
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 	__lowerCAmelCase       :




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase=13		,			__UpperCAmelCase=7		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=True		,			__UpperCAmelCase=99		,			__UpperCAmelCase=32		,			__UpperCAmelCase=5		,			__UpperCAmelCase=4		,			__UpperCAmelCase=37		,			__UpperCAmelCase="gelu"		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=0.1		,			__UpperCAmelCase=512		,			__UpperCAmelCase=16		,			__UpperCAmelCase=2		,			__UpperCAmelCase=0.02		,			__UpperCAmelCase=3		,			__UpperCAmelCase=4		,			__UpperCAmelCase=None		,			__UpperCAmelCase=1000		,			):



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




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size			)

    __lowerCamelCase  							=      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]:
             __lowerCamelCase  							=      bbox[i, j, 3]
             __lowerCamelCase  							=      bbox[i, j, 1]
             __lowerCamelCase  							=      t
          if bbox[i, j, 2] < bbox[i, j, 0]:
             __lowerCamelCase  							=      bbox[i, j, 2]
             __lowerCamelCase  							=      bbox[i, j, 0]
             __lowerCamelCase  							=      t

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

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

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

    __lowerCamelCase  							=      self.get_config()

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




 def     lowerCamelCase (  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     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      LayoutLMModel(config=UpperCamelCase__			)
    model.to(UpperCamelCase__			)
    model.eval()
    __lowerCamelCase  							=      model(UpperCamelCase__		,			UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			token_type_ids=UpperCamelCase__			)
    __lowerCamelCase  							=      model(UpperCamelCase__		,			UpperCamelCase__		,			token_type_ids=UpperCamelCase__			)
    __lowerCamelCase  							=      model(UpperCamelCase__		,			UpperCamelCase__			)
    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     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      LayoutLMForMaskedLM(config=UpperCamelCase__			)
    model.to(UpperCamelCase__			)
    model.eval()
    __lowerCamelCase  							=      model(UpperCamelCase__		,			UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			token_type_ids=UpperCamelCase__		,			labels=UpperCamelCase__			)
    self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.num_labels
    __lowerCamelCase  							=      LayoutLMForSequenceClassification(UpperCamelCase__			)
    model.to(UpperCamelCase__			)
    model.eval()
    __lowerCamelCase  							=      model(
        UpperCamelCase__		,			UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			token_type_ids=UpperCamelCase__		,			labels=UpperCamelCase__			)
    self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.num_labels
    __lowerCamelCase  							=      LayoutLMForTokenClassification(config=UpperCamelCase__			)
    model.to(UpperCamelCase__			)
    model.eval()
    __lowerCamelCase  							=      model(UpperCamelCase__		,			UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			token_type_ids=UpperCamelCase__		,			labels=UpperCamelCase__			)
    self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      LayoutLMForQuestionAnswering(config=UpperCamelCase__			)
    model.to(UpperCamelCase__			)
    model.eval()
    __lowerCamelCase  							=      model(
        UpperCamelCase__		,			bbox=UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			token_type_ids=UpperCamelCase__		,			start_positions=UpperCamelCase__		,			end_positions=UpperCamelCase__		,			)
    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     lowerCamelCase (  self			):



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



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




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      LayoutLMModelTester(self			)
    __lowerCamelCase  							=      ConfigTester(self		,			config_class=UpperCamelCase__		,			hidden_size=37			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       __lowerCamelCase  							=      type
       self.model_tester.create_and_check_model(*UpperCamelCase__			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__			)




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



    '''simple docstring'''
    pass





def 						a__     (						):
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   __lowerCamelCase  							=      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=_SCREAMING_SNAKE_CASE							)  # noqa: E231
   __lowerCamelCase  							=      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=_SCREAMING_SNAKE_CASE							)  # noqa: E231
   __lowerCamelCase  							=      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=_SCREAMING_SNAKE_CASE							)  # noqa: E231
   __lowerCamelCase  							=      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=_SCREAMING_SNAKE_CASE							)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   __lowerCamelCase  							=      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=_SCREAMING_SNAKE_CASE							)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels





@require_torch
class 	__lowerCAmelCase       (		unittest.TestCase				):




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''			).to(UpperCamelCase__			)

    __lowerCamelCase  							=      prepare_layoutlm_batch_inputs()

    # forward pass
    __lowerCamelCase  							=      model(input_ids=UpperCamelCase__		,			bbox=UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			token_type_ids=UpperCamelCase__			)

    # test the sequence output on [0, :3, :3]
    __lowerCamelCase  							=      torch.tensor(
        [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]		,			device=UpperCamelCase__		,			)

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

    # test the pooled output on [1, :3]
    __lowerCamelCase  							=      torch.tensor([-0.6_580, -0.0_214, 0.8_552]		,			device=UpperCamelCase__			)

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




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    # initialize model with randomly initialized sequence classification head
    __lowerCamelCase  							=      LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''		,			num_labels=2			).to(
        UpperCamelCase__			)

    __lowerCamelCase  							=      prepare_layoutlm_batch_inputs()

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

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

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




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    # initialize model with randomly initialized token classification head
    __lowerCamelCase  							=      LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''		,			num_labels=13			).to(
        UpperCamelCase__			)

    __lowerCamelCase  							=      prepare_layoutlm_batch_inputs()

    # forward pass
    __lowerCamelCase  							=      model(
        input_ids=UpperCamelCase__		,			bbox=UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			token_type_ids=UpperCamelCase__		,			labels=UpperCamelCase__			)

    # 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
    __lowerCamelCase  							=      outputs.logits
    __lowerCamelCase  							=      torch.Size((2, 25, 13)			)
    self.assertEqual(logits.shape		,			UpperCamelCase__			)




 @slow
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    # initialize model with randomly initialized token classification head
    __lowerCamelCase  							=      LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''			).to(UpperCamelCase__			)

    __lowerCamelCase  							=      prepare_layoutlm_batch_inputs()

    # forward pass
    __lowerCamelCase  							=      model(input_ids=UpperCamelCase__		,			bbox=UpperCamelCase__		,			attention_mask=UpperCamelCase__		,			token_type_ids=UpperCamelCase__			)

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











import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand





def 						a__     (						_UpperCamelCase     :	Optional[Any]							):
   return EnvironmentCommand()





class 	__lowerCAmelCase       (		lowerCAmelCase__				):




 @staticmethod
 def     lowerCamelCase (  __UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      parser.add_parser('''env'''		,			help='''Print relevant system environment info.'''			)
    download_parser.set_defaults(func=__UpperCAmelCase			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      {
        '''`datasets` version''': version,
        '''Platform''': platform.platform(),
        '''Python version''': platform.python_version(),
        '''Huggingface_hub version''': huggingface_hub.__version__,
        '''PyArrow version''': pyarrow.__version__,
        '''Pandas version''': pandas.__version__,
    }

    print('''\nCopy-and-paste the text below in your GitHub issue.\n'''			)
    print(self.format_dict(__UpperCAmelCase			)			)

    return info




 @staticmethod
 def     lowerCamelCase (  __UpperCAmelCase			):



    '''simple docstring'''
    return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]			) + "\n"






