

"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 import (
        TFDebertaVaForMaskedLM,
        TFDebertaVaForQuestionAnswering,
        TFDebertaVaForSequenceClassification,
        TFDebertaVaForTokenClassification,
        TFDebertaVaModel,
    )






class 			__a					:





       """simple docstring"""


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





        """simple docstring"""

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size   )

        lowerCAmelCase__   :				int 			=				None
        if self.use_input_mask:
         lowerCAmelCase__   :				Optional[int] 			=				random_attention_mask([self.batch_size, self.seq_length]   )

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

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

        lowerCAmelCase__   :				Optional[Any] 			=				DebertaVaConfig(
            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			,					relative_attention=self.relative_attention			,					position_biased_input=self.position_biased_input			,					initializer_range=self.initializer_range			,					return_dict=snake_case			,					)

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				TFDebertaVaModel(config=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

        lowerCAmelCase__   :				str 			=				[input_ids, input_mask]
        lowerCAmelCase__   :				Optional[int] 			=				model(snake_case   )

        lowerCAmelCase__   :				Any 			=				model(snake_case   )

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






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				TFDebertaVaForMaskedLM(config=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        lowerCAmelCase__   :				int 			=				model(snake_case   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length, self.vocab_size)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.num_labels
        lowerCAmelCase__   :				int 			=				TFDebertaVaForSequenceClassification(config=snake_case   )
        lowerCAmelCase__   :				Tuple 			=				{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        lowerCAmelCase__   :				Optional[int] 			=				model(snake_case   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.num_labels
        lowerCAmelCase__   :				int 			=				TFDebertaVaForTokenClassification(config=snake_case   )
        lowerCAmelCase__   :				Any 			=				{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        lowerCAmelCase__   :				Union[str, Any] 			=				model(snake_case   )
        self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length, self.num_labels)   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Any 			=				TFDebertaVaForQuestionAnswering(config=snake_case   )
        lowerCAmelCase__   :				str 			=				{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        lowerCAmelCase__   :				str 			=				model(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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						
        )   :				List[str] 			=				config_and_inputs
        lowerCAmelCase__   :				Tuple 			=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict






@require_tf
class 			__a					(						__magic_name__       ,	__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Any    =						(
           (
               TFDebertaVaModel,
               TFDebertaVaForMaskedLM,
               TFDebertaVaForQuestionAnswering,
               TFDebertaVaForSequenceClassification,
               TFDebertaVaForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       __UpperCamelCase					:      Tuple    =						(
           {
               'feature-extraction': TFDebertaVaModel,
               'fill-mask': TFDebertaVaForMaskedLM,
               'question-answering': TFDebertaVaForQuestionAnswering,
               'text-classification': TFDebertaVaForSequenceClassification,
               'token-classification': TFDebertaVaForTokenClassification,
               'zero-shot': TFDebertaVaForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )

       __UpperCamelCase					:      Tuple    =						False
       __UpperCamelCase					:      Union[str, Any]    =						False






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				TFDebertaVaModelTester(self   )
        lowerCAmelCase__   :				Tuple 			=				ConfigTester(self			,					config_class=snake_case			,					hidden_size=37   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        self.config_tester.run_common_tests()






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*snake_case   )






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
        self.assertIsNotNone(snake_case   )






@require_tf
class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       @unittest.skip(reason="Model not available yet"   )
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        pass






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"   )
        lowerCAmelCase__   :				Any 			=				tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   )
        lowerCAmelCase__   :				Tuple 			=				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
        lowerCAmelCase__   :				Union[str, Any] 			=				model(snake_case			,					attention_mask=snake_case   )[0]

        lowerCAmelCase__   :				Tuple 			=				tf.constant(
            [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]   )
        tf.debugging.assert_near(output[:, 1:4, 1:4]			,					snake_case			,					atol=1e-4   )









"""simple docstring"""

from __future__ import annotations

_UpperCamelCase		     =    []



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						bool:
 for i in range(len(lowercase__   )   ):
  if board[row][i] == 1:
   return False
 for i in range(len(lowercase__   )   ):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					-1					,					-1   )   ):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(lowercase__					,					-1					,					-1   )					,					range(lowercase__					,					len(lowercase__   )   )   ):
  if board[i][j] == 1:
   return False
 return True



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						bool:
 if row >= len(lowercase__   ):
  solution.append(lowercase__   )
  printboard(lowercase__   )
  print()
  return True
 for i in range(len(lowercase__   )   ):
  if is_safe(lowercase__					,					lowercase__					,					lowercase__   ):
   lowerCAmelCase__   :				List[Any] 			=				1
   solve(lowercase__					,					row + 1   )
   lowerCAmelCase__   :				Dict 			=				0
 return False



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						None:
 for i in range(len(lowercase__   )   ):
  for j in range(len(lowercase__   )   ):
   if board[i][j] == 1:
    print("Q"					,					end=" "   )
   else:
    print("."					,					end=" "   )
  print()


# n=int(input("The no. of queens"))
_UpperCamelCase		     =    8
_UpperCamelCase		     =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))






