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


UpperCAmelCase__     						=  logging.get_logger(__name__)


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Optional[int]   							=							['pixel_values']


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     Optional[Dict[str, int]] = None      , __lowerCAmelCase		:     PILImageResampling = PILImageResampling.BICUBIC      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     Union[int, float] = 1 / 255      , __lowerCAmelCase		:     Dict[str, int] = None      , __lowerCAmelCase		:     bool = True      , __lowerCAmelCase		:     Optional[Union[float, List[float]]] = None      , __lowerCAmelCase		:     Optional[Union[float, List[float]]] = None      , **__lowerCAmelCase		:     Tuple      , ):
      super().__init__(**__lowerCAmelCase    )
      _UpperCAmelCase         =					size if size is not None else {"""height""": 224, """width""": 224}
      _UpperCAmelCase         =					get_size_dict(__lowerCAmelCase    )
      _UpperCAmelCase         =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      _UpperCAmelCase         =					get_size_dict(__lowerCAmelCase      , default_to_square=__lowerCAmelCase      , param_name="""crop_size"""    )

      _UpperCAmelCase         =					do_resize
      _UpperCAmelCase         =					do_rescale
      _UpperCAmelCase         =					do_normalize
      _UpperCAmelCase         =					do_center_crop
      _UpperCAmelCase         =					crop_size
      _UpperCAmelCase         =					size
      _UpperCAmelCase         =					resample
      _UpperCAmelCase         =					rescale_factor
      _UpperCAmelCase         =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      _UpperCAmelCase         =					image_std if image_std is not None else IMAGENET_DEFAULT_STD


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     np.ndarray      , __lowerCAmelCase		:     Dict[str, int]      , __lowerCAmelCase		:     PILImageResampling = PILImageResampling.BILINEAR      , __lowerCAmelCase		:     Optional[Union[str, ChannelDimension]] = None      , **__lowerCAmelCase		:     Optional[Any]      , ):
      _UpperCAmelCase         =					get_size_dict(__lowerCAmelCase    )

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


    def    lowerCAmelCase_     (    self		:     Optional[Any]      , __lowerCAmelCase		:     np.ndarray      , __lowerCAmelCase		:     Dict[str, int]      , __lowerCAmelCase		:     Optional[Union[str, ChannelDimension]] = None      , **__lowerCAmelCase		:     Any      , ):
      _UpperCAmelCase         =					get_size_dict(__lowerCAmelCase    )
      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(__lowerCAmelCase      , size=(size["""height"""], size["""width"""])      , data_format=__lowerCAmelCase      , **__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     np.ndarray      , __lowerCAmelCase		:     float      , __lowerCAmelCase		:     Optional[Union[str, ChannelDimension]] = None      , **__lowerCAmelCase		:     List[str]    ):
      return rescale(__lowerCAmelCase      , scale=__lowerCAmelCase      , data_format=__lowerCAmelCase      , **__lowerCAmelCase    )


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


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     ImageInput      , __lowerCAmelCase		:     Optional[bool] = None      , __lowerCAmelCase		:     Dict[str, int] = None      , __lowerCAmelCase		:     PILImageResampling = None      , __lowerCAmelCase		:     bool = None      , __lowerCAmelCase		:     int = None      , __lowerCAmelCase		:     Optional[bool] = None      , __lowerCAmelCase		:     Optional[float] = None      , __lowerCAmelCase		:     Optional[bool] = None      , __lowerCAmelCase		:     Optional[Union[float, List[float]]] = None      , __lowerCAmelCase		:     Optional[Union[float, List[float]]] = None      , __lowerCAmelCase		:     Optional[Union[str, TensorType]] = None      , __lowerCAmelCase		:     Union[str, ChannelDimension] = ChannelDimension.FIRST      , **__lowerCAmelCase		:     Union[str, Any]      , ):
      _UpperCAmelCase         =					do_resize if do_resize is not None else self.do_resize
      _UpperCAmelCase         =					do_rescale if do_rescale is not None else self.do_rescale
      _UpperCAmelCase         =					do_normalize if do_normalize is not None else self.do_normalize
      _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(__lowerCAmelCase      , param_name="""crop_size"""      , default_to_square=__lowerCAmelCase    )
      _UpperCAmelCase         =					resample if resample is not None else self.resample
      _UpperCAmelCase         =					rescale_factor if rescale_factor is not None else self.rescale_factor
      _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         =					size if size is not None else self.size
      _UpperCAmelCase         =					get_size_dict(__lowerCAmelCase    )

      if not is_batched(__lowerCAmelCase    ):
        _UpperCAmelCase         =					[images]

      if not valid_images(__lowerCAmelCase    ):
        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         =					[to_numpy_array(__lowerCAmelCase    ) for image in images]

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

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

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

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

      _UpperCAmelCase         =					[to_channel_dimension_format(__lowerCAmelCase      , __lowerCAmelCase    ) for image in images]

      _UpperCAmelCase         =					{"""pixel_values""": images}
      return BatchFeature(data=__lowerCAmelCase      , tensor_type=__lowerCAmelCase    )

"""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 				a (						unittest.TestCase      ):


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[Any]=7      , __lowerCAmelCase		:     Optional[Any]=3      , __lowerCAmelCase		:     Optional[Any]=18      , __lowerCAmelCase		:     str=30      , __lowerCAmelCase		:     List[str]=400      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     List[str]=True      , ):
      _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
      _UpperCAmelCase         =					do_flip_channel_order


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      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 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							MobileViTImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					MobileViTImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_resize"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """size"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_flip_channel_order"""    )    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _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		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Dict    ):
      # 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=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , 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(__lowerCAmelCase      , 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=__lowerCAmelCase      , numpify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , 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(__lowerCAmelCase      , 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		:     Optional[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=__lowerCAmelCase      , torchify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , 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(__lowerCAmelCase      , 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"""],
          )      , )
