


'''simple docstring'''






import unittest

import numpy as np

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 GLPNImageProcessor




class 	a       (       unittest.TestCase    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__=7 ,			__magic_name__=3 ,			__magic_name__=18 ,			__magic_name__=30 ,			__magic_name__=4_00 ,			__magic_name__=True ,			__magic_name__=32 ,			__magic_name__=True ,			)   ->							Optional[int]:
      _a      =							parent
      _a      =							batch_size
      _a      =							num_channels
      _a      =							image_size
      _a      =							min_resolution
      _a      =							max_resolution
      _a      =							do_resize
      _a      =							size_divisor
      _a      =							do_rescale




 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }





@require_torch
@require_vision
class 	a       (       _SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		GLPNImageProcessor if is_vision_available() else None
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							GLPNImageProcessingTester(self  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(_lowercase ,			'do_resize'  )  )
      self.assertTrue(hasattr(_lowercase ,			'size_divisor'  )  )
      self.assertTrue(hasattr(_lowercase ,			'resample'  )  )
      self.assertTrue(hasattr(_lowercase ,			'do_rescale'  )  )
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      pass
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      # Initialize image_processing
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _a      =							prepare_image_inputs(self.image_processor_tester ,			equal_resolution=_lowercase  )
      for image in image_inputs:
           self.assertIsInstance(_lowercase ,			Image.Image  )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      # Initialize image_processing
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _a      =							prepare_image_inputs(self.image_processor_tester ,			equal_resolution=_lowercase ,			numpify=_lowercase  )
      for image in image_inputs:
           self.assertIsInstance(_lowercase ,			np.ndarray  )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )




 def 							__UpperCAmelCase						(	self  )   ->							int:
      # Initialize image_processing
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _a      =							prepare_image_inputs(self.image_processor_tester ,			equal_resolution=_lowercase ,			torchify=_lowercase  )
      for image in image_inputs:
           self.assertIsInstance(_lowercase ,			torch.Tensor  )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      _a      =							image_processing(image_inputs[0] ,			return_tensors='pt'  ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0  )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0  )








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

