




'''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		:			int     ,       lowercase		:			int     ,       lowercase		:			List[str]=7     ,       lowercase		:			Tuple=3     ,       lowercase		:			Union[str, Any]=18     ,       lowercase		:			Optional[int]=30     ,       lowercase		:			List[Any]=400     ,       lowercase		:			Any=True     ,       lowercase		:			Optional[Any]=32     ,       lowercase		:			str=True     ,       ):



           '''simple docstring'''

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







       def 					A   (			self		:			int       ):



           '''simple docstring'''

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






@require_torch
@require_vision
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  int            =      GLPNImageProcessor if is_vision_available() else None
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	GLPNImageProcessingTester(self       )
       @property
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	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 					A   (			self		:			Dict       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			Tuple       ):



           '''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=lowercase       )
           for image in image_inputs:
               self.assertIsInstance(lowercase     ,       Image.Image       )

           # Test not batched input (GLPNImageProcessor doesn't support batching)
           UpperCAmelCase     =	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 					A   (			self		:			Optional[int]       ):



           '''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=lowercase     ,       numpify=lowercase       )
           for image in image_inputs:
               self.assertIsInstance(lowercase     ,       np.ndarray       )

           # Test not batched input (GLPNImageProcessor doesn't support batching)
           UpperCAmelCase     =	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 					A   (			self		:			List[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=lowercase     ,       torchify=lowercase       )
           for image in image_inputs:
               self.assertIsInstance(lowercase     ,       torch.Tensor       )

           # Test not batched input (GLPNImageProcessor doesn't support batching)
           UpperCAmelCase     =	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


A									={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TableTransformerForObjectDetection',
       'TableTransformerModel',
       'TableTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_table_transformer import (
       TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TableTransformerConfig,
       TableTransformerOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TableTransformerForObjectDetection,
          TableTransformerModel,
          TableTransformerPreTrainedModel,
      )

else:
   import sys

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




