
from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
     import torch


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


class        lowercase      (	lowercase_     ):
  __lowercase : Optional[int]			  =       ['''pixel_values''']



  def __init__(				self       ,   A_ = True       ,   A_ = None       ,   A_ = PILImageResampling.BILINEAR       ,   A_ = True       ,   A_ = None       ,   A_ = True       ,   A_ = 1 / 255       ,   A_ = True       ,   A_ = None       ,   A_ = None       ,   **A_       ,   )						->      Tuple:
    """simple docstring"""
    super().__init__(**UpperCamelCase__ )
    UpperCamelCase						=  size if size is not None else {'shortest_edge': 256}
    UpperCamelCase						=  get_size_dict(UpperCamelCase__       ,   default_to_square=UpperCamelCase__ )
    UpperCamelCase						=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
    UpperCamelCase						=  get_size_dict(UpperCamelCase__       ,   param_name='crop_size' )
    UpperCamelCase						=  do_resize
    UpperCamelCase						=  size
    UpperCamelCase						=  resample
    UpperCamelCase						=  do_center_crop
    UpperCamelCase						=  crop_size
    UpperCamelCase						=  do_rescale
    UpperCamelCase						=  rescale_factor
    UpperCamelCase						=  do_normalize
    UpperCamelCase						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    UpperCamelCase						=  image_std if image_std is not None else IMAGENET_STANDARD_STD



  def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ = PILImageResampling.BICUBIC       ,   A_ = None       ,   **A_       ,   )						->      List[str]:
    """simple docstring"""
    UpperCamelCase						=  get_size_dict(UpperCamelCase__       ,   default_to_square=UpperCamelCase__ )
    if "shortest_edge" not in size:
      raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
    UpperCamelCase						=  get_resize_output_image_size(UpperCamelCase__       ,   size=size['shortest_edge']       ,   default_to_square=UpperCamelCase__ )
    return resize(UpperCamelCase__       ,   size=UpperCamelCase__       ,   resample=UpperCamelCase__       ,   data_format=UpperCamelCase__       ,   **UpperCamelCase__ )



  def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ = None       ,   **A_       ,   )						->      Optional[int]:
    """simple docstring"""
    UpperCamelCase						=  get_size_dict(UpperCamelCase__ )
    if "height" not in size or "width" not in size:
      raise ValueError(F'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}''' )
    return center_crop(UpperCamelCase__       ,   size=(size['height'], size['width'])       ,   data_format=UpperCamelCase__       ,   **UpperCamelCase__ )



  def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ = None       ,   **A_ )						->      Optional[int]:
    """simple docstring"""
    return rescale(UpperCamelCase__       ,   scale=UpperCamelCase__       ,   data_format=UpperCamelCase__       ,   **UpperCamelCase__ )



  def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_ = None       ,   **A_       ,   )						->      List[str]:
    """simple docstring"""
    return normalize(UpperCamelCase__       ,   mean=UpperCamelCase__       ,   std=UpperCamelCase__       ,   data_format=UpperCamelCase__       ,   **UpperCamelCase__ )



  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_ = None       ,   A_ = ChannelDimension.FIRST       ,   **A_       ,   )						->      Optional[int]:
    """simple docstring"""
    UpperCamelCase						=  do_resize if do_resize is not None else self.do_resize
    UpperCamelCase						=  size if size is not None else self.size
    UpperCamelCase						=  get_size_dict(UpperCamelCase__       ,   default_to_square=UpperCamelCase__ )
    UpperCamelCase						=  resample if resample is not None else self.resample
    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(UpperCamelCase__       ,   param_name='crop_size' )
    UpperCamelCase						=  do_rescale if do_rescale is not None else self.do_rescale
    UpperCamelCase						=  rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCamelCase						=  do_normalize if do_normalize is not None else self.do_normalize
    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						=  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_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.' )

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

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

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

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

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

    UpperCamelCase						=  [to_channel_dimension_format(UpperCamelCase__       ,   UpperCamelCase__ ) for image in images]

