"""simple docstring"""



import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     LayoutLMvaConfig,
     LayoutLMvaForQuestionAnswering,
     LayoutLMvaForSequenceClassification,
     LayoutLMvaForTokenClassification,
     LayoutLMvaModel,
 )
 from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=2  ,						lowerCamelCase=3  ,						lowerCamelCase=4  ,						lowerCamelCase=2  ,						lowerCamelCase=7  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=True  ,						lowerCamelCase=99  ,						lowerCamelCase=36  ,						lowerCamelCase=3  ,						lowerCamelCase=4  ,						lowerCamelCase=37  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=0.1  ,						lowerCamelCase=0.1  ,						lowerCamelCase=512  ,						lowerCamelCase=16  ,						lowerCamelCase=2  ,						lowerCamelCase=0.02  ,						lowerCamelCase=6  ,						lowerCamelCase=6  ,						lowerCamelCase=3  ,						lowerCamelCase=4  ,						lowerCamelCase=None  ,						lowerCamelCase=1000  ,						):
        __a     				=  parent
        __a     				=  batch_size
        __a     				=  num_channels
        __a     				=  image_size
        __a     				=  patch_size
        __a     				=  text_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     				=  coordinate_size
        __a     				=  shape_size
        __a     				=  num_labels
        __a     				=  num_choices
        __a     				=  scope
        __a     				=  range_bbox

        # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
        __a     				=  text_seq_length
        __a     				=  (image_size // patch_size) ** 2 + 1
        __a     				=  self.text_seq_length + self.image_seq_length


 def 		a__ (				self		):
        __a     				=  ids_tensor([self.batch_size, self.text_seq_length]  ,						self.vocab_size		)

        __a     				=  ids_tensor([self.batch_size, self.text_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     				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

        __a     				=  None
        if self.use_input_mask:
               __a     				=  random_attention_mask([self.batch_size, self.text_seq_length]		)

        __a     				=  None
        if self.use_token_type_ids:
               __a     				=  ids_tensor([self.batch_size, self.text_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.text_seq_length]  ,						self.num_labels		)

        __a     				=  LayoutLMvaConfig(
            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  ,						coordinate_size=self.coordinate_size  ,						shape_size=self.shape_size  ,						input_size=self.image_size  ,						patch_size=self.patch_size  ,						)

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  LayoutLMvaModel(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()

        # text + image
        __a     				=  model(lowerCamelCase  ,						pixel_values=lowerCamelCase		)
        __a     				=  model(
            lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						token_type_ids=lowerCamelCase		)
        __a     				=  model(lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase		)

        self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)		)

        # text only
        __a     				=  model(lowerCamelCase		)
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,						(self.batch_size, self.text_seq_length, self.hidden_size)		)

        # image only
        __a     				=  model(pixel_values=lowerCamelCase		)
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,						(self.batch_size, self.image_seq_length, self.hidden_size)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.num_labels
        __a     				=  LayoutLMvaForSequenceClassification(lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(
            lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						labels=lowerCamelCase  ,						)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  self.num_labels
        __a     				=  LayoutLMvaForTokenClassification(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(
            lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=lowerCamelCase  ,						attention_mask=lowerCamelCase  ,						token_type_ids=lowerCamelCase  ,						labels=lowerCamelCase  ,						)
        self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.text_seq_length, self.num_labels)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  LayoutLMvaForQuestionAnswering(config=lowerCamelCase		)
        model.to(lowerCamelCase		)
        model.eval()
        __a     				=  model(
            lowerCamelCase  ,						bbox=lowerCamelCase  ,						pixel_values=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 		a__ (				self		):
        __a     				=  self.prepare_config_and_inputs()
        (
            (
            __a 
        )				, (
            __a 
        )				, (
            __a 
        )				, (
            __a 
        )				, (
            __a 
        )				, (
            __a 
        )				, (
            __a 
        )				, (
            __a 
        )				,  
        )     				=  config_and_inputs
        __a     				=  {
            "input_ids": input_ids,
            "bbox": bbox,
            "pixel_values": pixel_values,
            "token_type_ids": token_type_ids,
            "attention_mask": input_mask,
        }
        return config, inputs_dict






