

'''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 BeitImageProcessor
class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase=7 ,   lowerCamelCase=3 ,   lowerCamelCase=18 ,   lowerCamelCase=30 ,   lowerCamelCase=400 ,   lowerCamelCase=True ,   lowerCamelCase=None ,   lowerCamelCase=True ,   lowerCamelCase=None ,   lowerCamelCase=True ,   lowerCamelCase=[0.5, 0.5, 0.5] ,   lowerCamelCase=[0.5, 0.5, 0.5] ,   lowerCamelCase=False ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    size if size is not None else {'height': 20, 'width': 20}
         __a				     =    crop_size if crop_size is not None else {'height': 18, 'width': 18}
         __a				     =    parent
         __a				     =    batch_size
         __a				     =    num_channels
         __a				     =    image_size
         __a				     =    min_resolution
         __a				     =    max_resolution
         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_center_crop
         __a				     =    crop_size
         __a				     =    do_normalize
         __a				     =    image_mean
         __a				     =    image_std
         __a				     =    do_reduce_labels






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



         '''simple docstring'''
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_reduce_labels": self.do_reduce_labels,
         }







def    __UpperCAmelCase	(				)  ->			int:

   """simple docstring"""
   __a				     =    load_dataset('hf-internal-testing/fixtures_ade20k',					split='test'    )

   __a				     =    Image.open(dataset[0]['file']    )
   __a				     =    Image.open(dataset[1]['file']    )

   return image, map



def    __UpperCAmelCase	(				)  ->			int:

   """simple docstring"""
   __a				     =    load_dataset('hf-internal-testing/fixtures_ade20k',					split='test'    )

   __a				     =    Image.open(ds[0]['file']    )
   __a				     =    Image.open(ds[1]['file']    )
   __a				     =    Image.open(ds[2]['file']    )
   __a				     =    Image.open(ds[3]['file']    )

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



@require_torch
@require_vision
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=BeitImageProcessor if is_vision_available() else None




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



         '''simple docstring'''
         __a				     =    BeitImageProcessingTester(self      )




      @property
      def  __UpperCamelCase							(  self      )							->str:



         '''simple docstring'''
         return self.image_processor_tester.prepare_image_processor_dict()




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(lowerCamelCase ,   'do_resize'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'size'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'do_center_crop'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'center_crop'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'do_normalize'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'image_mean'      )      )
         self.assertTrue(hasattr(lowerCamelCase ,   'image_std'      )      )




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



         '''simple docstring'''
         __a				     =    self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size ,   {'height': 20, 'width': 20}      )
         self.assertEqual(image_processor.crop_size ,   {'height': 18, 'width': 18}      )
         self.assertEqual(image_processor.do_reduce_labels ,   lowerCamelCase      )

         __a				     =    self.image_processing_class.from_dict(
             self.image_processor_dict ,   size=42 ,   crop_size=84 ,   reduce_labels=lowerCamelCase      )
         self.assertEqual(image_processor.size ,   {'height': 42, 'width': 42}      )
         self.assertEqual(image_processor.crop_size ,   {'height': 84, 'width': 84}      )
         self.assertEqual(image_processor.do_reduce_labels ,   lowerCamelCase      )




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



         '''simple docstring'''
         pass




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



         '''simple docstring'''
         __a				     =    self.image_processing_class(**self.image_processor_dict      )
         # create random PIL images
         __a				     =    prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase      )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase ,   Image.Image      )

         # Test not batched input
         __a				     =    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
         __a				     =    image_processing(lowerCamelCase ,   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  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    self.image_processing_class(**self.image_processor_dict      )
         # create random numpy tensors
         __a				     =    prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase ,   numpify=lowerCamelCase      )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase ,   np.ndarray      )

         # Test not batched input
         __a				     =    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
         __a				     =    image_processing(lowerCamelCase ,   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  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         __a				     =    prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase ,   torchify=lowerCamelCase      )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase ,   torch.Tensor      )

         # Test not batched input
         __a				     =    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
         __a				     =    image_processing(lowerCamelCase ,   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  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    self.image_processing_class(**self.image_processor_dict      )
         # create random PyTorch tensors
         __a				     =    prepare_image_inputs(self.image_processor_tester ,   equal_resolution=lowerCamelCase ,   torchify=lowerCamelCase      )
         __a				     =    []
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase ,   torch.Tensor      )
            maps.append(torch.zeros(image.shape[-2:]      ).long()      )

         # Test not batched input
         __a				     =    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.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             ) ,   )
         self.assertEqual(
             encoding['labels'].shape ,   (
                 1,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_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
         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   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.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             ) ,   )
         self.assertEqual(
             encoding['labels'].shape ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_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)
         __a     ,			__a				     =    prepare_semantic_single_inputs()

         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   return_tensors='pt'      )
         self.assertEqual(
             encoding['pixel_values'].shape ,   (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             ) ,   )
         self.assertEqual(
             encoding['labels'].shape ,   (
                 1,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_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)
         __a     ,			__a				     =    prepare_semantic_batch_inputs()

         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   return_tensors='pt'      )
         self.assertEqual(
             encoding['pixel_values'].shape ,   (
                 2,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             ) ,   )
         self.assertEqual(
             encoding['labels'].shape ,   (
                 2,
                 self.image_processor_tester.crop_size['height'],
                 self.image_processor_tester.crop_size['width'],
             ) ,   )
         self.assertEqual(encoding['labels'].dtype ,   torch.long      )
         self.assertTrue(encoding['labels'].min().item() >= 0      )
         self.assertTrue(encoding['labels'].max().item() <= 255      )






