

'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor







class 							A__    (       unittest.TestCase  ):





  def __init__(		self      :	Any							,					_a      :	List[Any]							,					_a      :	Optional[int]=7							,					_a      :	Any=3							,					_a      :	Optional[int]=18							,					_a      :	Dict=30							,					_a      :	int=400							,					_a      :	Any=True							,					_a      :	List[str]=None							,					_a      :	str=True							,					_a      :	str=False							,					_a      :	Optional[int]=True							,					_a      :	List[str]=True							,					_a      :	int=[0.5, 0.5, 0.5]							,					_a      :	Tuple=[0.5, 0.5, 0.5]							,					)	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =parent
       _SCREAMING_SNAKE_CASE							    =batch_size
       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =min_resolution
       _SCREAMING_SNAKE_CASE							    =max_resolution
       _SCREAMING_SNAKE_CASE							    =do_resize
       _SCREAMING_SNAKE_CASE							    =size if size is not None else {'height': 18, 'width': 20}
       _SCREAMING_SNAKE_CASE							    =do_thumbnail
       _SCREAMING_SNAKE_CASE							    =do_align_axis
       _SCREAMING_SNAKE_CASE							    =do_pad
       _SCREAMING_SNAKE_CASE							    =do_normalize
       _SCREAMING_SNAKE_CASE							    =image_mean
       _SCREAMING_SNAKE_CASE							    =image_std





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





       '''simple docstring'''






       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				DonutImageProcessor if is_vision_available() else None





  def 						A  (		self      :	List[Any] )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =DonutImageProcessingTester(self )





  @property
  def 						A  (		self      :	Tuple )	->    Union[str, Any]:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self      :	Tuple )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_a							,					'do_resize' ) )
       self.assertTrue(hasattr(_a							,					'size' ) )
       self.assertTrue(hasattr(_a							,					'do_thumbnail' ) )
       self.assertTrue(hasattr(_a							,					'do_align_long_axis' ) )
       self.assertTrue(hasattr(_a							,					'do_pad' ) )
       self.assertTrue(hasattr(_a							,					'do_normalize' ) )
       self.assertTrue(hasattr(_a							,					'image_mean' ) )
       self.assertTrue(hasattr(_a							,					'image_std' ) )





  def 						A  (		self      :	List[str] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size							,					{'height': 18, 'width': 20} )

       _SCREAMING_SNAKE_CASE							    =self.image_processing_class.from_dict(self.image_processor_dict							,					size=42 )
       self.assertEqual(image_processor.size							,					{'height': 42, 'width': 42} )

       # Previous config had dimensions in (width, height) order
       _SCREAMING_SNAKE_CASE							    =self.image_processing_class.from_dict(self.image_processor_dict							,					size=(42, 84) )
       self.assertEqual(image_processor.size							,					{'height': 84, 'width': 42} )





  def 						A  (		self      :	List[Any] )	->    Any:





       '''simple docstring'''






       pass





  @is_flaky()
  def 						A  (		self      :	List[Any] )	->    List[str]:





       '''simple docstring'''






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

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

       # Test batched
       _SCREAMING_SNAKE_CASE							    =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.size['height'],
               self.image_processor_tester.size['width'],
           )							,					)





  @is_flaky()
  def 						A  (		self      :	str )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       _SCREAMING_SNAKE_CASE							    =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
       _SCREAMING_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.size['height'],
               self.image_processor_tester.size['width'],
           )							,					)

       # Test batched
       _SCREAMING_SNAKE_CASE							    =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.size['height'],
               self.image_processor_tester.size['width'],
           )							,					)





  @is_flaky()
  def 						A  (		self      :	List[Any] )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       _SCREAMING_SNAKE_CASE							    =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
       _SCREAMING_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.size['height'],
               self.image_processor_tester.size['width'],
           )							,					)

       # Test batched
       _SCREAMING_SNAKE_CASE							    =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.size['height'],
               self.image_processor_tester.size['width'],
           )							,					)



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase_       	=	{'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''DPTFeatureExtractor''']
					lowerCAmelCase_       	=	['''DPTImageProcessor''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPTForDepthEstimation''',
					    '''DPTForSemanticSegmentation''',
					    '''DPTModel''',
					    '''DPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_dpt import DPTFeatureExtractor
										from .image_processing_dpt import DPTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpt import (
										    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPTForDepthEstimation,
										    DPTForSemanticSegmentation,
										    DPTModel,
										    DPTPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)