

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__UpperCAmelCase     					=  logging.get_logger(__name__)


if is_vision_available():
  import PIL





class A__     (			A			):



 """simple docstring"""







 _lowercase  :       List[str]  			=    ['''pixel_values''']

 def __init__(		self			:						Union[str, Any]  ,    A_			:						bool = True  ,    A_			:						Dict[str, int] = None  ,    A_			:						PILImageResampling = PILImageResampling.BICUBIC  ,    A_			:						bool = True  ,    A_			:						Dict[str, int] = None  ,    A_			:						bool = True  ,    A_			:						Union[int, float] = 1 / 2_5_5  ,    A_			:						bool = True  ,    A_			:						Optional[Union[float, List[float]]] = None  ,    A_			:						Optional[Union[float, List[float]]] = None  ,    A_			:						bool = True  ,    **A_			:						Optional[int]  ,    ):






      '''simple docstring'''




      super().__init__(**A_							)
      _lowerCAmelCase :       Any	   =     size if size is not None else {"shortest_edge": 2_2_4}
      _lowerCAmelCase :       Optional[Any]	   =     get_size_dict(A_  ,    default_to_square=A_							)
      _lowerCAmelCase :       int	   =     crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase :       Union[str, Any]	   =     get_size_dict(A_							)

      _lowerCAmelCase :       Dict	   =     do_resize
      _lowerCAmelCase :       List[Any]	   =     size
      _lowerCAmelCase :       str	   =     resample
      _lowerCAmelCase :       List[str]	   =     do_center_crop
      _lowerCAmelCase :       Any	   =     crop_size
      _lowerCAmelCase :       Any	   =     do_rescale
      _lowerCAmelCase :       List[Any]	   =     rescale_factor
      _lowerCAmelCase :       Dict	   =     do_normalize
      _lowerCAmelCase :       Union[str, Any]	   =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      _lowerCAmelCase :       Optional[Any]	   =     image_std if image_std is not None else OPENAI_CLIP_STD
      _lowerCAmelCase :       List[str]	   =     do_convert_rgb

 def  __magic_name__							(		self			:						Union[str, Any]  ,    A_			:						np.ndarray  ,    A_			:						Dict[str, int]  ,    A_			:						PILImageResampling = PILImageResampling.BICUBIC  ,    A_			:						Optional[Union[str, ChannelDimension]] = None  ,    **A_			:						str  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     get_size_dict(A_  ,    default_to_square=A_							)
      _lowerCAmelCase :       List[Any]	   =     get_resize_output_image_size(
          A_  ,    size=(size["height"], size["width"])  ,    default_to_square=A_							)
      return resize(A_  ,    size=A_  ,    resample=A_  ,    data_format=A_  ,    **A_							)

 def  __magic_name__							(		self			:						List[Any]  ,    A_			:						np.ndarray  ,    A_			:						Dict[str, int]  ,    A_			:						Optional[Union[str, ChannelDimension]] = None  ,    **A_			:						Dict  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     get_size_dict(A_							)
      return center_crop(A_  ,    size=(size["height"], size["width"])  ,    data_format=A_  ,    **A_							)

 def  __magic_name__							(		self			:						List[Any]  ,    A_			:						np.ndarray  ,    A_			:						Union[int, float]  ,    A_			:						Optional[Union[str, ChannelDimension]] = None  ,    **A_			:						List[Any]  ,    ):






      '''simple docstring'''




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

 def  __magic_name__							(		self			:						Optional[Any]  ,    A_			:						np.ndarray  ,    A_			:						Union[float, List[float]]  ,    A_			:						Union[float, List[float]]  ,    A_			:						Optional[Union[str, ChannelDimension]] = None  ,    **A_			:						Any  ,    ):






      '''simple docstring'''




