





'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.layoutlm.modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMModel,
  )


class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=13						,     _UpperCAmelCase=7						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase=99						,     _UpperCAmelCase=32						,     _UpperCAmelCase=2						,     _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'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size)

          # convert bbox to numpy since TF does not support item assignment
          __A						:			Dict								=	ids_tensor([self.batch_size, self.seq_length, 4]						,     self.range_bbox).numpy()
          # 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]:
                         __A						:			Optional[Any]								=	bbox[i, j, 3]
                         __A						:			str								=	bbox[i, j, 1]
                         __A						:			Dict								=	t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         __A						:			Tuple								=	bbox[i, j, 2]
                         __A						:			int								=	bbox[i, j, 0]
                         __A						:			int								=	t
          __A						:			str								=	tf.convert_to_tensor(_UpperCAmelCase)

          __A						:			Optional[Any]								=	None
          if self.use_input_mask:
               __A						:			Tuple								=	random_attention_mask([self.batch_size, self.seq_length])

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

          __A						:			Union[str, Any]								=	None
          __A						:			str								=	None
          __A						:			Any								=	None
          if self.use_labels:
               __A						:			List[Any]								=	ids_tensor([self.batch_size]						,     self.type_sequence_label_size)
               __A						:			Dict								=	ids_tensor([self.batch_size, self.seq_length]						,     self.num_labels)
               __A						:			Dict								=	ids_tensor([self.batch_size]						,     self.num_choices)

          __A						:			List[Any]								=	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						,     )

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	TFLayoutLMModel(config=_UpperCAmelCase)

          __A						:			Union[str, Any]								=	model(_UpperCAmelCase						,     _UpperCAmelCase						,     attention_mask=_UpperCAmelCase						,     token_type_ids=_UpperCAmelCase)
          __A						:			Tuple								=	model(_UpperCAmelCase						,     _UpperCAmelCase						,     token_type_ids=_UpperCAmelCase)
          __A						:			Union[str, Any]								=	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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	TFLayoutLMForMaskedLM(config=_UpperCAmelCase)

          __A						:			str								=	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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	self.num_labels
          __A						:			Optional[int]								=	TFLayoutLMForSequenceClassification(config=_UpperCAmelCase)

          __A						:			str								=	model(_UpperCAmelCase						,     _UpperCAmelCase						,     attention_mask=_UpperCAmelCase						,     token_type_ids=_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels))


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[int]								=	self.num_labels
          __A						:			int								=	TFLayoutLMForTokenClassification(config=_UpperCAmelCase)

          __A						:			Tuple								=	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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	TFLayoutLMForQuestionAnswering(config=_UpperCAmelCase)

          __A						:			Dict								=	model(_UpperCAmelCase						,     _UpperCAmelCase						,     attention_mask=_UpperCAmelCase						,     token_type_ids=_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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	self.prepare_config_and_inputs()
          (
              __A
          )						:			List[Any]								=	config_and_inputs
          __A						:			Optional[int]								=	{
              'input_ids': input_ids,
              'bbox': bbox,
              'token_type_ids': token_type_ids,
              'attention_mask': input_mask,
          }
          return config, inputs_dict




@require_tf
class 			SCREAMING_SNAKE_CASE   (a__ ,		a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	(
         (
             TFLayoutLMModel,
             TFLayoutLMForMaskedLM,
             TFLayoutLMForTokenClassification,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForQuestionAnswering,
         )
         if is_tf_available()
         else ()
     )
     lowerCAmelCase		      =	(
         {
             '''feature-extraction''': TFLayoutLMModel,
             '''fill-mask''': TFLayoutLMForMaskedLM,
             '''text-classification''': TFLayoutLMForSequenceClassification,
             '''token-classification''': TFLayoutLMForTokenClassification,
             '''zero-shot''': TFLayoutLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	True
     lowerCAmelCase		      =	10


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	TFLayoutLMModelTester(self)
          __A						:			Any								=	ConfigTester(self						,     config_class=_UpperCAmelCase						,     hidden_size=37)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A						:			int								=	TFLayoutLMModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)


