




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,
    get_resize_output_image_size,
    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, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Tuple       		=  ['pixel_values']
     def __init__(     self       :      Tuple ,   __snake_case       :      bool = True ,   __snake_case       :      Optional[Dict[str, int]] = None ,   __snake_case       :      PILImageResampling = PILImageResampling.BILINEAR ,   __snake_case       :      bool = True ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      bool = True ,   __snake_case       :      Union[int, float] = 1 / 2_55 ,   __snake_case       :      bool = True ,   __snake_case       :      Optional[Union[float, List[float]]] = None ,   __snake_case       :      Optional[Union[float, List[float]]] = None ,   **__snake_case       :      str ,   ):
         super().__init__(**__snake_case    )
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 2_56}
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         UpperCAmelCase_										=					get_size_dict(__snake_case    )
         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					resample
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size
         UpperCAmelCase_										=					do_rescale
         UpperCAmelCase_										=					rescale_factor
         UpperCAmelCase_										=					do_normalize
         UpperCAmelCase_										=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_										=					image_std if image_std is not None else IMAGENET_STANDARD_STD
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      np.ndarray ,   __snake_case       :      Dict[str, int] ,   __snake_case       :      PILImageResampling = PILImageResampling.BICUBIC ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      int ,   ):
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         if "shortest_edge" not in size:
             raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'    )
         UpperCAmelCase_										=					get_resize_output_image_size(__snake_case ,   size=size['''shortest_edge'''] ,   default_to_square=__snake_case    )
         return resize(__snake_case ,   size=__snake_case ,   resample=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      np.ndarray ,   __snake_case       :      Dict[str, int] ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      Optional[int] ,   ):
         UpperCAmelCase_										=					get_size_dict(__snake_case    )
         return center_crop(__snake_case ,   size=(size['''height'''], size['''width''']) ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      np.ndarray ,   __snake_case       :      float ,   __snake_case       :      Optional[Union[str, ChannelDimension]] = None ,   **__snake_case       :      str    ):
         return rescale(__snake_case ,   scale=__snake_case ,   data_format=__snake_case ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      Union[str, 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       :      Optional[Any] ,   ):
         return normalize(__snake_case ,   mean=__snake_case ,   std=__snake_case ,   data_format=__snake_case ,   **__snake_case    )




     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      ImageInput ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      PILImageResampling = None ,   __snake_case       :      bool = None ,   __snake_case       :      Dict[str, int] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[float] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Union[float, List[float]]] = None ,   __snake_case       :      Optional[Union[float, List[float]]] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   __snake_case       :      Union[str, ChannelDimension] = ChannelDimension.FIRST ,   **__snake_case       :      Optional[Any] ,   ):
         UpperCAmelCase_										=					do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_										=					size if size is not None else self.size
         UpperCAmelCase_										=					get_size_dict(__snake_case ,   default_to_square=__snake_case    )
         UpperCAmelCase_										=					resample if resample is not None else self.resample
         UpperCAmelCase_										=					do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_										=					crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_										=					get_size_dict(__snake_case    )
         UpperCAmelCase_										=					do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_										=					rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_										=					do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_										=					image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_										=					image_std if image_std is not None else self.image_std

         UpperCAmelCase_										=					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:
             raise ValueError('''Size 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.
         UpperCAmelCase_										=					[to_numpy_array(__snake_case    ) for image in images]

         if do_resize:
             UpperCAmelCase_										=					[self.resize(image=__snake_case ,   size=__snake_case ,   resample=__snake_case    ) for image in images]

         if do_center_crop:
             UpperCAmelCase_										=					[self.center_crop(image=__snake_case ,   size=__snake_case    ) for image in images]

         if do_rescale:
             UpperCAmelCase_										=					[self.rescale(image=__snake_case ,   scale=__snake_case    ) for image in images]

         if do_normalize:
             UpperCAmelCase_										=					[self.normalize(image=__snake_case ,   mean=__snake_case ,   std=__snake_case    ) for image in images]

         UpperCAmelCase_										=					[to_channel_dimension_format(__snake_case ,   __snake_case    ) for image in images]

         UpperCAmelCase_										=					{'''pixel_values''': images}
         return BatchFeature(data=__snake_case ,   tensor_type=__snake_case    )











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       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      int ,   __snake_case       :      List[Any]=7 ,   __snake_case       :      Any=3 ,   __snake_case       :      Any=18 ,   __snake_case       :      str=30 ,   __snake_case       :      Any=4_00 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      str=None ,   __snake_case       :      Any=True ,   __snake_case       :      List[Any]=None ,   ):
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 20}
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					num_channels
         UpperCAmelCase_										=					image_size
         UpperCAmelCase_										=					min_resolution
         UpperCAmelCase_										=					max_resolution
         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size




     def   lowerCamelCase_       (     self       :      Any    ):
         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       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  MobileNetVaImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					MobileNetVaImageProcessingTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         return self.image_processor_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					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 ,   '''do_center_crop'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''crop_size'''    )    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					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}    )

         UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Optional[int]    ):
         pass
     def   lowerCamelCase_       (     self       :      Tuple    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )
     def   lowerCamelCase_       (     self       :      str    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )




     def   lowerCamelCase_       (     self       :      int    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             ) ,   )





