




"""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 MobileViTImageProcessor






class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





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




   '''simple docstring'''



   _snake_case							:		List[Any]								=  size if size is not None else {"""shortest_edge""": 20}
   _snake_case							:		Tuple								=  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
   _snake_case							:		Optional[int]								=  parent
   _snake_case							:		Dict								=  batch_size
   _snake_case							:		Dict								=  num_channels
   _snake_case							:		List[Any]								=  image_size
   _snake_case							:		Tuple								=  min_resolution
   _snake_case							:		List[Any]								=  max_resolution
   _snake_case							:		Optional[Any]								=  do_resize
   _snake_case							:		List[str]								=  size
   _snake_case							:		int								=  do_center_crop
   _snake_case							:		str								=  crop_size
   _snake_case							:		Dict								=  do_flip_channel_order


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



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







@require_torch
@require_vision
class 					lowercase(     __a       ,			unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					MobileViTImageProcessor if is_vision_available() else None


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  MobileViTImageProcessingTester(self		)


 @property
 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  self.image_processing_class(**self.image_processor_dict		)
   self.assertTrue(hasattr(a_,      """do_resize"""		)		)
   self.assertTrue(hasattr(a_,      """size"""		)		)
   self.assertTrue(hasattr(a_,      """do_center_crop"""		)		)
   self.assertTrue(hasattr(a_,      """center_crop"""		)		)
   self.assertTrue(hasattr(a_,      """do_flip_channel_order"""		)		)


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Dict								=  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}		)

   _snake_case							:		Tuple								=  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  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   pass


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.image_processing_class(**self.image_processor_dict		)
   # create random PIL images
   _snake_case							:		List[Any]								=  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							:		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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),      )

   # Test batched
   _snake_case							:		Union[str, Any]								=  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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),      )


 def  UpperCamelCase_   (				self:		List[str]		):




   '''simple docstring'''



   _snake_case							:		int								=  self.image_processing_class(**self.image_processor_dict		)
   # create random numpy tensors
   _snake_case							:		Any								=  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							:		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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),      )

   # Test batched
   _snake_case							:		str								=  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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),      )


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self.image_processing_class(**self.image_processor_dict		)
   # create random PyTorch tensors
   _snake_case							:		Dict								=  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							:		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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),      )

   # Test batched
   _snake_case							:		Optional[Any]								=  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.crop_size["""height"""],
           self.image_processor_tester.crop_size["""width"""],
       ),      )








"""simple docstring"""


import os
from typing import Dict, List, Tuple, TypeVar, Union


A_						=				TypeVar('''T''')

A_						=				Union[List[T], Tuple[T, ...]]
A_						=				Union[T, List[T], Dict[str, T]]
A_						=				Union[str, bytes, os.PathLike]


