"""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,
    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, is_vision_available, logging


if is_vision_available():
       import PIL


_UpperCamelCase     :		Optional[Any]											=					logging.get_logger(__name__)


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					int										=						["pixel_values"]


      def __init__(     self	,    a = True	,    a = None	,    a = None	,    a = PILImageResampling.BILINEAR	,    a = True	,    a = 1 / 2_5_5	,    a = True	,    a = None	,    a = None	,    **a	,    ) ->      None:
             super().__init__(**a							)
             lowercase__    :	Union[str, Any]     					=			size if size is not None else {'shortest_edge': 3_8_4}
             lowercase__    :	int     					=			get_size_dict(a	,    default_to_square=a							)

             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	Union[str, Any]     					=			size
             # Default value set here for backwards compatibility where the value in config is None
             lowercase__    :	int     					=			crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
             lowercase__    :	List[str]     					=			resample
             lowercase__    :	List[Any]     					=			do_rescale
             lowercase__    :	Any     					=			rescale_factor
             lowercase__    :	List[str]     					=			do_normalize
             lowercase__    :	Any     					=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             lowercase__    :	List[str]     					=			image_std if image_std is not None else IMAGENET_STANDARD_STD


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a = PILImageResampling.BICUBIC	,    a = None	,    **a	,    ) ->      np.ndarray:
             lowercase__    :	List[str]     					=			get_size_dict(a	,    default_to_square=a							)
             if "shortest_edge" not in size:
                    raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""							)
             lowercase__    :	Tuple     					=			size['shortest_edge']

             if shortest_edge < 3_8_4:
                    # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                    lowercase__    :	List[Any]     					=			int(shortest_edge / crop_pct							)
                    lowercase__    :	List[Any]     					=			get_resize_output_image_size(a	,    size=a	,    default_to_square=a							)
                    lowercase__    :	Optional[Any]     					=			resize(image=a	,    size=a	,    resample=a	,    data_format=a	,    **a							)
                    # then crop to (shortest_edge, shortest_edge)
                    return center_crop(image=a	,    size=(shortest_edge, shortest_edge)	,    data_format=a	,    **a							)
             else:
                    # warping (no cropping) when evaluated at 384 or larger
                    return resize(
                        a	,    size=(shortest_edge, shortest_edge)	,    resample=a	,    data_format=a	,    **a							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a = None	,    **a	,    ) ->      Dict:
             return rescale(a	,    scale=a	,    data_format=a	,    **a							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a = None	,    **a	,    ) ->      np.ndarray:
             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 = ChannelDimension.FIRST	,    **a	,    ) ->      PIL.Image.Image:
             lowercase__    :	Optional[Any]     					=			do_resize if do_resize is not None else self.do_resize
             lowercase__    :	Optional[int]     					=			crop_pct if crop_pct is not None else self.crop_pct
             lowercase__    :	str     					=			resample if resample is not None else self.resample
             lowercase__    :	Optional[int]     					=			do_rescale if do_rescale is not None else self.do_rescale
             lowercase__    :	List[Any]     					=			rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__    :	List[str]     					=			do_normalize if do_normalize is not None else self.do_normalize
             lowercase__    :	Tuple     					=			image_mean if image_mean is not None else self.image_mean
             lowercase__    :	Union[str, Any]     					=			image_std if image_std is not None else self.image_std

             lowercase__    :	Dict     					=			size if size is not None else self.size
             lowercase__    :	str     					=			get_size_dict(a	,    default_to_square=a							)

             lowercase__    :	List[Any]     					=			make_list_of_images(a							)

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

             if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
                    raise ValueError('crop_pct must be specified if size < 384.'							)

             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.
             lowercase__    :	Union[str, Any]     					=			[to_numpy_array(a							) for image in images]

             if do_resize:
                    lowercase__    :	str     					=			[self.resize(image=a	,    size=a	,    crop_pct=a	,    resample=a							) for image in images]

             if do_rescale:
                    lowercase__    :	List[Any]     					=			[self.rescale(image=a	,    scale=a							) for image in images]

             if do_normalize:
                    lowercase__    :	int     					=			[self.normalize(image=a	,    mean=a	,    std=a							) for image in images]

             lowercase__    :	Tuple     					=			[to_channel_dimension_format(a	,    a							) for image in images]

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


"""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   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=7	,    a=3	,    a=1_8	,    a=3_0	,    a=4_0_0	,    a=True	,    a=None	,    a=True	,    a=None	,    a=True	,    ) ->      List[str]:
             lowercase__    :	Tuple     					=			size if size is not None else {'shortest_edge': 2_0}
             lowercase__    :	Union[str, Any]     					=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
             lowercase__    :	Optional[int]     					=			parent
             lowercase__    :	Optional[int]     					=			batch_size
             lowercase__    :	str     					=			num_channels
             lowercase__    :	Any     					=			image_size
             lowercase__    :	Optional[Any]     					=			min_resolution
             lowercase__    :	int     					=			max_resolution
             lowercase__    :	List[Any]     					=			do_resize
             lowercase__    :	List[str]     					=			size
             lowercase__    :	str     					=			do_center_crop
             lowercase__    :	List[Any]     					=			crop_size
             lowercase__    :	Union[str, Any]     					=			do_flip_channel_order


      def 	_UpperCAmelCase (     self							) ->      int:
             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   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[Any]										=						MobileViTImageProcessor if is_vision_available() else None


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Tuple     					=			MobileViTImageProcessingTester(self							)


      @property
      def 	_UpperCAmelCase (     self							) ->      int:
             return self.image_processor_tester.prepare_image_processor_dict()


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	List[str]     					=			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							) ->      List[str]:
             lowercase__    :	Optional[int]     					=			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}							)

             lowercase__    :	str     					=			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 	_UpperCAmelCase (     self							) ->      Tuple:
             pass


      def 	_UpperCAmelCase (     self							) ->      str:
             # Initialize image_processing
             lowercase__    :	Any     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PIL images
             lowercase__    :	List[str]     					=			prepare_image_inputs(self.image_processor_tester	,    equal_resolution=a							)
             for image in image_inputs:
                    self.assertIsInstance(a	,    Image.Image							)

             # Test not batched input
             lowercase__    :	Union[str, Any]     					=			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
             lowercase__    :	List[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							) ->      Tuple:
             # Initialize image_processing
             lowercase__    :	Dict     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random numpy tensors
             lowercase__    :	Tuple     					=			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
             lowercase__    :	List[Any]     					=			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
             lowercase__    :	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							) ->      Dict:
             # Initialize image_processing
             lowercase__    :	str     					=			self.image_processing_class(**self.image_processor_dict							)
             # create random PyTorch tensors
             lowercase__    :	Optional[Any]     					=			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
             lowercase__    :	Union[str, Any]     					=			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
             lowercase__    :	Tuple     					=			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'],
                 )	,    )

