


import unittest

import numpy as np
import requests

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

			from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
			lowerCAmelCase       =							False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor


class        _a							(			unittest.TestCase			):

							def __init__(							self:	Dict    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	List[str]=7    ,						UpperCamelCase_:	str=3    ,						UpperCamelCase_:	List[str]=18    ,						UpperCamelCase_:	Any=30    ,						UpperCamelCase_:	Tuple=400    ,						UpperCamelCase_:	Optional[int]=None    ,						UpperCamelCase_:	Any=True    ,						UpperCamelCase_:	Optional[int]=True    ,						UpperCamelCase_:	Optional[int]=None    ,						)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      size if size is not None else {"height": 20, "width": 20}
									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      num_channels
									lowercase__            =      image_size
									lowercase__            =      min_resolution
									lowercase__            =      max_resolution
									lowercase__            =      size
									lowercase__            =      do_normalize
									lowercase__            =      do_convert_rgb
									lowercase__            =      [512, 1_024, 2_048, 4_096]
									lowercase__            =      patch_size if patch_size is not None else {"height": 16, "width": 16}

							def       lowerCamelCase_ (							self:	List[str]							)			->   Optional[int]:





									"""simple docstring"""







									return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


							def       lowerCamelCase_ (							self:	Any							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
									lowercase__            =      Image.open(requests.get(UpperCamelCase_    ,						stream=UpperCamelCase_							).raw							).convert('''RGB'''							)
									return raw_image


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,	reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''     ,	)
@require_torch
@require_vision
class        _a							(			A__     ,	unittest.TestCase			):
							_lowercase	:			int        = PixaStructImageProcessor if is_vision_available() else None

							def       lowerCamelCase_ (							self:	List[Any]							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      PixaStructImageProcessingTester(self							)

							@property
							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def       lowerCamelCase_ (							self:	str							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_normalize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_convert_rgb'''							)							)

							def       lowerCamelCase_ (							self:	Dict							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.image_processor_tester.prepare_dummy_image()

									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									lowercase__            =      2_048

									lowercase__            =      image_processor(UpperCamelCase_    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							)
									self.assertTrue(torch.allclose(inputs.flattened_patches.mean()    ,						torch.tensor(0.0606							)    ,						atol=1E-3    ,						rtol=1E-3							)							)

							def       lowerCamelCase_ (							self:	Dict							)			->   Any:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PIL images
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						Image.Image							)

									# Test not batched input
									lowercase__            =      (
									    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											lowercase__            =      image_processor(
											    image_inputs[0]    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(1, max_patch, expected_hidden_dim)    ,						)

											# Test batched
											lowercase__            =      image_processor(
											    UpperCamelCase_    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,						)

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PIL images
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						Image.Image							)

									# Test not batched input
									lowercase__            =      (
									    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
									    * self.image_processor_tester.num_channels
									) + 2

									lowercase__            =      True

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											with self.assertRaises(UpperCamelCase_							):
													lowercase__            =      image_processor(
													    image_inputs[0]    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches

											lowercase__            =      "Hello"

											lowercase__            =      image_processor(
											    image_inputs[0]    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_    ,						header_text=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(1, max_patch, expected_hidden_dim)    ,						)

											# Test batched
											lowercase__            =      image_processor(
											    UpperCamelCase_    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_    ,						header_text=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,						)

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random numpy tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						numpify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						np.ndarray							)

									lowercase__            =      (
									    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											lowercase__            =      image_processor(
											    image_inputs[0]    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(1, max_patch, expected_hidden_dim)    ,						)

											# Test batched
											lowercase__            =      image_processor(
											    UpperCamelCase_    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,						)


							def       lowerCamelCase_ (							self:	int							)			->   Union[str, Any]:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PyTorch tensors
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_    ,						torchify=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						torch.Tensor							)

									# Test not batched input
									lowercase__            =      (
									    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											lowercase__            =      image_processor(
											    image_inputs[0]    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(1, max_patch, expected_hidden_dim)    ,						)

											# Test batched
											lowercase__            =      image_processor(
											    UpperCamelCase_    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,						)


@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,	reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''     ,	)
@require_torch
@require_vision
class        _a							(			A__     ,	unittest.TestCase			):
							_lowercase	:			Tuple        = PixaStructImageProcessor if is_vision_available() else None

							def       lowerCamelCase_ (							self:	Optional[int]							)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      PixaStructImageProcessingTester(self    ,						num_channels=4							)
									lowercase__            =      3

							@property
							def       lowerCamelCase_ (							self:	Any							)			->   Dict:





									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def       lowerCamelCase_ (							self:	List[str]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_normalize'''							)							)
									self.assertTrue(hasattr(UpperCamelCase_    ,						'''do_convert_rgb'''							)							)


							def       lowerCamelCase_ (							self:	List[Any]							)			->   int:





									"""simple docstring"""







									lowercase__            =      self.image_processing_class(**self.image_processor_dict							)
									# create random PIL images
									lowercase__            =      prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase_							)
									for image in image_inputs:
											self.assertIsInstance(UpperCamelCase_    ,						Image.Image							)

									# Test not batched input
									lowercase__            =      (
									    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
									    * (self.image_processor_tester.num_channels - 1)
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											lowercase__            =      image_processor(
											    image_inputs[0]    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(1, max_patch, expected_hidden_dim)    ,						)

											# Test batched
											lowercase__            =      image_processor(
											    UpperCamelCase_    ,						return_tensors='''pt'''    ,						max_patches=UpperCamelCase_							).flattened_patches
											self.assertEqual(
											    encoded_images.shape    ,						(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,						)










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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


lowerCamelCase	:							Any				=logging.get_logger(__name__)





class 			__a  (    A__       ):
 _lowerCAmelCase       :       List[str]        = ['''pixel_values''']







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PILImageResampling.BILINEAR							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Union[int, float] = 1 / 2_55							,   SCREAMING_SNAKE_CASE					:   bool = True							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = True							,   **SCREAMING_SNAKE_CASE					:   Tuple							,   ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  size if size is not None else {"shortest_edge": 2_24}
      UpperCamelCase__			:   Any       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
      UpperCamelCase__			:   int       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)

      UpperCamelCase__			:   Dict       				=  do_resize
      UpperCamelCase__			:   List[str]       				=  size
      UpperCamelCase__			:   int       				=  resample
      UpperCamelCase__			:   Optional[int]       				=  do_rescale
      UpperCamelCase__			:   List[Any]       				=  rescale_factor
      UpperCamelCase__			:   Union[str, Any]       				=  do_center_crop
      UpperCamelCase__			:   int       				=  crop_size
      UpperCamelCase__			:   Optional[int]       				=  do_flip_channel_order







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Dict[str, int]							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = PIL.Image.BILINEAR							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None							,   **SCREAMING_SNAKE_CASE					:   Optional[Any]							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      if "shortest_edge" not in size:
           raise ValueError(F'The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'						)
      UpperCamelCase__			:   Any       				=  get_resize_output_image_size(SCREAMING_SNAKE_CASE							,   size=size["shortest_edge"]							,   default_to_square=SCREAMING_SNAKE_CASE						)
      return resize(SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE							,   resample=SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE						)
      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 center_crop(SCREAMING_SNAKE_CASE							,   size=(size["height"], size["width"])							,   data_format=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







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


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   np.ndarray							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, ChannelDimension]] = None						):


      '''simple docstring'''






      return flip_channel_order(SCREAMING_SNAKE_CASE							,   data_format=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   ImageInput							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   PILImageResampling = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   float = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Dict[str, int] = None							,   SCREAMING_SNAKE_CASE					:   bool = None							,   SCREAMING_SNAKE_CASE					:   Optional[Union[str, TensorType]] = None							,   SCREAMING_SNAKE_CASE					:   ChannelDimension = ChannelDimension.FIRST							,   **SCREAMING_SNAKE_CASE					:   Dict							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  do_resize if do_resize is not None else self.do_resize
      UpperCamelCase__			:   List[str]       				=  resample if resample is not None else self.resample
      UpperCamelCase__			:   Any       				=  do_rescale if do_rescale is not None else self.do_rescale
      UpperCamelCase__			:   Tuple       				=  rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCamelCase__			:   Dict       				=  do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__			:   Any       				=  (
          do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
      )

      UpperCamelCase__			:   Optional[int]       				=  size if size is not None else self.size
      UpperCamelCase__			:   List[str]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   default_to_square=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__			:   Union[str, Any]       				=  get_size_dict(SCREAMING_SNAKE_CASE							,   param_name="crop_size"						)

      UpperCamelCase__			:   int       				=  make_list_of_images(SCREAMING_SNAKE_CASE						)

      if not valid_images(SCREAMING_SNAKE_CASE						):
           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_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."						)

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."						)

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

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

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

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

      # the pretrained checkpoints assume images are BGR, not RGB
      if do_flip_channel_order:
           UpperCamelCase__			:   Any       				=  [self.flip_channel_order(image=SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Optional[int]       				=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) for image in images]

      UpperCamelCase__			:   Optional[int]       				=  {"pixel_values": images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE							,   tensor_type=SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   List[Tuple] = None						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  outputs.logits

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

           if is_torch_tensor(SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Optional[Any]       				=  target_sizes.numpy()

           UpperCamelCase__			:   Any       				=  []

           for idx in range(len(SCREAMING_SNAKE_CASE						)						):
                UpperCamelCase__			:   Optional[Any]       				=  torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0						)							,   size=target_sizes[idx]							,   mode="bilinear"							,   align_corners=SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   List[str]       				=  resized_logits[0].argmax(dim=0						)
                semantic_segmentation.append(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__			:   List[str]       				=  logits.argmax(dim=1						)
           UpperCamelCase__			:   Dict       				=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

      return semantic_segmentation