

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	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE=13	,   _SCREAMING_SNAKE_CASE=7	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=True	,   _SCREAMING_SNAKE_CASE=99	,   _SCREAMING_SNAKE_CASE=32	,   _SCREAMING_SNAKE_CASE=5	,   _SCREAMING_SNAKE_CASE=4	,   _SCREAMING_SNAKE_CASE=37	,   _SCREAMING_SNAKE_CASE="gelu"	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=0.1	,   _SCREAMING_SNAKE_CASE=512	,   _SCREAMING_SNAKE_CASE=16	,   _SCREAMING_SNAKE_CASE=2	,   _SCREAMING_SNAKE_CASE=0.02	,   _SCREAMING_SNAKE_CASE=3	,   _SCREAMING_SNAKE_CASE=4	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE=1000	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[str]          =       parent
        SCREAMING_SNAKE_CASE_			:					Tuple          =       batch_size
        SCREAMING_SNAKE_CASE_			:					List[str]          =       seq_length
        SCREAMING_SNAKE_CASE_			:					Tuple          =       is_training
        SCREAMING_SNAKE_CASE_			:					Tuple          =       use_input_mask
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       use_token_type_ids
        SCREAMING_SNAKE_CASE_			:					int          =       use_labels
        SCREAMING_SNAKE_CASE_			:					Dict          =       vocab_size
        SCREAMING_SNAKE_CASE_			:					Tuple          =       hidden_size
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       num_hidden_layers
        SCREAMING_SNAKE_CASE_			:					Dict          =       num_attention_heads
        SCREAMING_SNAKE_CASE_			:					str          =       intermediate_size
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       hidden_act
        SCREAMING_SNAKE_CASE_			:					Tuple          =       hidden_dropout_prob
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_			:					Any          =       max_position_embeddings
        SCREAMING_SNAKE_CASE_			:					List[str]          =       type_vocab_size
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       type_sequence_label_size
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       initializer_range
        SCREAMING_SNAKE_CASE_			:					Tuple          =       num_labels
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       num_choices
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       scope
        SCREAMING_SNAKE_CASE_			:					List[str]          =       range_bbox

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size   )

        SCREAMING_SNAKE_CASE_			:					str          =       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_			:					Dict          =       bbox[i, j, 3]
                          SCREAMING_SNAKE_CASE_			:					Tuple          =       bbox[i, j, 1]
                          SCREAMING_SNAKE_CASE_			:					Any          =       t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                          SCREAMING_SNAKE_CASE_			:					int          =       bbox[i, j, 2]
                          SCREAMING_SNAKE_CASE_			:					int          =       bbox[i, j, 0]
                          SCREAMING_SNAKE_CASE_			:					Tuple          =       t

        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       None
        if self.use_input_mask:
              SCREAMING_SNAKE_CASE_			:					Dict          =       ids_tensor([self.batch_size, self.seq_length]	,   vocab_size=2   )

        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       None
        if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_			:					str          =       ids_tensor([self.batch_size, self.seq_length]	,   self.type_vocab_size   )

        SCREAMING_SNAKE_CASE_			:					int          =       None
        SCREAMING_SNAKE_CASE_			:					Any          =       None
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_			:					Optional[int]          =       ids_tensor([self.batch_size]	,   self.type_sequence_label_size   )
              SCREAMING_SNAKE_CASE_			:					int          =       ids_tensor([self.batch_size, self.seq_length]	,   self.num_labels   )
              SCREAMING_SNAKE_CASE_			:					Tuple          =       ids_tensor([self.batch_size]	,   self.num_choices   )

        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.get_config()

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

  def 	UpperCAmelCase      (			self   ):





        """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 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Dict          =       LayoutLMModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					Dict          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Dict          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
        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	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       LayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					int          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size)   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Any          =       self.num_labels
        SCREAMING_SNAKE_CASE_			:					str          =       LayoutLMForSequenceClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					Tuple          =       model(
            _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels)   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Dict          =       self.num_labels
        SCREAMING_SNAKE_CASE_			:					List[str]          =       LayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					List[str]          =       model(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.num_labels)   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       LayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        SCREAMING_SNAKE_CASE_			:					int          =       model(
            _SCREAMING_SNAKE_CASE	,   bbox=_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   start_positions=_SCREAMING_SNAKE_CASE	,   end_positions=_SCREAMING_SNAKE_CASE	,   )
        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   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					int          =       self.prepare_config_and_inputs()
        (
            SCREAMING_SNAKE_CASE_  
        )			:					Union[str, Any]          =       config_and_inputs
        SCREAMING_SNAKE_CASE_			:					Tuple          =       {
            "input_ids": input_ids,
            "bbox": bbox,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict

