'''simple docstring'''






import unittest

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

from ...generation.test_utils import GenerationTesterMixin
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 (
       LiltForQuestionAnswering,
       LiltForSequenceClassification,
       LiltForTokenClassification,
       LiltModel,
   )
   from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST







class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			str, UpperCamelCase__      :			str, UpperCamelCase__      :			str=13, UpperCamelCase__      :			Any=7, UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			str=True, UpperCamelCase__      :			int=True, UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[Any]=99, UpperCamelCase__      :			Any=24, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]=6, UpperCamelCase__      :			List[str]=37, UpperCamelCase__      :			int="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			List[str]=0.1, UpperCamelCase__      :			List[Any]=5_12, UpperCamelCase__      :			List[Any]=16, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			str=0.02, UpperCamelCase__      :			str=3, UpperCamelCase__      :			List[Any]=None, UpperCamelCase__      :			Optional[Any]=10_00, )  ->			Optional[int]:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  seq_length
        _A    		=  is_training
        _A    		=  use_input_mask
        _A    		=  use_token_type_ids
        _A    		=  use_labels
        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  intermediate_size
        _A    		=  hidden_act
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  type_sequence_label_size
        _A    		=  initializer_range
        _A    		=  num_labels
        _A    		=  scope
        _A    		=  range_bbox
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[str]:
        _A    		=  ids_tensor([self.batch_size, self.seq_length], self.vocab_size     )

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

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

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

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

        _A    		=  self.get_config()

        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        return LiltConfig(
            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      :			Dict, UpperCamelCase__      :			int, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			int, UpperCamelCase__      :			List[str], UpperCamelCase__      :			int, UpperCamelCase__      :			Any, )  ->			int:
        _A    		=  LiltModel(config=__lowerCAmelCase     )
        model.to(__lowerCAmelCase     )
        model.eval()
        _A    		=  model(__lowerCAmelCase, bbox=__lowerCAmelCase, attention_mask=__lowerCAmelCase, token_type_ids=__lowerCAmelCase     )
        _A    		=  model(__lowerCAmelCase, bbox=__lowerCAmelCase, token_type_ids=__lowerCAmelCase     )
        _A    		=  model(__lowerCAmelCase, bbox=__lowerCAmelCase     )

        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      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Optional[Any], UpperCamelCase__      :			List[str], UpperCamelCase__      :			List[str], UpperCamelCase__      :			List[Any], )  ->			Optional[Any]:
        _A    		=  self.num_labels
        _A    		=  LiltForTokenClassification(config=__lowerCAmelCase     )
        model.to(__lowerCAmelCase     )
        model.eval()
        _A    		=  model(
            __lowerCAmelCase, bbox=__lowerCAmelCase, attention_mask=__lowerCAmelCase, token_type_ids=__lowerCAmelCase, labels=__lowerCAmelCase     )
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)     )
   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			List[str], UpperCamelCase__      :			str, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			str, UpperCamelCase__      :			List[Any], UpperCamelCase__      :			Any, UpperCamelCase__      :			Union[str, Any], )  ->			Dict:
        _A    		=  LiltForQuestionAnswering(config=__lowerCAmelCase     )
        model.to(__lowerCAmelCase     )
        model.eval()
        _A    		=  model(
            __lowerCAmelCase, bbox=__lowerCAmelCase, attention_mask=__lowerCAmelCase, token_type_ids=__lowerCAmelCase, start_positions=__lowerCAmelCase, end_positions=__lowerCAmelCase, )
        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      :			List[Any]     )  ->			Dict:
        _A    		=  self.prepare_config_and_inputs()
        (
            _A      
        )    		=  config_and_inputs
        _A    		=  {
            """input_ids""": input_ids,
            """bbox""": bbox,
            """token_type_ids""": token_type_ids,
            """attention_mask""": input_mask,
        }
        return config, inputs_dict







@require_torch
class lowercase_       (		SCREAMING_SNAKE_CASE__    ,    SCREAMING_SNAKE_CASE__    ,    SCREAMING_SNAKE_CASE__    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   (
       (
           LiltModel,
           LiltForSequenceClassification,
           LiltForTokenClassification,
           LiltForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   __lowerCAmelCase         =   (
       {
           '''feature-extraction''': LiltModel,
           '''question-answering''': LiltForQuestionAnswering,
           '''text-classification''': LiltForSequenceClassification,
           '''token-classification''': LiltForTokenClassification,
           '''zero-shot''': LiltForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   False
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			str, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			str:
        return True
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Any:
        _A    		=  LiltModelTester(self     )
        _A    		=  ConfigTester(self, config_class=__lowerCAmelCase, hidden_size=37     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Dict:
        self.config_tester.run_common_tests()
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Union[str, Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             _A    		=  type
             self.model_tester.create_and_check_model(*__lowerCAmelCase     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			int:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			List[Any]:
        _A    		=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase     )

   @slow
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[str]:
        for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A    		=  LiltModel.from_pretrained(__lowerCAmelCase     )
             self.assertIsNotNone(__lowerCAmelCase     )







@require_torch
@slow
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Tuple:
        _A    		=  LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'     ).to(__lowerCAmelCase     )

        _A    		=  torch.tensor([[1, 2]], device=__lowerCAmelCase     )
        _A    		=  torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]], device=__lowerCAmelCase     )

        # forward pass
        with torch.no_grad():
             _A    		=  model(input_ids=__lowerCAmelCase, bbox=__lowerCAmelCase     )

        _A    		=  torch.Size([1, 2, 7_68]     )
        _A    		=  torch.tensor(
            [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]], device=__lowerCAmelCase, )

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











'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






