


import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor







class 		_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=[0.5, 0.5, 0.5] ,  _lowerCamelCase=[0.5, 0.5, 0.5] ,  ):
       a						:Optional[Any]										=	size if size is not None else {'''height''': 18, '''width''': 18}
       a						:str										=	parent
       a						:List[str]										=	batch_size
       a						:Optional[int]										=	num_channels
       a						:Optional[int]										=	image_size
       a						:Union[str, Any]										=	min_resolution
       a						:Any										=	max_resolution
       a						:int										=	do_resize
       a						:Any										=	size
       a						:str										=	do_normalize
       a						:int										=	image_mean
       a						:Optional[Any]										=	image_std




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }







@require_torch
@require_vision
class 		_snake_case  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	DPTImageProcessor if is_vision_available() else None




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	DPTImageProcessingTester(self   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''image_mean'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''image_std'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_normalize'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_resize'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''size'''   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	self.image_processing_class.from_dict(self.image_processor_dict   )
       self.assertEqual(image_processor.size ,  {'''height''': 18, '''width''': 18}   )

       a						:Optional[Any]										=	self.image_processing_class.from_dict(self.image_processor_dict ,  size=42   )
       self.assertEqual(image_processor.size ,  {'''height''': 42, '''width''': 42}   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processing
       a						:Dict										=	self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       a						:List[Any]										=	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						:Optional[int]										=	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
       a						:Optional[Any]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processing
       a						:Tuple										=	self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       a						:Optional[int]										=	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						:Any										=	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
       a						:Optional[int]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processing
       a						:Optional[int]										=	self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       a						:Optional[int]										=	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						:Tuple										=	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
       a						:Tuple										=	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'''],
           ) ,  )









from typing import TYPE_CHECKING

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


snake_case    :   Any	    =					{'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case    :   Union[str, Any]	    =					[
         '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''VanForImageClassification''',
         '''VanModel''',
         '''VanPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     snake_case    :   Optional[Any]	    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure)