@require_torch
class 	_A						(					snake_case__				,       snake_case__				,       unittest.TestCase):
  SCREAMING_SNAKE_CASE      : Optional[int]    =					(
      (
          LayoutLMModel,
          LayoutLMForMaskedLM,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMForQuestionAnswering,
      )
      if is_torch_available()
      else None
  )
  SCREAMING_SNAKE_CASE      : Union[str, Any]    =					(
      {
          "document-question-answering": LayoutLMForQuestionAnswering,
          "feature-extraction": LayoutLMModel,
          "fill-mask": LayoutLMForMaskedLM,
          "text-classification": LayoutLMForSequenceClassification,
          "token-classification": LayoutLMForTokenClassification,
          "zero-shot": LayoutLMForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE      : Union[str, Any]    =					True

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       LayoutLMModelTester(self   )
        SCREAMING_SNAKE_CASE_			:					Any          =       ConfigTester(self	,   config_class=_SCREAMING_SNAKE_CASE	,   hidden_size=37   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        self.config_tester.run_common_tests()

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Dict          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE   )



  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        pass

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Dict          =       torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]   ,				device=_UpperCAmelCase					)  # noqa: E231
      SCREAMING_SNAKE_CASE_			:					str          =       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_			:					Dict          =       torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]   ,				device=_UpperCAmelCase					)  # noqa: E231
      SCREAMING_SNAKE_CASE_			:					int          =       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_			:					Optional[int]          =       torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]   ,				device=_UpperCAmelCase					)  # noqa: E231
      # fmt: on

      return input_ids, attention_mask, bbox, token_type_ids, labels



@require_torch
class 	_A						(					unittest.TestCase):

  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'   ).to(_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					List[Any]          =       prepare_layoutlm_batch_inputs()

        # forward pass
        SCREAMING_SNAKE_CASE_			:					Dict          =       model(input_ids=_SCREAMING_SNAKE_CASE	,   bbox=_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE   )

        # test the sequence output on [0, :3, :3]
        SCREAMING_SNAKE_CASE_			:					List[str]          =       torch.tensor(
            [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]	,   device=_SCREAMING_SNAKE_CASE	,   )

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

        # test the pooled output on [1, :3]
        SCREAMING_SNAKE_CASE_			:					int          =       torch.tensor([-0.6580, -0.0214, 0.8552]	,   device=_SCREAMING_SNAKE_CASE   )

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

  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'	,   num_labels=2   ).to(
            _SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					int          =       prepare_layoutlm_batch_inputs()

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

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

        # test the shape of the logits
        SCREAMING_SNAKE_CASE_			:					int          =       outputs.logits
        SCREAMING_SNAKE_CASE_			:					str          =       torch.Size((2, 2)   )
        self.assertEqual(logits.shape	,   _SCREAMING_SNAKE_CASE   )

  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







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

        SCREAMING_SNAKE_CASE_			:					Dict          =       prepare_layoutlm_batch_inputs()

        # forward pass
        SCREAMING_SNAKE_CASE_			:					str          =       model(
            input_ids=_SCREAMING_SNAKE_CASE	,   bbox=_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE	,   labels=_SCREAMING_SNAKE_CASE   )

        # 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_			:					int          =       outputs.logits
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       torch.Size((2, 25, 13)   )
        self.assertEqual(logits.shape	,   _SCREAMING_SNAKE_CASE   )



  @slow
  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'   ).to(_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					Tuple          =       prepare_layoutlm_batch_inputs()

        # forward pass
        SCREAMING_SNAKE_CASE_			:					Tuple          =       model(input_ids=_SCREAMING_SNAKE_CASE	,   bbox=_SCREAMING_SNAKE_CASE	,   attention_mask=_SCREAMING_SNAKE_CASE	,   token_type_ids=_SCREAMING_SNAKE_CASE   )

        # test the shape of the logits
        SCREAMING_SNAKE_CASE_			:					List[str]          =       torch.Size((2, 25)   )
        self.assertEqual(outputs.start_logits.shape	,   _SCREAMING_SNAKE_CASE   )
        self.assertEqual(outputs.end_logits.shape	,   _SCREAMING_SNAKE_CASE   )



import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase       :				Optional[int]         =					logging.get_logger(__name__)  # pylint: disable=invalid-name


lowerCAmelCase       :				Any         =					'lfs-multipart-upload'
class 	_A						(					__magic_name__):

  @staticmethod
  def 	UpperCAmelCase      (			_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       parser.add_parser(
            'lfs-enable-largefiles'	,   help=(
                'Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'
            )	,   )
        enable_parser.add_argument('path'	,   type=_SCREAMING_SNAKE_CASE	,   help='Local path to repository you want to configure.'   )
        enable_parser.set_defaults(func=lambda _SCREAMING_SNAKE_CASE   : LfsEnableCommand(_SCREAMING_SNAKE_CASE   )   )

        SCREAMING_SNAKE_CASE_			:					str          =       parser.add_parser(
            _SCREAMING_SNAKE_CASE	,   help=(
                'Deprecated: use `huggingface-cli` instead. '
                'Command will get called by git-lfs, do not call it directly.'
            )	,   )
        upload_parser.set_defaults(func=lambda _SCREAMING_SNAKE_CASE   : LfsUploadCommand(_SCREAMING_SNAKE_CASE   )   )

class 	_A						:

  def __init__(			self	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       args



