

'''simple docstring'''
import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         LayoutLMForMaskedLM,
         LayoutLMForQuestionAnswering,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMModel,
     )







class 							A__					:





  def __init__(	self   :			List[str]							,      _a   :			List[Any]							,      _a   :			List[str]=13							,      _a   :			Any=7							,      _a   :			Optional[int]=True							,      _a   :			Union[str, Any]=True							,      _a   :			int=True							,      _a   :			Union[str, Any]=True							,      _a   :			Dict=99							,      _a   :			List[Any]=32							,      _a   :			List[Any]=5							,      _a   :			Tuple=4							,      _a   :			int=37							,      _a   :			int="gelu"							,      _a   :			Dict=0.1							,      _a   :			Optional[int]=0.1							,      _a   :			Union[str, Any]=512							,      _a   :			Optional[Any]=16							,      _a   :			Optional[int]=2							,      _a   :			Optional[int]=0.02							,      _a   :			Optional[Any]=3							,      _a   :			Any=4							,      _a   :			int=None							,      _a   :			Optional[Any]=1000							,      )			->					Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=parent
       _SCREAMING_SNAKE_CASE												=batch_size
       _SCREAMING_SNAKE_CASE												=seq_length
       _SCREAMING_SNAKE_CASE												=is_training
       _SCREAMING_SNAKE_CASE												=use_input_mask
       _SCREAMING_SNAKE_CASE												=use_token_type_ids
       _SCREAMING_SNAKE_CASE												=use_labels
       _SCREAMING_SNAKE_CASE												=vocab_size
       _SCREAMING_SNAKE_CASE												=hidden_size
       _SCREAMING_SNAKE_CASE												=num_hidden_layers
       _SCREAMING_SNAKE_CASE												=num_attention_heads
       _SCREAMING_SNAKE_CASE												=intermediate_size
       _SCREAMING_SNAKE_CASE												=hidden_act
       _SCREAMING_SNAKE_CASE												=hidden_dropout_prob
       _SCREAMING_SNAKE_CASE												=attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE												=max_position_embeddings
       _SCREAMING_SNAKE_CASE												=type_vocab_size
       _SCREAMING_SNAKE_CASE												=type_sequence_label_size
       _SCREAMING_SNAKE_CASE												=initializer_range
       _SCREAMING_SNAKE_CASE												=num_labels
       _SCREAMING_SNAKE_CASE												=num_choices
       _SCREAMING_SNAKE_CASE												=scope
       _SCREAMING_SNAKE_CASE												=range_bbox





  def 		A						(	self   :			List[Any]				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size				)

       _SCREAMING_SNAKE_CASE												=ids_tensor([self.batch_size, self.seq_length, 4]							,      self.range_bbox				)
       # Ensure that bbox is legal
       for i in range(bbox.shape[0]				):
            for j in range(bbox.shape[1]				):
                 if bbox[i, j, 3] < bbox[i, j, 1]:
                      _SCREAMING_SNAKE_CASE												=bbox[i, j, 3]
                      _SCREAMING_SNAKE_CASE												=bbox[i, j, 1]
                      _SCREAMING_SNAKE_CASE												=t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                      _SCREAMING_SNAKE_CASE												=bbox[i, j, 2]
                      _SCREAMING_SNAKE_CASE												=bbox[i, j, 0]
                      _SCREAMING_SNAKE_CASE												=t

       _SCREAMING_SNAKE_CASE												=None
       if self.use_input_mask:
            _SCREAMING_SNAKE_CASE												=ids_tensor([self.batch_size, self.seq_length]							,      vocab_size=2				)

       _SCREAMING_SNAKE_CASE												=None
       if self.use_token_type_ids:
            _SCREAMING_SNAKE_CASE												=ids_tensor([self.batch_size, self.seq_length]							,      self.type_vocab_size				)

       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=None
       _SCREAMING_SNAKE_CASE												=None
       if self.use_labels:
            _SCREAMING_SNAKE_CASE												=ids_tensor([self.batch_size]							,      self.type_sequence_label_size				)
            _SCREAMING_SNAKE_CASE												=ids_tensor([self.batch_size, self.seq_length]							,      self.num_labels				)
            _SCREAMING_SNAKE_CASE												=ids_tensor([self.batch_size]							,      self.num_choices				)

       _SCREAMING_SNAKE_CASE												=self.get_config()

       return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





  def 		A						(	self   :			List[str]				)			->					Dict:





       '''simple docstring'''






       return LayoutLMConfig(
           vocab_size=self.vocab_size							,      hidden_size=self.hidden_size							,      num_hidden_layers=self.num_hidden_layers							,      num_attention_heads=self.num_attention_heads							,      intermediate_size=self.intermediate_size							,      hidden_act=self.hidden_act							,      hidden_dropout_prob=self.hidden_dropout_prob							,      attention_probs_dropout_prob=self.attention_probs_dropout_prob							,      max_position_embeddings=self.max_position_embeddings							,      type_vocab_size=self.type_vocab_size							,      initializer_range=self.initializer_range							,      )





  def 		A						(	self   :			Dict							,      _a   :			List[str]							,      _a   :			str							,      _a   :			Optional[Any]							,      _a   :			List[str]							,      _a   :			List[str]							,      _a   :			Optional[Any]							,      _a   :			str							,      _a   :			Dict				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=LayoutLMModel(config=_a				)
       model.to(_a				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(_a							,      _a							,      attention_mask=_a							,      token_type_ids=_a				)
       _SCREAMING_SNAKE_CASE												=model(_a							,      _a							,      token_type_ids=_a				)
       _SCREAMING_SNAKE_CASE												=model(_a							,      _a				)
       self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)				)
       self.parent.assertEqual(result.pooler_output.shape							,      (self.batch_size, self.hidden_size)				)





  def 		A						(	self   :			Dict							,      _a   :			int							,      _a   :			Any							,      _a   :			Optional[int]							,      _a   :			List[str]							,      _a   :			Optional[int]							,      _a   :			str							,      _a   :			Any							,      _a   :			Dict				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=LayoutLMForMaskedLM(config=_a				)
       model.to(_a				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(_a							,      _a							,      attention_mask=_a							,      token_type_ids=_a							,      labels=_a				)
       self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.vocab_size)				)





  def 		A						(	self   :			List[str]							,      _a   :			List[Any]							,      _a   :			Optional[Any]							,      _a   :			Any							,      _a   :			Any							,      _a   :			int							,      _a   :			Optional[int]							,      _a   :			Dict							,      _a   :			Optional[int]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.num_labels
       _SCREAMING_SNAKE_CASE												=LayoutLMForSequenceClassification(_a				)
       model.to(_a				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(
           _a							,      _a							,      attention_mask=_a							,      token_type_ids=_a							,      labels=_a				)
       self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)				)





  def 		A						(	self   :			Optional[int]							,      _a   :			Tuple							,      _a   :			Any							,      _a   :			Optional[Any]							,      _a   :			Optional[Any]							,      _a   :			Optional[int]							,      _a   :			Optional[Any]							,      _a   :			List[Any]							,      _a   :			Tuple				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.num_labels
       _SCREAMING_SNAKE_CASE												=LayoutLMForTokenClassification(config=_a				)
       model.to(_a				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(_a							,      _a							,      attention_mask=_a							,      token_type_ids=_a							,      labels=_a				)
       self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.num_labels)				)





  def 		A						(	self   :			Dict							,      _a   :			Tuple							,      _a   :			Optional[Any]							,      _a   :			Dict							,      _a   :			List[Any]							,      _a   :			int							,      _a   :			Union[str, Any]							,      _a   :			List[Any]							,      _a   :			int				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=LayoutLMForQuestionAnswering(config=_a				)
       model.to(_a				)
       model.eval()
       _SCREAMING_SNAKE_CASE												=model(
           _a							,      bbox=_a							,      attention_mask=_a							,      token_type_ids=_a							,      start_positions=_a							,      end_positions=_a							,      )
       self.parent.assertEqual(result.start_logits.shape							,      (self.batch_size, self.seq_length)				)
       self.parent.assertEqual(result.end_logits.shape							,      (self.batch_size, self.seq_length)				)





  def 		A						(	self   :			Optional[Any]				)			->					str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.prepare_config_and_inputs()
       (
           (
           _SCREAMING_SNAKE_CASE		
       )					,		(
           _SCREAMING_SNAKE_CASE		
       )					,		(
           _SCREAMING_SNAKE_CASE		
       )					,		(
           _SCREAMING_SNAKE_CASE		
       )					,		(
           _SCREAMING_SNAKE_CASE		
       )					,		(
           _SCREAMING_SNAKE_CASE		
       )					,		(
           _SCREAMING_SNAKE_CASE		
       )					,		(
           _SCREAMING_SNAKE_CASE		
       )					,				
       )												=config_and_inputs
       _SCREAMING_SNAKE_CASE												={
           'input_ids': input_ids,
           'bbox': bbox,
           'token_type_ids': token_type_ids,
           'attention_mask': input_mask,
       }
       return config, inputs_dict







@require_torch
class 							A__					( A__  ,     A__  ,     unittest.TestCase						):
  A__					=       (
      (
          LayoutLMModel,
          LayoutLMForMaskedLM,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  A__					=       (
      {
          'document-question-answering': LayoutLMForQuestionAnswering,
          'feature-extraction': LayoutLMModel,
          'fill-mask': LayoutLMForMaskedLM,
          'text-classification': LayoutLMForSequenceClassification,
          'token-classification': LayoutLMForTokenClassification,
          'zero-shot': LayoutLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  A__					=       True





  def 		A						(	self   :			List[Any]				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=LayoutLMModelTester(self				)
       _SCREAMING_SNAKE_CASE												=ConfigTester(self							,      config_class=_a							,      hidden_size=37				)





  def 		A						(	self   :			Dict				)			->					List[str]:





       '''simple docstring'''






       self.config_tester.run_common_tests()





  def 		A						(	self   :			Union[str, Any]				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a				)





  def 		A						(	self   :			List[Any]				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            _SCREAMING_SNAKE_CASE												=type
            self.model_tester.create_and_check_model(*_a				)





  def 		A						(	self   :			List[str]				)			->					Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_a				)





  def 		A						(	self   :			Tuple				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_a				)





  def 		A						(	self   :			Union[str, Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_a				)





  def 		A						(	self   :			Tuple				)			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_a				)





  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
  def 		A						(	self   :			int				)			->					Optional[int]:





       '''simple docstring'''






       pass


def  _lowerCAmelCase      (		)   ->    int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]							,				device=_UpperCamelCase       )  # noqa: E231
     _SCREAMING_SNAKE_CASE												=torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]							,				device=_UpperCamelCase       )  # noqa: E231
     _SCREAMING_SNAKE_CASE												=torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]							,				device=_UpperCamelCase       )  # noqa: E231
     _SCREAMING_SNAKE_CASE												=torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]							,				device=_UpperCamelCase       )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     _SCREAMING_SNAKE_CASE												=torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]							,				device=_UpperCamelCase       )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 							A__					( unittest.TestCase						):





  @slow
  def 		A						(	self   :			List[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'				).to(_a				)

       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=prepare_layoutlm_batch_inputs()

       # forward pass
       _SCREAMING_SNAKE_CASE												=model(input_ids=_a							,      bbox=_a							,      attention_mask=_a							,      token_type_ids=_a				)

       # test the sequence output on [0, :3, :3]
       _SCREAMING_SNAKE_CASE												=torch.tensor(
           [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]							,      device=_a							,      )

       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,      _a							,      atol=1e-3				)				)

       # test the pooled output on [1, :3]
       _SCREAMING_SNAKE_CASE												=torch.tensor([-0.65_80, -0.02_14, 0.85_52]							,      device=_a				)

       self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]							,      _a							,      atol=1e-3				)				)





  @slow
  def 		A						(	self   :			int				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,      num_labels=2				).to(
           _a				)

       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=prepare_layoutlm_batch_inputs()

       # forward pass
       _SCREAMING_SNAKE_CASE												=model(
           input_ids=_a							,      bbox=_a							,      attention_mask=_a							,      token_type_ids=_a							,      labels=torch.tensor([1, 1]							,      device=_a				)							,      )

       # test whether we get a loss as a scalar
       _SCREAMING_SNAKE_CASE												=outputs.loss
       _SCREAMING_SNAKE_CASE												=torch.Size([]				)
       self.assertEqual(loss.shape							,      _a				)

       # test the shape of the logits
       _SCREAMING_SNAKE_CASE												=outputs.logits
       _SCREAMING_SNAKE_CASE												=torch.Size((2, 2)				)
       self.assertEqual(logits.shape							,      _a				)





  @slow
  def 		A						(	self   :			Optional[int]				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'							,      num_labels=13				).to(
           _a				)

       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=prepare_layoutlm_batch_inputs()

       # forward pass
       _SCREAMING_SNAKE_CASE												=model(
           input_ids=_a							,      bbox=_a							,      attention_mask=_a							,      token_type_ids=_a							,      labels=_a				)

       # test the loss calculation to be around 2.65
       # expected_loss = torch.tensor(2.65, device=torch_device)

       # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
       # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

       # test the shape of the logits
       _SCREAMING_SNAKE_CASE												=outputs.logits
       _SCREAMING_SNAKE_CASE												=torch.Size((2, 25, 13)				)
       self.assertEqual(logits.shape							,      _a				)





  @slow
  def 		A						(	self   :			Any				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'				).to(_a				)

       _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=prepare_layoutlm_batch_inputs()

       # forward pass
       _SCREAMING_SNAKE_CASE												=model(input_ids=_a							,      bbox=_a							,      attention_mask=_a							,      token_type_ids=_a				)

       # test the shape of the logits
       _SCREAMING_SNAKE_CASE												=torch.Size((2, 25)				)
       self.assertEqual(outputs.start_logits.shape							,      _a				)
       self.assertEqual(outputs.end_logits.shape							,      _a				)



'''simple docstring'''
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase				:   Dict      				=  datasets.utils.logging.get_logger(__name__)







class 							A__					( folder_based_builder.FolderBasedBuilderConfig						):
  A__					=       None
  A__					=       None







class 							A__					( folder_based_builder.FolderBasedBuilder						):
  A__					=       datasets.Image()
  A__					=       'image'
  A__					=       ImageFolderConfig
  A__					=       42  # definition at the bottom of the script
  A__					=       ImageClassification(image_column='image'  ,     label_column='label'						)

lowerCamelCase				:   Optional[int]      				=  [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
lowerCamelCase				:   Any      				=  IMAGE_EXTENSIONS