     @unittest.skip('Onnx compliancy broke with TF 2.10')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass






def 	_lowerCAmelCase      (  )					->					int:
     # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
     # fmt: off
     __A						:			List[str]								=	tf.convert_to_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]]      )  # noqa: E231
     __A						:			List[str]								=	tf.convert_to_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],]      )  # noqa: E231
     __A						:			str								=	tf.convert_to_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]]]      )  # noqa: E231
     __A						:			Optional[Any]								=	tf.convert_to_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]]      )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     __A						:			str								=	tf.convert_to_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]]      )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased')

          __A						:			int								=	prepare_layoutlm_batch_inputs()

          # forward pass
          __A						:			List[Any]								=	model(input_ids=_UpperCAmelCase						,     bbox=_UpperCAmelCase						,     attention_mask=_UpperCAmelCase						,     token_type_ids=_UpperCAmelCase)

          # test the sequence output on [0, :3, :3]
          __A						:			Dict								=	tf.convert_to_tensor(
              [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]						,     )

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

          # test the pooled output on [1, :3]
          __A						:			Union[str, Any]								=	tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])

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


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,     num_labels=2)

          __A						:			Tuple								=	prepare_layoutlm_batch_inputs()

          # forward pass
          __A						:			str								=	model(
              input_ids=_UpperCAmelCase						,     bbox=_UpperCAmelCase						,     attention_mask=_UpperCAmelCase						,     token_type_ids=_UpperCAmelCase						,     labels=tf.convert_to_tensor([1, 1])						,     )

          # test whether we get a loss as a scalar
          __A						:			Union[str, Any]								=	outputs.loss
          __A						:			Any								=	(2,)
          self.assertEqual(loss.shape						,     _UpperCAmelCase)

          # test the shape of the logits
          __A						:			Tuple								=	outputs.logits
          __A						:			List[str]								=	(2, 2)
          self.assertEqual(logits.shape						,     _UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,     num_labels=13)

          __A						:			int								=	prepare_layoutlm_batch_inputs()

          # forward pass
          __A						:			Dict								=	model(
              input_ids=_UpperCAmelCase						,     bbox=_UpperCAmelCase						,     attention_mask=_UpperCAmelCase						,     token_type_ids=_UpperCAmelCase						,     labels=_UpperCAmelCase)

          # test the shape of the logits
          __A						:			Any								=	outputs.logits
          __A						:			Optional[Any]								=	tf.convert_to_tensor((2, 25, 13))
          self.assertEqual(logits.shape						,     _UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased')

          __A						:			Optional[Any]								=	prepare_layoutlm_batch_inputs()

          # forward pass
          __A						:			Union[str, Any]								=	model(input_ids=_UpperCAmelCase						,     bbox=_UpperCAmelCase						,     attention_mask=_UpperCAmelCase						,     token_type_ids=_UpperCAmelCase)

          # test the shape of the logits
          __A						:			Dict								=	tf.convert_to_tensor((2, 25))
          self.assertEqual(outputs.start_logits.shape						,     _UpperCAmelCase)
          self.assertEqual(outputs.end_logits.shape						,     _UpperCAmelCase)






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					str:
     __A						:			Optional[Any]								=	[0] * len(__snake_case      )
     __A						:			Dict								=	[]
     __A						:			Optional[int]								=	[1] * len(__snake_case      )

     for values in graph.values():
          for i in values:
               indegree[i] += 1

     for i in range(len(__snake_case      )      ):
          if indegree[i] == 0:
               queue.append(__snake_case      )

     while queue:
          __A						:			int								=	queue.pop(0      )
          for x in graph[vertex]:
               indegree[x] -= 1

               if long_dist[vertex] + 1 > long_dist[x]:
                    __A						:			str								=	long_dist[vertex] + 1

               if indegree[x] == 0:
                    queue.append(__snake_case      )

     print(max(__snake_case      )      )


# Adjacency list of Graph
lowercase__   :				Dict      =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}







longest_distance(graph)