

'''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, 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


_snake_case     						=						logging.get_logger(__name__)


class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  int			 =							['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    ,       ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase						)
      _lowercase    :   List[Any]      =     crop_size if crop_size is not None else {"height": 256, "width": 256}
      _lowercase    :   List[Any]      =     get_size_dict(_UpperCamelCase    ,       param_name="crop_size"						)
      _lowercase    :   List[Any]      =     size if size is not None else {"height": 224, "width": 224}
      _lowercase    :   Union[str, Any]      =     get_size_dict(_UpperCamelCase						)

      _lowercase    :   Dict      =     do_center_crop
      _lowercase    :   Optional[int]      =     crop_size
      _lowercase    :   Optional[int]      =     do_resize
      _lowercase    :   Dict      =     size
      _lowercase    :   List[str]      =     resample
      _lowercase    :   Optional[int]      =     do_rescale
      _lowercase    :   List[str]      =     rescale_factor
      _lowercase    :   List[Any]      =     do_normalize
      _lowercase    :   Dict      =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      _lowercase    :   Optional[Any]      =     image_std if image_std is not None else IMAGENET_DEFAULT_STD


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase = None    ,       _UpperCamelCase = None    ,       **_UpperCamelCase    ,       ):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     self.size if size is None else size
      _lowercase    :   Any      =     get_size_dict(_UpperCamelCase						)
      _lowercase    :   Optional[int]      =     get_size_dict(_UpperCamelCase    ,       param_name="crop_size"						)

      _lowercase  ,   _lowercase    :   Optional[Any]      =     get_image_size(_UpperCamelCase						)
      _lowercase    :   List[str]      =     min(_UpperCamelCase    ,       _UpperCamelCase						)
      _lowercase    :   Optional[int]      =     (size["height"] / crop_size["height"]) * min_dim
      _lowercase    :   str      =     (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    ,       ):





      """simple docstring"""





      _lowercase    :   Optional[int]      =     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    ,       ):





      """simple docstring"""





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


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase = None    ,       **_UpperCamelCase    ,       ):





      """simple docstring"""





      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    ,       ):





      """simple docstring"""





      _lowercase    :   int      =     do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowercase    :   Optional[Any]      =     crop_size if crop_size is not None else self.crop_size
      _lowercase    :   Tuple      =     get_size_dict(_UpperCamelCase    ,       param_name="crop_size"						)
      _lowercase    :   Union[str, Any]      =     do_resize if do_resize is not None else self.do_resize
      _lowercase    :   Optional[int]      =     size if size is not None else self.size
      _lowercase    :   List[Any]      =     get_size_dict(_UpperCamelCase						)
      _lowercase    :   int      =     resample if resample is not None else self.resample
      _lowercase    :   Optional[Any]      =     do_rescale if do_rescale is not None else self.do_rescale
      _lowercase    :   Union[str, Any]      =     rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowercase    :   Any      =     do_normalize if do_normalize is not None else self.do_normalize
      _lowercase    :   Any      =     image_mean if image_mean is not None else self.image_mean
      _lowercase    :   str      =     image_std if image_std is not None else self.image_std

      _lowercase    :   Tuple      =     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.
      _lowercase    :   List[str]      =     [to_numpy_array(_UpperCamelCase						) for image in images]

      if do_center_crop:
         _lowercase    :   int      =     [self.center_crop(_UpperCamelCase    ,       _UpperCamelCase    ,       size=_UpperCamelCase						) for image in images]

      if do_resize:
         _lowercase    :   Optional[Any]      =     [self.resize(image=_UpperCamelCase    ,       size=_UpperCamelCase    ,       resample=_UpperCamelCase						) for image in images]

      if do_rescale:
         _lowercase    :   Optional[Any]      =     [self.rescale(image=_UpperCamelCase    ,       scale=_UpperCamelCase						) for image in images]

      if do_normalize:
         _lowercase    :   Any      =     [self.normalize(image=_UpperCamelCase    ,       mean=_UpperCamelCase    ,       std=_UpperCamelCase						) for image in images]

      _lowercase    :   List[Any]      =     [to_channel_dimension_format(_UpperCamelCase    ,       _UpperCamelCase						) for image in images]

