

"""simple docstring"""




import unittest

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

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


if is_torch_available():
     import torch

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


class 		_A							:





      def __init__(	self     ,    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     ,    ) ->       Dict:
         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		) ->       Optional[int]:
         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		) ->       List[Any]:
         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     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       Dict:
         lowerCamelCase__						       =      LayoutLMModel(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						       =      model(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    attention_mask=SCREAMING_SNAKE_CASE__     ,    token_type_ids=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      model(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    token_type_ids=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						       =      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        _lowerCamelCase	(	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__		) ->       List[Any]:
         lowerCamelCase__						       =      LayoutLMForMaskedLM(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						       =      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        _lowerCamelCase	(	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__		) ->       str:
         lowerCamelCase__						       =      self.num_labels
         lowerCamelCase__						       =      LayoutLMForSequenceClassification(SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						       =      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        _lowerCamelCase	(	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__		) ->       List[Any]:
         lowerCamelCase__						       =      self.num_labels
         lowerCamelCase__						       =      LayoutLMForTokenClassification(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						       =      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        _lowerCamelCase	(	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__		) ->       List[str]:
         lowerCamelCase__						       =      LayoutLMForQuestionAnswering(config=SCREAMING_SNAKE_CASE__		)
         model.to(SCREAMING_SNAKE_CASE__		)
         model.eval()
         lowerCamelCase__						       =      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        _lowerCamelCase	(	self		) ->       List[Any]:
         lowerCamelCase__						       =      self.prepare_config_and_inputs()
         (
             (
             lowerCamelCase__
         )       ,							(
             lowerCamelCase__
         )       ,							(
             lowerCamelCase__
         )       ,							(
             lowerCamelCase__
         )       ,							(
             lowerCamelCase__
         )       ,							(
             lowerCamelCase__
         )       ,							(
             lowerCamelCase__
         )       ,							(
             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 		_A							(      __a					,							__a					,							unittest.TestCase       ):
      __a             =						(
          (
              LayoutLMModel,
              LayoutLMForMaskedLM,
              LayoutLMForSequenceClassification,
              LayoutLMForTokenClassification,
              LayoutLMForQuestionAnswering,
          )
          if is_torch_available()
          else None
      )
      __a             =						(
          {
              'document-question-answering': LayoutLMForQuestionAnswering,
              'feature-extraction': LayoutLMModel,
              'fill-mask': LayoutLMForMaskedLM,
              'text-classification': LayoutLMForSequenceClassification,
              'token-classification': LayoutLMForTokenClassification,
              'zero-shot': LayoutLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __a             =						True





      def        _lowerCamelCase	(	self		) ->       List[Any]:
         lowerCamelCase__						       =      LayoutLMModelTester(self		)
         lowerCamelCase__						       =      ConfigTester(self     ,    config_class=SCREAMING_SNAKE_CASE__     ,    hidden_size=37		)





      def        _lowerCamelCase	(	self		) ->       List[str]:
         self.config_tester.run_common_tests()





      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         lowerCamelCase__						       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         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(*SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       Tuple:
         lowerCamelCase__						       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       Tuple:
         lowerCamelCase__						       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       Tuple:
         lowerCamelCase__						       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       List[Any]:
         lowerCamelCase__						       =      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        _lowerCamelCase	(	self		) ->       Dict:
         pass






def 						UpperCAmelCase__ (					)   ->   Union[str, Any]:




   """simple docstring"""
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   lowerCamelCase__						       =      torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]						,     device=A__ )  # 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=A__ )  # noqa: E231
   lowerCamelCase__						       =      torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]						,     device=A__ )  # 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=A__ )  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   lowerCamelCase__						       =      torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]						,     device=A__ )  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class 		_A							(      unittest.TestCase       ):





      @slow
      def        _lowerCamelCase	(	self		) ->       str:
         lowerCamelCase__						       =      LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__						       =      prepare_layoutlm_batch_inputs()

         # forward pass
         lowerCamelCase__						       =      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]
         lowerCamelCase__						       =      torch.tensor(
             [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]     ,    device=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]
         lowerCamelCase__						       =      torch.tensor([-0.65_80, -0.02_14, 0.85_52]     ,    device=SCREAMING_SNAKE_CASE__		)

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





      @slow
      def        _lowerCamelCase	(	self		) ->       List[str]:
         # initialize model with randomly initialized sequence classification head
         lowerCamelCase__						       =      LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"     ,    num_labels=2		).to(
             SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__						       =      prepare_layoutlm_batch_inputs()

         # forward pass
         lowerCamelCase__						       =      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
         lowerCamelCase__						       =      outputs.loss
         lowerCamelCase__						       =      torch.Size([]		)
         self.assertEqual(loss.shape     ,    SCREAMING_SNAKE_CASE__		)

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





      @slow
      def        _lowerCamelCase	(	self		) ->       Any:
         # initialize model with randomly initialized token classification head
         lowerCamelCase__						       =      LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"     ,    num_labels=13		).to(
             SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__						       =      prepare_layoutlm_batch_inputs()

         # forward pass
         lowerCamelCase__						       =      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
         lowerCamelCase__						       =      outputs.logits
         lowerCamelCase__						       =      torch.Size((2, 25, 13)		)
         self.assertEqual(logits.shape     ,    SCREAMING_SNAKE_CASE__		)





      @slow
      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         # initialize model with randomly initialized token classification head
         lowerCamelCase__						       =      LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__						       =      prepare_layoutlm_batch_inputs()

         # forward pass
         lowerCamelCase__						       =      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
         lowerCamelCase__						       =      torch.Size((2, 25)		)
         self.assertEqual(outputs.start_logits.shape     ,    SCREAMING_SNAKE_CASE__		)
         self.assertEqual(outputs.end_logits.shape     ,    SCREAMING_SNAKE_CASE__		)







"""simple docstring"""




SCREAMING_SNAKE_CASE_  :      Optional[int]							=      {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

SCREAMING_SNAKE_CASE_  :      str							=      {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}



def 						UpperCAmelCase__ (					A__						,     A__						,     A__ )   ->   list:




   """simple docstring"""
   lowerCamelCase__						       =      True
   lowerCamelCase__						       =      []

   for neighbour in graph[vert]:
      if not visited[neighbour]:
         connected_verts += dfs(A__						,     A__						,     A__ )

   return [vert, *connected_verts]



def 						UpperCAmelCase__ (					A__ )   ->   list:




   """simple docstring"""
   lowerCamelCase__						       =      len(A__ )
   lowerCamelCase__						       =      graph_size * [False]
   lowerCamelCase__						       =      []

   for i in range(A__ ):
      if not visited[i]:
         lowerCamelCase__						       =      dfs(A__						,     A__						,     A__ )
         components_list.append(A__ )

   return components_list


if __name__ == "__main__":
     import doctest

     doctest.testmod()




