'''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


__SCREAMING_SNAKE_CASE       :     List[str]     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						["pixel_values"]




      def __init__(       self						:				Optional[int]						,					A						:				bool = True						,					A						:				Dict[str, int] = None						,					A						:				float = None						,					A						:				PILImageResampling = PILImageResampling.BILINEAR						,					A						:				bool = True						,					A						:				Union[int, float] = 1 / 255						,					A						:				bool = True						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					**A						:				int						,					):
             super().__init__(**A       )
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 384}
             _UpperCAmelCase     :  Union[str, Any]							      =    get_size_dict(A						,					default_to_square=A       )

             _UpperCAmelCase     :  int							      =    do_resize
             _UpperCAmelCase     :  Optional[int]							      =    size
             # Default value set here for backwards compatibility where the value in config is None
             _UpperCAmelCase     :  str							      =    crop_pct if crop_pct is not None else 224 / 256
             _UpperCAmelCase     :  Tuple							      =    resample
             _UpperCAmelCase     :  Union[str, Any]							      =    do_rescale
             _UpperCAmelCase     :  List[str]							      =    rescale_factor
             _UpperCAmelCase     :  Tuple							      =    do_normalize
             _UpperCAmelCase     :  Optional[Any]							      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             _UpperCAmelCase     :  Optional[Any]							      =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def      _A	(       self						:				List[Any]						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				float						,					A						:				PILImageResampling = PILImageResampling.BICUBIC						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Any						,					):
             _UpperCAmelCase     :  Optional[int]							      =    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()}"""       )
             _UpperCAmelCase     :  Optional[Any]							      =    size["shortest_edge"]

             if shortest_edge < 384:
                    # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                    _UpperCAmelCase     :  Dict							      =    int(shortest_edge / crop_pct       )
                    _UpperCAmelCase     :  Optional[Any]							      =    get_resize_output_image_size(A						,					size=A						,					default_to_square=A       )
                    _UpperCAmelCase     :  int							      =    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      _A	(       self						:				Dict						,					A						:				np.ndarray						,					A						:				Union[int, float]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				int						,					):
             return rescale(A						,					scale=A						,					data_format=A						,					**A       )




      def      _A	(       self						:				List[Any]						,					A						:				np.ndarray						,					A						:				Union[float, List[float]]						,					A						:				Union[float, List[float]]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Optional[int]						,					):
             return normalize(A						,					mean=A						,					std=A						,					data_format=A						,					**A       )





      def      _A	(       self						:				str						,					A						:				ImageInput						,					A						:				bool = None						,					A						:				Dict[str, int] = None						,					A						:				float = None						,					A						:				PILImageResampling = None						,					A						:				bool = None						,					A						:				float = None						,					A						:				bool = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					A						:				ChannelDimension = ChannelDimension.FIRST						,					**A						:				List[Any]						,					):
             _UpperCAmelCase     :  Optional[int]							      =    do_resize if do_resize is not None else self.do_resize
             _UpperCAmelCase     :  str							      =    crop_pct if crop_pct is not None else self.crop_pct
             _UpperCAmelCase     :  Dict							      =    resample if resample is not None else self.resample
             _UpperCAmelCase     :  List[Any]							      =    do_rescale if do_rescale is not None else self.do_rescale
             _UpperCAmelCase     :  Optional[Any]							      =    rescale_factor if rescale_factor is not None else self.rescale_factor
             _UpperCAmelCase     :  str							      =    do_normalize if do_normalize is not None else self.do_normalize
             _UpperCAmelCase     :  Optional[int]							      =    image_mean if image_mean is not None else self.image_mean
             _UpperCAmelCase     :  Dict							      =    image_std if image_std is not None else self.image_std

             _UpperCAmelCase     :  Tuple							      =    size if size is not None else self.size
             _UpperCAmelCase     :  List[str]							      =    get_size_dict(A						,					default_to_square=A       )

             _UpperCAmelCase     :  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"] < 384 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.
             _UpperCAmelCase     :  List[Any]							      =    [to_numpy_array(A       ) for image in images]

             if do_resize:
                    _UpperCAmelCase     :  List[Any]							      =    [self.resize(image=A						,					size=A						,					crop_pct=A						,					resample=A       ) for image in images]

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

             if do_normalize:
                    _UpperCAmelCase     :  Any							      =    [self.normalize(image=A						,					mean=A						,					std=A       ) for image in images]

             _UpperCAmelCase     :  Any							      =    [to_channel_dimension_format(A						,					A       ) for image in images]

             _UpperCAmelCase     :  Optional[Any]							      =    {"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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             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 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  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      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    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     :  Dict							      =    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      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, 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
             _UpperCAmelCase     :  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
             _UpperCAmelCase     :  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"],
                 )						,					)




      def      _A	(       self						:				Union[str, Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Dict							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  Optional[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
             _UpperCAmelCase     :  Optional[int]							      =    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     :  Optional[int]							      =    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      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  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
             _UpperCAmelCase     :  List[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
             _UpperCAmelCase     :  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"],
                 )						,					)

