

'''simple docstring'''





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 LevitImageProcessor





class A__    ( unittest.TestCase ):



 """simple docstring"""







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






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    size if size is not None else {"shortest_edge": 1_8}
      _lowerCAmelCase   :					List[Any]        =    crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
      _lowerCAmelCase   :					Dict        =    parent
      _lowerCAmelCase   :					Tuple        =    batch_size
      _lowerCAmelCase   :					int        =    num_channels
      _lowerCAmelCase   :					Tuple        =    image_size
      _lowerCAmelCase   :					Any        =    min_resolution
      _lowerCAmelCase   :					Dict        =    max_resolution
      _lowerCAmelCase   :					str        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					int        =    do_center_crop
      _lowerCAmelCase   :					Tuple        =    crop_size
      _lowerCAmelCase   :					List[str]        =    do_normalize
      _lowerCAmelCase   :					Any        =    image_mean
      _lowerCAmelCase   :					Tuple        =    image_std


 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




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





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



 """simple docstring"""







 _lowercase     :					int      =						LevitImageProcessor if is_vision_available() else None

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






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    LevitImageProcessingTester(self       )

 @property
 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.image_processing_class.from_dict(self.image_processor_dict       )
      self.assertEqual(image_processor.size     ,						{"shortest_edge": 1_8}       )
      self.assertEqual(image_processor.crop_size     ,						{"height": 1_8, "width": 1_8}       )

      _lowerCAmelCase   :					str        =    self.image_processing_class.from_dict(self.image_processor_dict     ,						size=4_2     ,						crop_size=8_4       )
      self.assertEqual(image_processor.size     ,						{"shortest_edge": 4_2}       )
      self.assertEqual(image_processor.crop_size     ,						{"height": 8_4, "width": 8_4}       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      pass

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      _lowerCAmelCase   :					Dict        =    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   :					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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )     ,						)

      # Test batched
      _lowerCAmelCase   :					Tuple        =    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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )     ,						)

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    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   :					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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )     ,						)

      # Test batched
      _lowerCAmelCase   :					List[str]        =    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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )     ,						)


 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      _lowerCAmelCase   :					Tuple        =    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   :					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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )     ,						)

      # Test batched
      _lowerCAmelCase   :					Optional[int]        =    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.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )     ,						)









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    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					int      =						['''pixel_values''']

 def __init__(		self :	Union[str, Any]     ,						A_ :	bool = True     ,						A_ :	Optional[Dict[str, int]] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						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_ :	Optional[Any]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    size if size is not None else {"shortest_edge": 2_5_6}
      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      _lowerCAmelCase   :					Dict        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Tuple        =    get_size_dict(A_     ,						param_name="crop_size"       )
      _lowerCAmelCase   :					Dict        =    do_resize
      _lowerCAmelCase   :					Optional[Any]        =    size
      _lowerCAmelCase   :					Union[str, Any]        =    resample
      _lowerCAmelCase   :					List[Any]        =    do_center_crop
      _lowerCAmelCase   :					str        =    crop_size
      _lowerCAmelCase   :					int        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					str        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					str        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

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






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_     ,						default_to_square=A_       )
      if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _lowerCAmelCase   :					Optional[Any]        =    get_resize_output_image_size(A_     ,						size=size["shortest_edge"]     ,						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   :					Any        =    get_size_dict(A_       )
      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(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

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






      '''simple docstring'''




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

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






      '''simple docstring'''




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

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					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   :					List[Any]        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

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

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

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

      _lowerCAmelCase   :					Tuple        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )


 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Tuple     ,						A_ :	List[Tuple] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    outputs.logits

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

           if is_torch_tensor(A_       ):
                _lowerCAmelCase   :					List[str]        =    target_sizes.numpy()

           _lowerCAmelCase   :					Tuple        =    []

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

      return semantic_segmentation






