
"""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  lowerCAmelCase__							(		unittest.TestCase      ):







     def __init__(				self     :     Optional[int]       , _lowerCamelCase     :     List[str]       , _lowerCamelCase     :     int=7       , _lowerCamelCase     :     Optional[Any]=3       , _lowerCamelCase     :     Optional[Any]=18       , _lowerCamelCase     :     int=30       , _lowerCamelCase     :     Any=400       , _lowerCamelCase     :     Tuple=True       , _lowerCamelCase     :     Union[str, Any]=None       , _lowerCamelCase     :     int=True       , _lowerCamelCase     :     Union[str, Any]=None       , _lowerCamelCase     :     Union[str, Any]=True       , _lowerCamelCase     :     Union[str, Any]=[0.5, 0.5, 0.5]       , _lowerCamelCase     :     Dict=[0.5, 0.5, 0.5]       , _lowerCamelCase     :     Dict=False       , ):
       _snake_case   	=			size if size is not None else {'''height''': 20, '''width''': 20}
       _snake_case   	=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
       _snake_case   	=			parent
       _snake_case   	=			batch_size
       _snake_case   	=			num_channels
       _snake_case   	=			image_size
       _snake_case   	=			min_resolution
       _snake_case   	=			max_resolution
       _snake_case   	=			do_resize
       _snake_case   	=			size
       _snake_case   	=			do_center_crop
       _snake_case   	=			crop_size
       _snake_case   	=			do_normalize
       _snake_case   	=			image_mean
       _snake_case   	=			image_std
       _snake_case   	=			do_reduce_labels







     def 	lowercase      (				self     :     Optional[Any]     ):
       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 (					)	->   Union[str, Any]:
  _snake_case   	=			load_dataset('''hf-internal-testing/fixtures_ade20k'''					,   split='''test'''						)

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

  return image, map
def    _UpperCAmelCase (					)	->   Tuple:
  _snake_case   	=			load_dataset('''hf-internal-testing/fixtures_ade20k'''					,   split='''test'''						)

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

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

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







     def 	lowercase      (				self     :     List[Any]     ):
       _snake_case   	=			BeitImageProcessingTester(self     )







     @property
     def 	lowercase      (				self     :     str     ):
       return self.image_processor_tester.prepare_image_processor_dict()







     def 	lowercase      (				self     :     Optional[int]     ):
       _snake_case   	=			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 	lowercase      (				self     :     List[Any]     ):
       _snake_case   	=			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__     )

       _snake_case   	=			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 	lowercase      (				self     :     List[str]     ):
       pass







     def 	lowercase      (				self     :     Any     ):
       _snake_case   	=			self.image_processing_class(**self.image_processor_dict     )
       # create random PIL images
       _snake_case   	=			prepare_image_inputs(self.image_processor_tester       , equal_resolution=lowerCAmelCase__     )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase__       , Image.Image     )

       # Test not batched input
       _snake_case   	=			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
       _snake_case   	=			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 	lowercase      (				self     :     Tuple     ):
       _snake_case   	=			self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       _snake_case   	=			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
       _snake_case   	=			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
       _snake_case   	=			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 	lowercase      (				self     :     List[str]     ):
       _snake_case   	=			self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       _snake_case   	=			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
       _snake_case   	=			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
       _snake_case   	=			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 	lowercase      (				self     :     Union[str, Any]     ):
       _snake_case   	=			self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       _snake_case   	=			prepare_image_inputs(self.image_processor_tester       , equal_resolution=lowerCAmelCase__       , torchify=lowerCAmelCase__     )
       _snake_case   	=			[]
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase__       , torch.Tensor     )
         maps.append(torch.zeros(image.shape[-2:]     ).long()     )

       # Test not batched input
       _snake_case   	=			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
       _snake_case   	=			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)
       _snake_case	,   _snake_case   	=			prepare_semantic_single_inputs()

       _snake_case   	=			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)
       _snake_case	,   _snake_case   	=			prepare_semantic_batch_inputs()

       _snake_case   	=			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 	lowercase      (				self     :     Any     ):
       _snake_case   	=			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
       _snake_case	,   _snake_case   	=			prepare_semantic_single_inputs()
       _snake_case   	=			image_processing(lowerCAmelCase__       , lowerCAmelCase__       , return_tensors='''pt'''     )
       self.assertTrue(encoding['''labels'''].min().item() >= 0     )
       self.assertTrue(encoding['''labels'''].max().item() <= 150     )

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










'''simple docstring'''

from ..utils import DummyObject, requires_backends
class 							__lowerCAmelCase	(       metaclass=__magic_name__					):






       """simple docstring"""
       _snake_case      :	Dict									=       ['transformers', 'torch', 'note_seq']





       def __init__(				self  :						Tuple			,					*lowerCAmelCase__  :						List[str]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	int:

            '''simple docstring'''




            requires_backends(self			,					['''transformers''', '''torch''', '''note_seq''']	)





       @classmethod
       def snake_case__		(				cls  :						Dict			,					*lowerCAmelCase__  :						Optional[Any]			,					**lowerCAmelCase__  :						Optional[int]	)       ->	Any:

            '''simple docstring'''




            requires_backends(cls			,					['''transformers''', '''torch''', '''note_seq''']	)







       @classmethod
       def snake_case__		(				cls  :						str			,					*lowerCAmelCase__  :						Any			,					**lowerCAmelCase__  :						List[Any]	)       ->	Tuple:

            '''simple docstring'''




            requires_backends(cls			,					['''transformers''', '''torch''', '''note_seq''']	)



