.next
 return True


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 if not head or not head.next:
  return True

 # 1. Get the midpoint (slow)
 a						=		a						=		a						=		head
 while fast and fast.next:
  a		,  a						=		fast.next.next, slow.next

 # 2. Push the second half into the stack
 a						=		[slow.val]
 while slow.next:
  a						=		slow.next
  stack.append(slow.val					)

 # 3. Comparison
 while stack:
  if stack.pop() != cur.val:
   return False
  a						=		cur.next

 return True




def 	UpperCAmelCase__      (      UpperCAmelCase__ :Any					):






 '''simple docstring'''

 if not head or not head.next:
  return True
 a						=		{}
 a						=		0
 while head:
  if head.val in d:
   d[head.val].append(UpperCAmelCase__					)
  else:
   a						=		[pos]
  a						=		head.next
  pos += 1
 a						=		pos - 1
 a						=		0
 for v in d.values():
  if len(UpperCAmelCase__					) % 2 != 0:
   middle += 1
  else:
   a						=		0
   for i in range(0 ,					len(UpperCAmelCase__					)					):
    if v[i] + v[len(UpperCAmelCase__					) - 1 - step] != checksum:
     return False
    step += 1
  if middle > 1:
   return False
 return True

