


"""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    lowerCamelCase__						(		unittest.TestCase    ):
      def __init__(		self	:	int      ,       A_	:	int      ,       A_	:	Any=7      ,       A_	:	Tuple=3      ,       A_	:	Tuple=1_8      ,       A_	:	List[str]=3_0      ,       A_	:	str=4_0_0      ,       A_	:	int=True      ,       A_	:	List[str]=3_2      ,       A_	:	List[Any]=True      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	num_channels
       __lowercase									=	image_size
       __lowercase									=	min_resolution
       __lowercase									=	max_resolution
       __lowercase									=	do_resize
       __lowercase									=	size_divisor
       __lowercase									=	do_rescale






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }






@require_torch
@require_vision
class    lowerCamelCase__						(		_a			,   unittest.TestCase    ):
      a :     Dict											=		GLPNImageProcessor if is_vision_available() else None
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	GLPNImageProcessingTester(self							)
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       return self.image_processor_tester.prepare_image_processor_dict()
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(A_      ,       """do_resize"""							)							)
       self.assertTrue(hasattr(A_      ,       """size_divisor"""							)							)
       self.assertTrue(hasattr(A_      ,       """resample"""							)							)
       self.assertTrue(hasattr(A_      ,       """do_rescale"""							)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       pass
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


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

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       __lowercase									=	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 (GLPNImageProcessor doesn't support batching)
       __lowercase									=	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 SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       __lowercase									=	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 (GLPNImageProcessor doesn't support batching)
       __lowercase									=	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_torch_available, is_vision_available


UpperCAmelCase__  							={"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__  							=[
           "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

       UpperCAmelCase__  							=_LazyModule(__name__, globals()["__file__"], _import_structure)
