




'''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 MobileNetVaImageProcessor





class __magic_name__(						unittest.TestCase      ):






						def __init__(   self						:						int					,   __UpperCamelCase						:						List[Any]					,   __UpperCamelCase						:						int=7					,   __UpperCamelCase						:						Optional[Any]=3					,   __UpperCamelCase						:						Dict=1_8					,   __UpperCamelCase						:						Tuple=3_0					,   __UpperCamelCase						:						List[Any]=4_0_0					,   __UpperCamelCase						:						List[str]=True					,   __UpperCamelCase						:						Any=None					,   __UpperCamelCase						:						int=True					,   __UpperCamelCase						:						Optional[int]=None					,   ):



									'''simple docstring'''






									snake_case__          =						size if size is not None else {"""shortest_edge""": 2_0}
									snake_case__          =						crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}
									snake_case__          =						parent
									snake_case__          =						batch_size
									snake_case__          =						num_channels
									snake_case__          =						image_size
									snake_case__          =						min_resolution
									snake_case__          =						max_resolution
									snake_case__          =						do_resize
									snake_case__          =						size
									snake_case__          =						do_center_crop
									snake_case__          =						crop_size







						def       __lowerCAmelCase(   self						:						Dict       ):



									'''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 __magic_name__(						__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					Optional[Any]    =  MobileNetVaImageProcessor if is_vision_available() else None






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						MobileNetVaImageProcessingTester(self       )






						@property
						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									return self.image_processor_tester.prepare_image_processor_dict()






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						self.image_processing_class(**self.image_processor_dict       )
									self.assertTrue(hasattr(__UpperCamelCase					,   """do_resize"""       )       )
									self.assertTrue(hasattr(__UpperCamelCase					,   """size"""       )       )
									self.assertTrue(hasattr(__UpperCamelCase					,   """do_center_crop"""       )       )
									self.assertTrue(hasattr(__UpperCamelCase					,   """center_crop"""       )       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						self.image_processing_class.from_dict(self.image_processor_dict       )
									self.assertEqual(image_processor.size					,   {"""shortest_edge""": 2_0}       )
									self.assertEqual(image_processor.crop_size					,   {"""height""": 1_8, """width""": 1_8}       )

									snake_case__          =						self.image_processing_class.from_dict(self.image_processor_dict					,   size=4_2					,   crop_size=8_4       )
									self.assertEqual(image_processor.size					,   {"""shortest_edge""": 4_2}       )
									self.assertEqual(image_processor.crop_size					,   {"""height""": 8_4, """width""": 8_4}       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									pass






						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






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

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






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									snake_case__          =						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
									snake_case__          =						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__          =						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.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )					,   )







						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






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


"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


A									=						logging.get_logger(__name__)

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    ['pixel_values']



  def __init__( self					: Union[str, Any]       ,       snake_case					: bool = True       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: bool = False       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Union[int, float] = 1 / 255       ,       snake_case					: bool = False       ,       snake_case					: bool = True       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = True       ,       **snake_case					: Optional[Any]       ,       )	->			None:


         '''simple docstring'''





         super().__init__(**snake_case      )
         __magic_name__			:		Any        =     size if size is not None else {'''height''': 346, '''width''': 346}
         __magic_name__			:		int        =     get_size_dict(snake_case      )
         __magic_name__			:		List[str]        =     crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Any        =     do_resize
         __magic_name__			:		Tuple        =     size
         __magic_name__			:		List[Any]        =     resample
         __magic_name__			:		Tuple        =     do_center_crop
         __magic_name__			:		str        =     crop_size
         __magic_name__			:		Tuple        =     do_rescale
         __magic_name__			:		Optional[Any]        =     rescale_factor
         __magic_name__			:		Optional[Any]        =     rescale_offset
         __magic_name__			:		int        =     do_normalize
         __magic_name__			:		str        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __magic_name__			:		Optional[int]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD
         __magic_name__			:		Optional[int]        =     include_top



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: PILImageResampling = PIL.Image.NEAREST       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Union[str, Any]       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Optional[Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return resize(
             snake_case       ,       size=(size['''height'''], size['''width'''])       ,       resample=snake_case       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Dict[str, int]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: str       ,       )	->			np.ndarray:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     get_size_dict(snake_case      )
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""      )
         return center_crop(snake_case       ,       size=(size['''height'''], size['''width'''])       ,       data_format=snake_case       ,       **snake_case      )



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: np.ndarray       ,       snake_case					: Union[int, float]       ,       snake_case					: bool = True       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: Dict       ,       )	->			Any:


         '''simple docstring'''





         if offset:
                __magic_name__			:		List[str]        =     (image - 127.5) * scale
                if data_format is not None:
                       __magic_name__			:		str        =     to_channel_dimension_format(snake_case       ,       snake_case      )
                __magic_name__			:		Optional[int]        =     rescaled_image.astype(np.floataa      )
         else:
                __magic_name__			:		str        =     rescale(snake_case       ,       scale=snake_case       ,       data_format=snake_case       ,       **snake_case      )
         return rescaled_image



  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: np.ndarray       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Union[float, List[float]]       ,       snake_case					: Optional[Union[str, ChannelDimension]] = None       ,       **snake_case					: int       ,       )	->			np.ndarray:


         '''simple docstring'''





         return normalize(snake_case       ,       mean=snake_case       ,       std=snake_case       ,       data_format=snake_case       ,       **snake_case      )




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: ImageInput       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: Any=None       ,       snake_case					: bool = None       ,       snake_case					: Dict[str, int] = None       ,       snake_case					: bool = None       ,       snake_case					: float = None       ,       snake_case					: bool = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: Optional[Union[float, List[float]]] = None       ,       snake_case					: bool = None       ,       snake_case					: Optional[Union[str, TensorType]] = None       ,       snake_case					: ChannelDimension = ChannelDimension.FIRST       ,       **snake_case					: List[str]       ,       )	->			PIL.Image.Image:


         '''simple docstring'''





         __magic_name__			:		Any        =     do_resize if do_resize is not None else self.do_resize
         __magic_name__			:		Optional[Any]        =     resample if resample is not None else self.resample
         __magic_name__			:		Optional[Any]        =     do_center_crop if do_center_crop is not None else self.do_center_crop
         __magic_name__			:		Optional[int]        =     do_rescale if do_rescale is not None else self.do_rescale
         __magic_name__			:		Tuple        =     rescale_factor if rescale_factor is not None else self.rescale_factor
         __magic_name__			:		Dict        =     rescale_offset if rescale_offset is not None else self.rescale_offset
         __magic_name__			:		Union[str, Any]        =     do_normalize if do_normalize is not None else self.do_normalize
         __magic_name__			:		Tuple        =     image_mean if image_mean is not None else self.image_mean
         __magic_name__			:		Dict        =     image_std if image_std is not None else self.image_std
         __magic_name__			:		Union[str, Any]        =     include_top if include_top is not None else self.include_top

         __magic_name__			:		Any        =     size if size is not None else self.size
         __magic_name__			:		List[str]        =     get_size_dict(snake_case      )
         __magic_name__			:		List[Any]        =     crop_size if crop_size is not None else self.crop_size
         __magic_name__			:		Dict        =     get_size_dict(snake_case       ,       param_name='''crop_size'''      )

         __magic_name__			:		Optional[Any]        =     make_list_of_images(snake_case      )

         if not valid_images(snake_case      ):
                raise ValueError(
                    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

         if do_resize and size is None or resample is None:
                raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

         if do_center_crop and crop_size is None:
                raise ValueError('''Crop size must be specified if do_center_crop is True.'''      )

         if do_rescale and rescale_factor is None:
                raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

         if do_normalize and (image_mean is None or image_std is None):
                raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

         # All transformations expect numpy arrays.
         __magic_name__			:		Union[str, Any]        =     [to_numpy_array(snake_case      ) for image in images]

         if do_resize:
                __magic_name__			:		Optional[int]        =     [self.resize(image=snake_case       ,       size=snake_case       ,       resample=snake_case      ) for image in images]

         if do_center_crop:
                __magic_name__			:		Dict        =     [self.center_crop(image=snake_case       ,       size=snake_case      ) for image in images]

         if do_rescale:
                __magic_name__			:		Any        =     [self.rescale(image=snake_case       ,       scale=snake_case       ,       offset=snake_case      ) for image in images]

         if do_normalize:
                __magic_name__			:		Union[str, Any]        =     [self.normalize(image=snake_case       ,       mean=snake_case       ,       std=snake_case      ) for image in images]

         if include_top:
                __magic_name__			:		Optional[Any]        =     [self.normalize(image=snake_case       ,       mean=[0, 0, 0]       ,       std=snake_case      ) for image in images]

         __magic_name__			:		Any        =     [to_channel_dimension_format(snake_case       ,       snake_case      ) for image in images]

         __magic_name__			:		List[str]        =     {'''pixel_values''': images}
         return BatchFeature(data=snake_case       ,       tensor_type=snake_case      )