      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         __a				     =    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
         __a     ,			__a				     =    prepare_semantic_single_inputs()
         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   return_tensors='pt'      )
         self.assertTrue(encoding['labels'].min().item() >= 0      )
         self.assertTrue(encoding['labels'].max().item() <= 150      )

         __a				     =    True
         __a				     =    image_processing(lowerCamelCase ,   lowerCamelCase ,   return_tensors='pt'      )
         self.assertTrue(encoding['labels'].min().item() >= 0      )
         self.assertTrue(encoding['labels'].max().item() <= 255      )







'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	str    )  ->			List[List[ImageInput]]:

   """simple docstring"""
   if isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and isinstance(videos[0],					(list, tuple)    ) and is_valid_image(videos[0][0]    ):
      return videos

   elif isinstance(SCREAMING_SNAKE_CASE__,					(list, tuple)    ) and is_valid_image(videos[0]    ):
      return [videos]

   elif is_valid_image(SCREAMING_SNAKE_CASE__    ):
      return [[videos]]

   raise ValueError(f"""Could not make batched video from {videos}"""    )



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=["pixel_values"]




      def __init__(  self ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = True ,   lowerCamelCase = 1 / 255 ,   lowerCamelCase = True ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->None:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )
         __a				     =    size if size is not None else {'shortest_edge': 256}
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

         __a				     =    do_resize
         __a				     =    size
         __a				     =    do_center_crop
         __a				     =    crop_size
         __a				     =    resample
         __a				     =    do_rescale
         __a				     =    rescale_factor
         __a				     =    offset
         __a				     =    do_normalize
         __a				     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __a				     =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = PILImageResampling.BILINEAR ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         if "shortest_edge" in size:
            __a				     =    get_resize_output_image_size(lowerCamelCase ,   size['shortest_edge'] ,   default_to_square=lowerCamelCase      )
         elif "height" in size and "width" in size:
            __a				     =    (size['height'], size['width'])
         else:
            raise ValueError(F"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
         return resize(lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         __a				     =    get_size_dict(lowerCamelCase      )
         if "height" not in size or "width" not in size:
            raise ValueError(F"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""      )
         return center_crop(lowerCamelCase ,   size=(size['height'], size['width']) ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = True ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    image.astype(np.floataa      )
         if offset:
            __a				     =    image - (scale / 2)
         return rescale(lowerCamelCase ,   scale=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase = None ,   **lowerCamelCase ,   )							->np.ndarray:



         '''simple docstring'''
         return normalize(lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase ,   data_format=lowerCamelCase ,   **lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   )							->np.ndarray:



         '''simple docstring'''
         if do_resize and size is None or resample is None:
            raise ValueError('Size and resample 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.'      )

         if offset and not do_rescale:
            raise ValueError('For offset, do_rescale must also be set to True.'      )

         # All transformations expect numpy arrays.
         __a				     =    to_numpy_array(lowerCamelCase      )

         if do_resize:
            __a				     =    self.resize(image=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase      )

         if do_center_crop:
            __a				     =    self.center_crop(lowerCamelCase ,   size=lowerCamelCase      )

         if do_rescale:
            __a				     =    self.rescale(image=lowerCamelCase ,   scale=lowerCamelCase ,   offset=lowerCamelCase      )

         if do_normalize:
            __a				     =    self.normalize(image=lowerCamelCase ,   mean=lowerCamelCase ,   std=lowerCamelCase      )

         __a				     =    to_channel_dimension_format(lowerCamelCase ,   lowerCamelCase      )
         return image






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = ChannelDimension.FIRST ,   **lowerCamelCase ,   )							->PIL.Image.Image:



         '''simple docstring'''
         __a				     =    do_resize if do_resize is not None else self.do_resize
         __a				     =    resample if resample is not None else self.resample
         __a				     =    do_center_crop if do_center_crop is not None else self.do_center_crop
         __a				     =    do_rescale if do_rescale is not None else self.do_rescale
         __a				     =    rescale_factor if rescale_factor is not None else self.rescale_factor
         __a				     =    offset if offset is not None else self.offset
         __a				     =    do_normalize if do_normalize is not None else self.do_normalize
         __a				     =    image_mean if image_mean is not None else self.image_mean
         __a				     =    image_std if image_std is not None else self.image_std

         __a				     =    size if size is not None else self.size
         __a				     =    get_size_dict(lowerCamelCase ,   default_to_square=lowerCamelCase      )
         __a				     =    crop_size if crop_size is not None else self.crop_size
         __a				     =    get_size_dict(lowerCamelCase ,   param_name='crop_size'      )

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

         __a				     =    make_batched(lowerCamelCase      )

         __a				     =    [
             [
                 self._preprocess_image(
                     image=lowerCamelCase ,   do_resize=lowerCamelCase ,   size=lowerCamelCase ,   resample=lowerCamelCase ,   do_center_crop=lowerCamelCase ,   crop_size=lowerCamelCase ,   do_rescale=lowerCamelCase ,   rescale_factor=lowerCamelCase ,   offset=lowerCamelCase ,   do_normalize=lowerCamelCase ,   image_mean=lowerCamelCase ,   image_std=lowerCamelCase ,   data_format=lowerCamelCase ,   )
                 for img in video
             ]
             for video in videos
         ]

         __a				     =    {'pixel_values': videos}
         return BatchFeature(data=lowerCamelCase ,   tensor_type=lowerCamelCase      )