



import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.bert.modeling_flax_bert import (
     FlaxBertForMaskedLM,
     FlaxBertForMultipleChoice,
     FlaxBertForNextSentencePrediction,
     FlaxBertForPreTraining,
     FlaxBertForQuestionAnswering,
     FlaxBertForSequenceClassification,
     FlaxBertForTokenClassification,
     FlaxBertModel,
 )

class 					_A (			unittest.TestCase					):



       def __init__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str=13  ,	__SCREAMING_SNAKE_CASE							:  Tuple=7  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Dict=True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Tuple=True  ,	__SCREAMING_SNAKE_CASE							:  int=99  ,	__SCREAMING_SNAKE_CASE							:  Tuple=32  ,	__SCREAMING_SNAKE_CASE							:  int=5  ,	__SCREAMING_SNAKE_CASE							:  Dict=4  ,	__SCREAMING_SNAKE_CASE							:  Dict=37  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]="gelu"  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=0.1  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=0.1  ,	__SCREAMING_SNAKE_CASE							:  Dict=512  ,	__SCREAMING_SNAKE_CASE							:  Dict=16  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=2  ,	__SCREAMING_SNAKE_CASE							:  Dict=0.02  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=4  ,	):


           '''simple docstring'''

           __a		 =       parent
           __a		 =       batch_size
           __a		 =       seq_length
           __a		 =       is_training
           __a		 =       use_attention_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_choices



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size)

           __a		 =       None
           if self.use_attention_mask:
               __a		 =       random_attention_mask([self.batch_size, self.seq_length])

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

           __a		 =       BertConfig(
               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  ,	is_decoder=__SCREAMING_SNAKE_CASE  ,	initializer_range=self.initializer_range  ,	)

           return config, input_ids, token_type_ids, attention_mask



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       self.prepare_config_and_inputs()
           __a ,     __a ,     __a ,     __a		 =       config_and_inputs
           __a		 =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict





       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.prepare_config_and_inputs()
           __a ,     __a ,     __a ,     __a		 =       config_and_inputs

           __a		 =       True
           __a		 =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           __a		 =       ids_tensor([self.batch_size, self.seq_length]  ,	vocab_size=2)

           return (
               config,
               input_ids,
               attention_mask,
               encoder_hidden_states,
               encoder_attention_mask,
           )


@require_flax
class 					_A (			__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					str		      =		True

       UpperCamelCase__						:					Dict		      =		(
           (
               FlaxBertModel,
               FlaxBertForPreTraining,
               FlaxBertForMaskedLM,
               FlaxBertForMultipleChoice,
               FlaxBertForQuestionAnswering,
               FlaxBertForNextSentencePrediction,
               FlaxBertForSequenceClassification,
               FlaxBertForTokenClassification,
               FlaxBertForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       FlaxBertModelTester(self)





       @slow
       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       FlaxBertModel.from_pretrained('''bert-base-cased''')
           __a		 =       model(np.ones((1, 1)))
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE)










from __future__ import annotations

__snake_case		:Optional[Any]   	=  []





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





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    if row >= len(_UpperCAmelCase       ):
        solution.append(_UpperCAmelCase       )
        printboard(_UpperCAmelCase       )
        print()
        return True
    for i in range(len(_UpperCAmelCase       )       ):
        if is_safe(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
            __a		 =       1
            solve(_UpperCAmelCase     ,				row + 1       )
            __a		 =       0
    return False





def   __snake_case			(    _UpperCAmelCase       ):
    for i in range(len(_UpperCAmelCase       )       ):
        for j in range(len(_UpperCAmelCase       )       ):
            if board[i][j] == 1:
                print('''Q'''     ,				end=''' '''       )
            else:
                print('''.'''     ,				end=''' '''       )
        print()


# n=int(input("The no. of queens"))
__snake_case		:Optional[Any]   	=  8
__snake_case		:Tuple   	=  [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('''The total no. of solutions are :''', len(solution))





