

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 			__lowerCAmelCase					( unittest.TestCase							):




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=7							,       lowerCAmelCase=3							,       lowerCAmelCase=30							,       lowerCAmelCase=400							,       lowerCAmelCase=True							,       lowerCAmelCase=None							,       lowerCAmelCase=True							,       lowerCAmelCase=[0.5, 0.5, 0.5]							,       lowerCAmelCase=[0.5, 0.5, 0.5]							,       lowerCAmelCase=False							,       )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=size if size is not None else {'height': 30, 'width': 30}
        _lowercase     	=parent
        _lowercase     	=batch_size
        _lowercase     	=num_channels
        _lowercase     	=min_resolution
        _lowercase     	=max_resolution
        _lowercase     	=do_resize
        _lowercase     	=size
        _lowercase     	=do_normalize
        _lowercase     	=image_mean
        _lowercase     	=image_std
        _lowercase     	=do_reduce_labels




  def   A__					(    self )  ->			str:
        '''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				(      )   ->				int:





      """simple docstring"""



      _lowercase     	=load_dataset('hf-internal-testing/fixtures_ade20k'				,       split='test'					)

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

      return image, map
def 					a				(      )   ->				List[str]:





      """simple docstring"""



      _lowercase     	=load_dataset('hf-internal-testing/fixtures_ade20k'				,       split='test'					)

      _lowercase     	=Image.open(dataset[0]['file']					)
      _lowercase     	=Image.open(dataset[1]['file']					)
      _lowercase     	=Image.open(dataset[2]['file']					)
      _lowercase     	=Image.open(dataset[3]['file']					)

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







@require_torch
@require_vision
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  SegformerImageProcessor if is_vision_available() else None




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=SegformerImageProcessingTester(self )




  @property
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=self.image_processing_class(**self.image_processor_dict )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_resize' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'size' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_normalize' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'image_mean' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'image_std' ) )
        self.assertTrue(hasattr(lowerCAmelCase							,       'do_reduce_labels' ) )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=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							,       lowerCAmelCase )

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




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        pass




  def   A__					(    self )  ->			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=lowerCAmelCase )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,       Image.Image )

        # Test not batched input
        _lowercase     	=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     	=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.size['height'],
                self.image_processor_tester.size['width'],
            )							,       )




  def   A__					(    self )  ->			Optional[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=lowerCAmelCase							,       numpify=lowerCAmelCase )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,       np.ndarray )

        # Test not batched input
        _lowercase     	=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     	=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.size['height'],
                self.image_processor_tester.size['width'],
            )							,       )




  def   A__					(    self )  ->			Optional[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=lowerCAmelCase							,       torchify=lowerCAmelCase )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,       torch.Tensor )

        # Test not batched input
        _lowercase     	=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     	=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.size['height'],
                self.image_processor_tester.size['width'],
            )							,       )




  def   A__					(    self )  ->			str:
        '''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=lowerCAmelCase							,       torchify=lowerCAmelCase )
        _lowercase     	=[]
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,       torch.Tensor )
              maps.append(torch.zeros(image.shape[-2:] ).long() )

        # Test not batched input
        _lowercase     	=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     	=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.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     	=prepare_semantic_single_inputs()

        _lowercase     	=image_processing(lowerCAmelCase							,       lowerCAmelCase							,       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     	=prepare_semantic_batch_inputs()

        _lowercase     	=image_processing(lowerCAmelCase							,       lowerCAmelCase							,       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   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=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     	=prepare_semantic_single_inputs()
        _lowercase     	=image_processing(lowerCAmelCase							,       lowerCAmelCase							,       return_tensors='pt' )
        self.assertTrue(encoding['labels'].min().item() >= 0 )
        self.assertTrue(encoding['labels'].max().item() <= 150 )

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



import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


lowercase_     =       logging.get_logger(__name__)





class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE							):




  def __init__(    self							,       *lowerCAmelCase							,       **lowerCAmelCase )  ->			None:
        '''simple docstring'''






        warnings.warn(
            'The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
            ' use ViTImageProcessor instead.'							,       lowerCAmelCase							,       )
        super().__init__(*lowerCAmelCase							,       **lowerCAmelCase )