      _lowercase    :   Any      =     {"pixel_values": images}
      return BatchFeature(data=_UpperCamelCase    ,       tensor_type=_UpperCamelCase						)









'''simple docstring'''
import unittest

import numpy as np
from datasets import load_dataset

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 SegformerImageProcessor


class      a__						(							unittest.TestCase						):


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=7    ,       _UpperCamelCase=3    ,       _UpperCamelCase=30    ,       _UpperCamelCase=400    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None    ,       _UpperCamelCase=True    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=False    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     size if size is not None else {"height": 30, "width": 30}
      _lowercase    :   str      =     parent
      _lowercase    :   Optional[int]      =     batch_size
      _lowercase    :   Optional[int]      =     num_channels
      _lowercase    :   Any      =     min_resolution
      _lowercase    :   Any      =     max_resolution
      _lowercase    :   Tuple      =     do_resize
      _lowercase    :   List[Any]      =     size
      _lowercase    :   Tuple      =     do_normalize
      _lowercase    :   Optional[Any]      =     image_mean
      _lowercase    :   Any      =     image_std
      _lowercase    :   Dict      =     do_reduce_labels






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }







def       _A	(			)    ->			List[Any]:
   _lowercase    :   List[str]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

   _lowercase    :   Union[str, Any]      =     Image.open(dataset[0]["file"]  )
   _lowercase    :   Optional[int]      =     Image.open(dataset[1]["file"]  )

   return image, map







def       _A	(			)    ->			Optional[int]:
   _lowercase    :   List[Any]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

   _lowercase    :   int      =     Image.open(dataset[0]["file"]  )
   _lowercase    :   str      =     Image.open(dataset[1]["file"]  )
   _lowercase    :   Union[str, Any]      =     Image.open(dataset[2]["file"]  )
   _lowercase    :   Union[str, Any]      =     Image.open(dataset[3]["file"]  )

   return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class      a__						(							lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							SegformerImageProcessor if is_vision_available() else None


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     SegformerImageProcessingTester(self						)


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_resize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "size"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_normalize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_mean"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_std"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_reduce_labels"						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Dict      =     self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size    ,       {"height": 30, "width": 30}						)
      self.assertEqual(image_processor.do_reduce_labels    ,       _UpperCamelCase						)

      _lowercase    :   Tuple      =     self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42    ,       reduce_labels=_UpperCamelCase						)
      self.assertEqual(image_processor.size    ,       {"height": 42, "width": 42}						)
      self.assertEqual(image_processor.do_reduce_labels    ,       _UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      _lowercase    :   str      =     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    :   Optional[int]      =     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
      _lowercase    :   int      =     image_processing(_UpperCamelCase    ,       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"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      _lowercase    :   List[str]      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       numpify=_UpperCamelCase						)
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       np.ndarray						)

      # Test not batched input
      _lowercase    :   Dict      =     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
      _lowercase    :   List[Any]      =     image_processing(_UpperCamelCase    ,       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"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   List[Any]      =     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    :   Optional[int]      =     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
      _lowercase    :   Dict      =     image_processing(_UpperCamelCase    ,       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"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   Tuple      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       torchify=_UpperCamelCase						)
      _lowercase    :   Tuple      =     []
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       torch.Tensor						)
         maps.append(torch.zeros(image.shape[-2:]						).long()						)

      # Test not batched input
      _lowercase    :   List[str]      =     image_processing(image_inputs[0]    ,       maps[0]    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].shape    ,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(encoding["labels"].dtype    ,       torch.long						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)

      # Test batched
      _lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].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"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(encoding["labels"].dtype    ,       torch.long						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)

      # Test not batched input (PIL images)
      _lowercase  ,   _lowercase    :   Optional[int]      =     prepare_semantic_single_inputs()

      _lowercase    :   int      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].shape    ,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(encoding["labels"].dtype    ,       torch.long						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)

      # Test batched input (PIL images)
      _lowercase  ,   _lowercase    :   Dict      =     prepare_semantic_batch_inputs()

      _lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].shape    ,       (
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              2,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(encoding["labels"].dtype    ,       torch.long						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   str      =     self.image_processing_class(**self.image_processor_dict						)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      _lowercase  ,   _lowercase    :   str      =     prepare_semantic_single_inputs()
      _lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 150						)

      _lowercase    :   str      =     True
      _lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)