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        warnings.warn(
            'Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'   )
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       os.path.abspath(self.args.path   )
        if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
              print('This does not look like a valid git repo.'   )
              exit(1   )
        subprocess.run(
            'git config lfs.customtransfer.multipart.path transformers-cli'.split()	,   check=_SCREAMING_SNAKE_CASE	,   cwd=_SCREAMING_SNAKE_CASE   )
        subprocess.run(
            f"git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}".split()	,   check=_SCREAMING_SNAKE_CASE	,   cwd=_SCREAMING_SNAKE_CASE	,   )
        print('Local repo set up for largefiles'   )

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Any          =       json.dumps(a					) + '\n'
      sys.stdout.write(a					)
      sys.stdout.flush()

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[Any]          =       json.loads(sys.stdin.readline().strip()					)

      if "terminate" in (msg.get('type'					), msg.get('event'					)):
            # terminate message received
            return None

      if msg.get('event'					) not in ("download", "upload"):
            logger.critical('Received unexpected message'					)
            sys.exit(1					)

      return msg
class 	_A						(					__magic_name__):

  def __init__(			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       filepath
        SCREAMING_SNAKE_CASE_			:					Tuple          =       seek_from
        SCREAMING_SNAKE_CASE_			:					Tuple          =       read_limit
        SCREAMING_SNAKE_CASE_			:					int          =       0

  def __enter__(			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       open(self.filepath	,   'rb'   )
        self.f.seek(self.seek_from   )
        return self

  def __len__(			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       os.fstat(self.f.fileno()   ).st_size
        return min(self.read_limit	,   total_length - self.seek_from   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE=-1   ):





        """simple docstring"""







        if self.n_seen >= self.read_limit:
              return b""
        SCREAMING_SNAKE_CASE_			:					str          =       self.read_limit - self.n_seen
        SCREAMING_SNAKE_CASE_			:					Any          =       self.f.read(remaining_amount if n < 0 else min(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )   )
        self.n_seen += len(_SCREAMING_SNAKE_CASE   )
        return data

  def __iter__(			self   ):





        """simple docstring"""







        yield self.read(n=4 * 1024 * 1024   )



  def __exit__(			self	,   *_SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        self.f.close()

class 	_A						:

  def __init__(			self	,   _SCREAMING_SNAKE_CASE   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       args



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       json.loads(sys.stdin.readline().strip()   )
        if not (init_msg.get('event'   ) == "init" and init_msg.get('operation'   ) == "upload"):
              write_msg({'error': {'code': 32, 'message': 'Wrong lfs init operation'}}   )
              sys.exit(1   )

        # The transfer process should use the information it needs from the
        # initiation structure, and also perform any one-off setup tasks it
        # needs to do. It should then respond on stdout with a simple empty
        # confirmation structure, as follows:
        write_msg({}   )

        # After the initiation exchange, git-lfs will send any number of
        # transfer requests to the stdin of the transfer process, in a serial sequence.
        while True:
              SCREAMING_SNAKE_CASE_			:					str          =       read_msg()
              if msg is None:
                    # When all transfers have been processed, git-lfs will send
                    # a terminate event to the stdin of the transfer process.
                    # On receiving this message the transfer process should
                    # clean up and terminate. No response is expected.
                    sys.exit(0   )

              SCREAMING_SNAKE_CASE_			:					int          =       msg['oid']
              SCREAMING_SNAKE_CASE_			:					int          =       msg['path']
              SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       msg['action']['href']
              SCREAMING_SNAKE_CASE_			:					str          =       msg['action']['header']
              SCREAMING_SNAKE_CASE_			:					Any          =       int(header.pop('chunk_size'   )   )
              SCREAMING_SNAKE_CASE_			:					List[str]          =       list(header.values()   )

              SCREAMING_SNAKE_CASE_			:					List[str]          =       []
              for i, presigned_url in enumerate(_SCREAMING_SNAKE_CASE   ):
                    with FileSlice(_SCREAMING_SNAKE_CASE	,   seek_from=i * chunk_size	,   read_limit=_SCREAMING_SNAKE_CASE   ) as data:
                          SCREAMING_SNAKE_CASE_			:					int          =       requests.put(_SCREAMING_SNAKE_CASE	,   data=_SCREAMING_SNAKE_CASE   )
                          r.raise_for_status()
                          parts.append(
                              {
                                  'etag': r.headers.get('etag'   ),
                                  'partNumber': i + 1,
                              }   )
                          # In order to support progress reporting while data is uploading / downloading,
                          # the transfer process should post messages to stdout
                          write_msg(
                              {
                                  'event': 'progress',
                                  'oid': oid,
                                  'bytesSoFar': (i + 1) * chunk_size,
                                  'bytesSinceLast': chunk_size,
                              }   )
                          # Not precise but that's ok.

              SCREAMING_SNAKE_CASE_			:					Dict          =       requests.post(
                  _SCREAMING_SNAKE_CASE	,   json={
                      'oid': oid,
                      'parts': parts,
                  }	,   )
              r.raise_for_status()

              write_msg({'event': 'complete', 'oid': oid}   )






