





'''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					):






       """simple docstring"""





       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			,					)       ->	Tuple:

            '''simple docstring'''




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







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

            '''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    a__							(							)						->						Union[str, Any]:





     """simple docstring"""

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

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

     return image, map







def    a__							(							)						->						Tuple:





     """simple docstring"""

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

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

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




@require_torch
@require_vision
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	str									=       BeitImageProcessor if is_vision_available() else None





       def snake_case__		(				self  :						List[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			BeitImageProcessingTester(self	)





       @property
       def snake_case__		(				self  :						str	)       ->	Dict:

            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()





       def snake_case__		(				self  :						Optional[int]	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			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 snake_case__		(				self  :						List[Any]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			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__	)

            _UpperCamelCase							       =			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 snake_case__		(				self  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						Any	)       ->	Any:

            '''simple docstring'''




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

            # Test not batched input
            _UpperCamelCase							       =			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
            _UpperCamelCase							       =			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 snake_case__		(				self  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            _UpperCamelCase							       =			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
            _UpperCamelCase							       =			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
            _UpperCamelCase							       =			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 snake_case__		(				self  :						List[str]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            _UpperCamelCase							       =			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
            _UpperCamelCase							       =			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
            _UpperCamelCase							       =			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 snake_case__		(				self  :						Union[str, Any]	)       ->	str:

            '''simple docstring'''




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

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

            _UpperCamelCase							       =			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)
            _UpperCamelCase       ,							_UpperCamelCase							       =			prepare_semantic_batch_inputs()

            _UpperCamelCase							       =			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 snake_case__		(				self  :						Any	)       ->	List[str]:

            '''simple docstring'''




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

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










'''simple docstring'''

import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       def snake_case__		(				self  :						str	)       ->	Optional[int]:

            '''simple docstring'''




            debug_launcher(test_script.main	)







       def snake_case__		(				self  :						Any	)       ->	Optional[Any]:

            '''simple docstring'''




            debug_launcher(test_ops.main	)