    UpperCamelCase						=  {'pixel_values': images}
    return BatchFeature(data=UpperCamelCase__       ,   tensor_type=UpperCamelCase__ )



  def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      Optional[int]:
    """simple docstring"""
    UpperCamelCase						=  outputs.logits

    # Resize logits and compute semantic segmentation maps
    if target_sizes is not None:
      if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
        raise ValueError(
            'Make sure that you pass in as many target sizes as the batch dimension of the logits' )

      if is_torch_tensor(UpperCamelCase__ ):
        UpperCamelCase						=  target_sizes.numpy()

      UpperCamelCase						=  []

      for idx in range(len(UpperCamelCase__ ) ):
        UpperCamelCase						=  torch.nn.functional.interpolate(
            logits[idx].unsqueeze(dim=0 )       ,   size=target_sizes[idx]       ,   mode='bilinear'       ,   align_corners=UpperCamelCase__ )
        UpperCamelCase						=  resized_logits[0].argmax(dim=0 )
        semantic_segmentation.append(UpperCamelCase__ )
    else:
      UpperCamelCase						=  logits.argmax(dim=1 )
      UpperCamelCase						=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]

    return semantic_segmentation


import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor


class        lowercase      (	unittest.TestCase     ):



		def __init__(				self       ,   A_       ,   A_=7       ,   A_=3       ,   A_=18       ,   A_=30       ,   A_=400       ,   A_=True       ,   A_=None       ,   A_=True       ,   A_=False       ,   A_=True       ,   A_=True       ,   A_=[0.5, 0.5, 0.5]       ,   A_=[0.5, 0.5, 0.5]       ,   )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  image_size
				UpperCamelCase						=  min_resolution
				UpperCamelCase						=  max_resolution
				UpperCamelCase						=  do_resize
				UpperCamelCase						=  size if size is not None else {'height': 18, 'width': 20}
				UpperCamelCase						=  do_thumbnail
				UpperCamelCase						=  do_align_axis
				UpperCamelCase						=  do_pad
				UpperCamelCase						=  do_normalize
				UpperCamelCase						=  image_mean
				UpperCamelCase						=  image_std



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_thumbnail": self.do_thumbnail,
				    "do_align_long_axis": self.do_align_axis,
				    "do_pad": self.do_pad,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}






@require_torch
@require_vision
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : Optional[int]			  =       DonutImageProcessor if is_vision_available() else None



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  DonutImageProcessingTester(self )



		@property
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class(**self.image_processor_dict )
				self.assertTrue(hasattr(A_       ,   'do_resize' ) )
				self.assertTrue(hasattr(A_       ,   'size' ) )
				self.assertTrue(hasattr(A_       ,   'do_thumbnail' ) )
				self.assertTrue(hasattr(A_       ,   'do_align_long_axis' ) )
				self.assertTrue(hasattr(A_       ,   'do_pad' ) )
				self.assertTrue(hasattr(A_       ,   'do_normalize' ) )
				self.assertTrue(hasattr(A_       ,   'image_mean' ) )
				self.assertTrue(hasattr(A_       ,   'image_std' ) )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict )
				self.assertEqual(image_processor.size       ,   {'height': 18, 'width': 20} )

				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=42 )
				self.assertEqual(image_processor.size       ,   {'height': 42, 'width': 42} )

				# Previous config had dimensions in (width, height) order
				UpperCamelCase						=  self.image_processing_class.from_dict(self.image_processor_dict       ,   size=(42, 84) )
				self.assertEqual(image_processor.size       ,   {'height': 84, 'width': 42} )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		@is_flaky()
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				# 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=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   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.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  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['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )



		@is_flaky()
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				# 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=A_       ,   numpify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   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.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  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['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )



		@is_flaky()
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				# 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=A_       ,   torchify=A_ )
				for image in image_inputs:
						self.assertIsInstance(A_       ,   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.size['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )

				# Test batched
				UpperCamelCase						=  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['height'],
				        self.image_processor_tester.size['width'],
				    )       ,   )