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


 def  __magic_name__							(		self			:						Optional[Any]  ,    A_			:						ImageInput  ,    A_			:						bool = None  ,    A_			:						Dict[str, int] = None  ,    A_			:						PILImageResampling = None  ,    A_			:						bool = None  ,    A_			:						int = 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_			:						bool = None  ,    A_			:						Optional[Union[str, TensorType]] = None  ,    A_			:						Optional[ChannelDimension] = ChannelDimension.FIRST  ,    **A_			:						List[Any]  ,    ):






      '''simple docstring'''




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

      _lowerCAmelCase :       Optional[int]	   =     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:
           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."							)

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
           _lowerCAmelCase :       int	   =     [convert_to_rgb(A_							) for image in images]

      # All transformations expect numpy arrays.
      _lowerCAmelCase :       Optional[Any]	   =     [to_numpy_array(A_							) for image in images]

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

      if do_center_crop:
           _lowerCAmelCase :       Union[str, Any]	   =     [self.center_crop(image=A_  ,    size=A_							) for image in images]

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

      if do_normalize:
           _lowerCAmelCase :       List[Any]	   =     [self.normalize(image=A_  ,    mean=A_  ,    std=A_							) for image in images]

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

      _lowerCAmelCase :       Dict	   =     {"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 ConvNextImageProcessor





class A__     (			unittest.TestCase			):



 """simple docstring"""







 def __init__(		self			:						Optional[Any]  ,    A_			:						Union[str, Any]  ,    A_			:						List[Any]=7  ,    A_			:						Optional[Any]=3  ,    A_			:						Optional[int]=1_8  ,    A_			:						Dict=3_0  ,    A_			:						Dict=4_0_0  ,    A_			:						List[Any]=True  ,    A_			:						int=None  ,    A_			:						List[Any]=0.875  ,    A_			:						List[str]=True  ,    A_			:						List[Any]=[0.5, 0.5, 0.5]  ,    A_			:						Optional[Any]=[0.5, 0.5, 0.5]  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       List[str]	   =     size if size is not None else {"shortest_edge": 2_0}
      _lowerCAmelCase :       Union[str, Any]	   =     parent
      _lowerCAmelCase :       int	   =     batch_size
      _lowerCAmelCase :       Any	   =     num_channels
      _lowerCAmelCase :       str	   =     image_size
      _lowerCAmelCase :       List[Any]	   =     min_resolution
      _lowerCAmelCase :       Tuple	   =     max_resolution
      _lowerCAmelCase :       Any	   =     do_resize
      _lowerCAmelCase :       List[str]	   =     size
      _lowerCAmelCase :       List[str]	   =     crop_pct
      _lowerCAmelCase :       Tuple	   =     do_normalize
      _lowerCAmelCase :       int	   =     image_mean
      _lowerCAmelCase :       Dict	   =     image_std


 def  __magic_name__							(		self			:						Dict							):






      '''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,
          "crop_pct": self.crop_pct,
      }





@require_torch
@require_vision
class A__     (			A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       int  			=    ConvNextImageProcessor if is_vision_available() else None

 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     ConvNextImageProcessingTester(self							)

 @property
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(A_  ,    "do_resize"							)							)
      self.assertTrue(hasattr(A_  ,    "size"							)							)
      self.assertTrue(hasattr(A_  ,    "crop_pct"							)							)
      self.assertTrue(hasattr(A_  ,    "do_normalize"							)							)
      self.assertTrue(hasattr(A_  ,    "image_mean"							)							)
      self.assertTrue(hasattr(A_  ,    "image_std"							)							)

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 2_0}							)

      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class.from_dict(self.image_processor_dict  ,    size=4_2							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 4_2}							)

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      pass

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      _lowerCAmelCase :       Tuple	   =     prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    Image.Image							)

      # Test not batched input
      _lowerCAmelCase :       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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random numpy tensors
      _lowerCAmelCase :       str	   =     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
      _lowerCAmelCase :       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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )


 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PyTorch tensors
      _lowerCAmelCase :       List[str]	   =     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
      _lowerCAmelCase :       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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Union[str, 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.size["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )






