
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 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A ,     _A=13 ,     _A=7 ,     _A=True ,     _A=True ,     _A=True ,     _A=True ,     _A=99 ,     _A=32 ,     _A=5 ,     _A=4 ,     _A=37 ,     _A="gelu" ,     _A=0.1 ,     _A=0.1 ,     _A=512 ,     _A=16 ,     _A=2 ,     _A=0.02 ,     _A=3 ,     _A=4 ,     _A=None ,     _A=1000 ,     )				->				str:
          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     _UpperCamelCase	(							self	)				->				Any:
          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     _UpperCamelCase	(							self	)				->				Dict:
          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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A	)				->				List[str]:
          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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A	)				->				List[str]:
          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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A	)				->				Dict:
          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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A	)				->				int:
          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     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A ,     _A	)				->				Optional[Any]:
          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     _UpperCamelCase	(							self	)				->				str:
          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 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    unittest.TestCase	):






       """simple docstring"""


       UpperCAmelCase_				       =(
           (
               LayoutLMModel,
               LayoutLMForMaskedLM,
               LayoutLMForSequenceClassification,
               LayoutLMForTokenClassification,
               LayoutLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       UpperCAmelCase_				       =(
           {
               "document-question-answering": LayoutLMForQuestionAnswering,
               "feature-extraction": LayoutLMModel,
               "fill-mask": LayoutLMForMaskedLM,
               "text-classification": LayoutLMForSequenceClassification,
               "token-classification": LayoutLMForTokenClassification,
               "zero-shot": LayoutLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCAmelCase_				       =True



       def     _UpperCamelCase	(							self	)				->				List[str]:
          SCREAMING_SNAKE_CASE_	=      LayoutLMModelTester(self	)
          SCREAMING_SNAKE_CASE_	=      ConfigTester(self ,     config_class=_A ,     hidden_size=37	)



       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          self.config_tester.run_common_tests()



       def     _UpperCamelCase	(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_A	)



       def     _UpperCamelCase	(							self	)				->				Any:
          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     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_A	)



       def     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_A	)



       def     _UpperCamelCase	(							self	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_A	)



       def     _UpperCamelCase	(							self	)				->				Optional[Any]:
          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     _UpperCamelCase	(							self	)				->				List[str]:
          pass







def  A__ (       ):
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   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=__lowerCamelCase						)  # 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=__lowerCamelCase						)  # 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=__lowerCamelCase						)  # 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=__lowerCamelCase						)  # 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=__lowerCamelCase						)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels





@require_torch
class 				UpperCamelCase__		(	unittest.TestCase	):






       """simple docstring"""


       @slow
       def     _UpperCamelCase	(							self	)				->				int:
          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(
              [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] ,     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([-0.6580, -0.0214, 0.8552] ,     device=_A	)

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



       @slow
       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          # initialize model with randomly initialized sequence classification head
          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     _UpperCamelCase	(							self	)				->				Optional[int]:
          # initialize model with randomly initialized token classification head
          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     _UpperCamelCase	(							self	)				->				Tuple:
          # initialize model with randomly initialized token classification head
          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	)




from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__UpperCAmelCase   						=     datasets.utils.logging.get_logger(__name__)





class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilderConfig	):






       """simple docstring"""


       UpperCAmelCase_				       =None
       UpperCAmelCase_				       =None







class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilder	):






       """simple docstring"""


       UpperCAmelCase_				       =datasets.Image()
       UpperCAmelCase_				       ="image"
       UpperCAmelCase_				       =ImageFolderConfig
       UpperCAmelCase_				       =42  # definition at the bottom of the script
       UpperCAmelCase_				       =ImageClassification(image_column="image"       ,    label_column="label"	)







__UpperCAmelCase   						=     [
    ".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",
]
__UpperCAmelCase   						=     IMAGE_EXTENSIONS


