

"""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 ViTImageProcessor







class      __lowercase			(    unittest.TestCase					):

  def __init__(  self		:			Optional[Any]					,A		:			str					,A		:			Optional[int]=13					,A		:			List[str]=3					,A		:			str=224					,A		:			List[str]=30					,A		:			List[str]=400					,A		:			Dict=True					,A		:			Dict=None					,A		:			Tuple=True					,A		:			Tuple=[0.5, 0.5, 0.5]					,A		:			Union[str, Any]=[0.5, 0.5, 0.5]					,):






   '''simple docstring'''
   UpperCAmelCase__					:  List[str]					   =					size if size is not None else {"""height""": 18, """width""": 18}
   UpperCAmelCase__					:  Optional[int]					   =					parent
   UpperCAmelCase__					:  List[Any]					   =					batch_size
   UpperCAmelCase__					:  Union[str, Any]					   =					num_channels
   UpperCAmelCase__					:  Dict					   =					image_size
   UpperCAmelCase__					:  Any					   =					min_resolution
   UpperCAmelCase__					:  int					   =					max_resolution
   UpperCAmelCase__					:  Dict					   =					do_resize
   UpperCAmelCase__					:  Optional[int]					   =					size
   UpperCAmelCase__					:  int					   =					do_normalize
   UpperCAmelCase__					:  Optional[int]					   =					image_mean
   UpperCAmelCase__					:  Optional[Any]					   =					image_std




  def    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   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      __lowercase			(    __lowerCamelCase	,			unittest.TestCase					):
  snake_case_				       =       ViTImageProcessor if is_vision_available() else None

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					EfficientFormerImageProcessorTester(self    )

  @property
  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   return self.image_proc_tester.prepare_image_processor_dict()

  def    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					self.image_processing_class(**self.image_processor_dict    )
   self.assertTrue(hasattr(A					,"""image_mean"""    )    )
   self.assertTrue(hasattr(A					,"""image_std"""    )    )
   self.assertTrue(hasattr(A					,"""do_normalize"""    )    )
   self.assertTrue(hasattr(A					,"""do_resize"""    )    )
   self.assertTrue(hasattr(A					,"""size"""    )    )

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   pass

  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__					:  Dict					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PIL images
   UpperCAmelCase__					:  str					   =					prepare_image_inputs(self.image_proc_tester					,equal_resolution=A    )
   for image in image_inputs:
    self.assertIsInstance(A					,Image.Image    )

   # Test not batched input
   UpperCAmelCase__					:  Union[str, Any]					   =					image_processor(image_inputs[0]					,return_tensors="""pt"""    ).pixel_values
   self.assertEqual(
       encoded_images.shape					,(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )					,)

   # Test batched
   UpperCAmelCase__					:  Tuple					   =					image_processor(A					,return_tensors="""pt"""    ).pixel_values
   self.assertEqual(
       encoded_images.shape					,(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )					,)

  def    __lowercase       (  self		:			Dict    ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__					:  Dict					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random numpy tensors
   UpperCAmelCase__					:  Tuple					   =					prepare_image_inputs(self.image_proc_tester					,equal_resolution=A					,numpify=A    )
   for image in image_inputs:
    self.assertIsInstance(A					,np.ndarray    )

   # Test not batched input
   UpperCAmelCase__					:  Tuple					   =					image_processor(image_inputs[0]					,return_tensors="""pt"""    ).pixel_values
   self.assertEqual(
       encoded_images.shape					,(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )					,)

   # Test batched
   UpperCAmelCase__					:  List[str]					   =					image_processor(A					,return_tensors="""pt"""    ).pixel_values
   self.assertEqual(
       encoded_images.shape					,(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )					,)




  def    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   # Initialize image_processor
   UpperCAmelCase__					:  str					   =					self.image_processing_class(**self.image_processor_dict    )
   # create random PyTorch tensors
   UpperCAmelCase__					:  List[str]					   =					prepare_image_inputs(self.image_proc_tester					,equal_resolution=A					,torchify=A    )
   for image in image_inputs:
    self.assertIsInstance(A					,torch.Tensor    )

   # Test not batched input
   UpperCAmelCase__					:  Union[str, Any]					   =					image_processor(image_inputs[0]					,return_tensors="""pt"""    ).pixel_values
   self.assertEqual(
       encoded_images.shape					,(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )					,)

   # Test batched
   UpperCAmelCase__					:  str					   =					image_processor(A					,return_tensors="""pt"""    ).pixel_values
   self.assertEqual(
       encoded_images.shape					,(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )					,)








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase =				[
          '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

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





