






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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_UpperCAmelCase					:						Tuple         =     logging.get_logger(__name__)


class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Optional[Any] 	=     ['''pixel_values''']





     def __init__(	self							, _UpperCAmelCase = True							, _UpperCAmelCase = None							, _UpperCAmelCase = True							, _UpperCAmelCase = None							, _UpperCAmelCase = PILImageResampling.BICUBIC							, _UpperCAmelCase = True							, _UpperCAmelCase = 1 / 255							, _UpperCAmelCase = True							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, **_UpperCAmelCase							, )		->				None:
        super().__init__(**_UpperCAmelCase  )
        lowerCamelCase									=crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
        lowerCamelCase									=get_size_dict(_UpperCAmelCase							, param_name="""crop_size"""  )
        lowerCamelCase									=size if size is not None else {"""height""": 224, """width""": 224}
        lowerCamelCase									=get_size_dict(_UpperCAmelCase  )

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





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, **_UpperCAmelCase							, )		->				np.ndarray:
        lowerCamelCase									=self.size if size is None else size
        lowerCamelCase									=get_size_dict(_UpperCAmelCase  )
        lowerCamelCase									=get_size_dict(_UpperCAmelCase							, param_name="""crop_size"""  )

        lowerCamelCase				,       lowerCamelCase									=get_image_size(_UpperCAmelCase  )
        lowerCamelCase									=min(_UpperCAmelCase							, _UpperCAmelCase  )
        lowerCamelCase									=(size["""height"""] / crop_size["""height"""]) * min_dim
        lowerCamelCase									=(size["""width"""] / crop_size["""width"""]) * min_dim
        return center_crop(_UpperCAmelCase							, size=(cropped_height, cropped_width)							, data_format=_UpperCAmelCase							, **_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase = PIL.Image.BICUBIC							, _UpperCAmelCase = None							, **_UpperCAmelCase							, )		->				np.ndarray:
        lowerCamelCase									=get_size_dict(_UpperCAmelCase  )
        if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''  )
        return resize(
            _UpperCAmelCase							, size=(size["""height"""], size["""width"""])							, resample=_UpperCAmelCase							, data_format=_UpperCAmelCase							, **_UpperCAmelCase  )





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





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase = None							, **_UpperCAmelCase							, )		->				np.ndarray:
        return normalize(_UpperCAmelCase							, mean=_UpperCAmelCase							, std=_UpperCAmelCase							, data_format=_UpperCAmelCase							, **_UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = None							, _UpperCAmelCase = ChannelDimension.FIRST							, **_UpperCAmelCase							, )		->				PIL.Image.Image:
        lowerCamelCase									=do_center_crop if do_center_crop is not None else self.do_center_crop
        lowerCamelCase									=crop_size if crop_size is not None else self.crop_size
        lowerCamelCase									=get_size_dict(_UpperCAmelCase							, param_name="""crop_size"""  )
        lowerCamelCase									=do_resize if do_resize is not None else self.do_resize
        lowerCamelCase									=size if size is not None else self.size
        lowerCamelCase									=get_size_dict(_UpperCAmelCase  )
        lowerCamelCase									=resample if resample is not None else self.resample
        lowerCamelCase									=do_rescale if do_rescale is not None else self.do_rescale
        lowerCamelCase									=rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCamelCase									=do_normalize if do_normalize is not None else self.do_normalize
        lowerCamelCase									=image_mean if image_mean is not None else self.image_mean
        lowerCamelCase									=image_std if image_std is not None else self.image_std

        lowerCamelCase									=make_list_of_images(_UpperCAmelCase  )

        if not valid_images(_UpperCAmelCase  ):
           raise ValueError(
               """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
               """torch.Tensor, tf.Tensor or jax.ndarray."""  )

        if do_center_crop and crop_size is None:
           raise ValueError("""If `do_center_crop` is set to `True`, `crop_size` must be provided."""  )

        if do_resize and size is None:
           raise ValueError("""Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."""  )

        # All transformations expect numpy arrays.
        lowerCamelCase									=[to_numpy_array(_UpperCAmelCase  ) for image in images]

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

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

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

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

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

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







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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=1 / 255							, _UpperCAmelCase=True							, _UpperCAmelCase=8							, )		->				Tuple:
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_rescale
        lowerCamelCase									=rescale_factor
        lowerCamelCase									=do_pad
        lowerCamelCase									=pad_size





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				int:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[Any] 	=     SwinaSRImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=SwinaSRImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_rescale"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """rescale_factor"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_pad"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """pad_size"""  )  )





     def 						lowerCamelCase							(	self  )		->				Any:
        pass





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=get_image_size(_UpperCAmelCase  )
        lowerCamelCase									=self.image_processor_tester.pad_size

        lowerCamelCase									=(old_height // size + 1) * size - old_height
        lowerCamelCase									=(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Any:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )