



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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase	:					Union[str, Any]			  =					logging.get_logger(__name__)



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :str						   =       ['pixel_values']





      def __init__(			self	,							A_ = True	,							A_ = None	,							A_ = PILImageResampling.BICUBIC	,							A_ = True	,							A_ = True	,							A_ = 1 / 255	,							A_ = None	,							A_ = True	,							A_ = None	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       super().__init__(**A_       )
       UpperCamelCase     : str	     =       size if size is not None else {"height": 224, "width": 224}
       UpperCamelCase     : Optional[Any]	     =       get_size_dict(A_       )
       UpperCamelCase     : Optional[int]	     =       crop_size if crop_size is not None else {"height": 224, "width": 224}
       UpperCamelCase     : Optional[int]	     =       get_size_dict(A_	,							default_to_square=A_	,							param_name="crop_size"       )

       UpperCamelCase     : Union[str, Any]	     =       do_resize
       UpperCamelCase     : Any	     =       do_rescale
       UpperCamelCase     : Any	     =       do_normalize
       UpperCamelCase     : Union[str, Any]	     =       do_center_crop
       UpperCamelCase     : Optional[Any]	     =       crop_size
       UpperCamelCase     : Tuple	     =       size
       UpperCamelCase     : Optional[int]	     =       resample
       UpperCamelCase     : List[str]	     =       rescale_factor
       UpperCamelCase     : int	     =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       UpperCamelCase     : Optional[int]	     =       image_std if image_std is not None else IMAGENET_DEFAULT_STD





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = PILImageResampling.BILINEAR	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       get_size_dict(A_       )

       if "shortest_edge" in size:
        UpperCamelCase     : Optional[int]	     =       get_resize_output_image_size(A_	,							size=size["shortest_edge"]	,							default_to_square=A_       )
        # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
       elif "height" in size and "width" in size:
        UpperCamelCase     : Tuple	     =       (size["height"], size["width"])
       else:
        raise ValueError(F"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""       )
       return resize(A_	,							size=A_	,							resample=A_	,							data_format=A_	,							**A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       get_size_dict(A_       )
       if "height" not in size or "width" not in size:
        raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""       )
       return center_crop(A_	,							size=(size["height"], size["width"])	,							data_format=A_	,							**A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_ = None	,							**A_       ):
       '''simple docstring'''




       return rescale(A_	,							scale=A_	,							data_format=A_	,							**A_       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = None	,							A_ = ChannelDimension.FIRST	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       do_resize if do_resize is not None else self.do_resize
       UpperCamelCase     : Optional[Any]	     =       do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase     : Optional[Any]	     =       do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase     : List[str]	     =       do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase     : int	     =       crop_size if crop_size is not None else self.crop_size
       UpperCamelCase     : str	     =       get_size_dict(A_	,							param_name="crop_size"	,							default_to_square=A_       )
       UpperCamelCase     : Optional[Any]	     =       resample if resample is not None else self.resample
       UpperCamelCase     : List[Any]	     =       rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase     : Optional[Any]	     =       image_mean if image_mean is not None else self.image_mean
       UpperCamelCase     : Optional[Any]	     =       image_std if image_std is not None else self.image_std

       UpperCamelCase     : str	     =       size if size is not None else self.size
       UpperCamelCase     : List[str]	     =       get_size_dict(A_       )

       if not is_batched(A_       ):
        UpperCamelCase     : Dict	     =       [images]

       if not valid_images(A_       ):
        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."       )

       # All transformations expect numpy arrays.
       UpperCamelCase     : str	     =       [to_numpy_array(A_       ) for image in images]

       if do_resize:
        UpperCamelCase     : Union[str, Any]	     =       [self.resize(image=A_	,							size=A_	,							resample=A_       ) for image in images]

       if do_center_crop:
        UpperCamelCase     : int	     =       [self.center_crop(image=A_	,							size=A_       ) for image in images]

       if do_rescale:
        UpperCamelCase     : Any	     =       [self.rescale(image=A_	,							scale=A_       ) for image in images]

       if do_normalize:
        UpperCamelCase     : Dict	     =       [self.normalize(image=A_	,							mean=A_	,							std=A_       ) for image in images]

       UpperCamelCase     : List[str]	     =       [to_channel_dimension_format(A_	,							A_       ) for image in images]

       UpperCamelCase     : Any	     =       {"pixel_values": images}
       return BatchFeature(data=A_	,							tensor_type=A_       )







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 ViTImageProcessor



class  A__       (	unittest.TestCase      ):





      def __init__(			self	,							A_	,							A_=13	,							A_=3	,							A_=224	,							A_=30	,							A_=400	,							A_=True	,							A_=None	,							A_=True	,							A_=[0.5, 0.5, 0.5]	,							A_=[0.5, 0.5, 0.5]	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       size if size is not None else {"height": 18, "width": 18}
       UpperCamelCase     : Tuple	     =       parent
       UpperCamelCase     : Tuple	     =       batch_size
       UpperCamelCase     : Tuple	     =       num_channels
       UpperCamelCase     : str	     =       image_size
       UpperCamelCase     : Optional[int]	     =       min_resolution
       UpperCamelCase     : List[Any]	     =       max_resolution
       UpperCamelCase     : Union[str, Any]	     =       do_resize
       UpperCamelCase     : str	     =       size
       UpperCamelCase     : List[str]	     =       do_normalize
       UpperCamelCase     : Any	     =       image_mean
       UpperCamelCase     : int	     =       image_std





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }



@require_torch
@require_vision
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Tuple						   =       ViTImageProcessor if is_vision_available() else None





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       EfficientFormerImageProcessorTester(self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.image_proc_tester.prepare_image_processor_dict()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(A_	,							"image_mean"       )       )
       self.assertTrue(hasattr(A_	,							"image_std"       )       )
       self.assertTrue(hasattr(A_	,							"do_normalize"       )       )
       self.assertTrue(hasattr(A_	,							"do_resize"       )       )
       self.assertTrue(hasattr(A_	,							"size"       )       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       pass





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PIL images
       UpperCamelCase     : Union[str, Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							Image.Image       )

       # Test not batched input
       UpperCamelCase     : Optional[Any]	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Tuple	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       UpperCamelCase     : List[Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							numpify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							np.ndarray       )

       # Test not batched input
       UpperCamelCase     : Dict	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Optional[Any]	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PyTorch tensors
       UpperCamelCase     : Any	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							torchify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							torch.Tensor       )

       # Test not batched input
       UpperCamelCase     : Any	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Any	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)