@require_torch
class 				snake_case__     (   snake_case_,		snake_case_,		unittest.TestCase   ):
 _snake_case   :			str           =		False
 _snake_case   :			int           =		False
 _snake_case   :			int           =		False

 _snake_case   :			Any           =		(
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 _snake_case   :			Tuple           =		(
     {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
     if is_torch_available()
     else {}
 )


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
        # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
        # the sequence dimension of the text embedding only.
        # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
        return True


 def 		a__ (				self		):
        __a     				=  LayoutLMvaModelTester(self		)
        __a     				=  ConfigTester(self  ,						config_class=lowerCamelCase  ,						hidden_size=37		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase=False		):
        __a     				=  copy.deepcopy(lowerCamelCase		)
        if model_class in get_values(lowerCamelCase		):
               __a     				=  {
                   k: v.unsqueeze(1		).expand(-1  ,						self.model_tester.num_choices  ,						-1		).contiguous()
                   if isinstance(lowerCamelCase  ,						torch.Tensor		) and v.ndim > 1
                   else v
                   for k, v in inputs_dict.items()
               }
        if return_labels:
               if model_class in get_values(lowerCamelCase		):
                      __a     				=  torch.ones(self.model_tester.batch_size  ,						dtype=torch.long  ,						device=lowerCamelCase		)
               elif model_class in get_values(lowerCamelCase		):
                      __a     				=  torch.zeros(
                          self.model_tester.batch_size  ,						dtype=torch.long  ,						device=lowerCamelCase		)
                      __a     				=  torch.zeros(
                          self.model_tester.batch_size  ,						dtype=torch.long  ,						device=lowerCamelCase		)
               elif model_class in [
                   *get_values(lowerCamelCase		),
               ]:
                      __a     				=  torch.zeros(
                          self.model_tester.batch_size  ,						dtype=torch.long  ,						device=lowerCamelCase		)
               elif model_class in [
                   *get_values(lowerCamelCase		),
               ]:
                      __a     				=  torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.text_seq_length)  ,						dtype=torch.long  ,						device=lowerCamelCase  ,						)

        return inputs_dict


 def 		a__ (				self		):
        self.config_tester.run_common_tests()


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase		)


 def 		a__ (				self		):
        __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 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCamelCase		)






 @slow
 def 		a__ (				self		):
        for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a     				=  LayoutLMvaModel.from_pretrained(lowerCamelCase		)
               self.assertIsNotNone(lowerCamelCase		)



def 					_lowerCamelCase(					):
       __a     				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
       return image






@require_torch
class 				snake_case__     (   unittest.TestCase   ):


 @cached_property
 def 		a__ (				self		):
        return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase		) if is_vision_available() else None






 @slow
 def 		a__ (				self		):
        __a     				=  LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		).to(lowerCamelCase		)

        __a     				=  self.default_image_processor
        __a     				=  prepare_img()
        __a     				=  image_processor(images=lowerCamelCase  ,						return_tensors="pt"		).pixel_values.to(lowerCamelCase		)

        __a     				=  torch.tensor([[1, 2]]		)
        __a     				=  torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

        # forward pass
        __a     				=  model(
            input_ids=input_ids.to(lowerCamelCase		)  ,						bbox=bbox.to(lowerCamelCase		)  ,						pixel_values=pixel_values.to(lowerCamelCase		)  ,						)

        # verify the logits
        __a     				=  torch.Size((1, 199, 768)		)
        self.assertEqual(outputs.last_hidden_state.shape  ,						lowerCamelCase		)

        __a     				=  torch.tensor(
            [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]		).to(lowerCamelCase		)

        self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]  ,						lowerCamelCase  ,						atol=1E-4		)		)

"""simple docstring"""



from __future__ import annotations



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a ,  ):
       __a     				=  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 					_lowerCamelCase(					a							):
       __a     				=  []
       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)
