"""simple docstring"""







import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
       import torch

       from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=7     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=99     ,__UpperCamelCase=32     ,__UpperCamelCase=5     ,__UpperCamelCase=4     ,__UpperCamelCase=4     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.0     ,__UpperCamelCase=0.1     ,__UpperCamelCase=True     ,__UpperCamelCase=512     ,__UpperCamelCase=16     ,__UpperCamelCase=2     ,__UpperCamelCase=0.02     ,__UpperCamelCase=3     ,__UpperCamelCase=4     ,__UpperCamelCase=None     ,)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	parent
        lowercase_	:				Any													=	batch_size
        lowercase_	:				Union[str, Any]													=	seq_length
        lowercase_	:				Dict													=	is_training
        lowercase_	:				Optional[int]													=	use_input_mask
        lowercase_	:				str													=	use_token_type_ids
        lowercase_	:				str													=	use_labels
        lowercase_	:				List[str]													=	vocab_size
        lowercase_	:				str													=	hidden_size
        lowercase_	:				str													=	num_hidden_layers
        lowercase_	:				str													=	num_attention_heads
        lowercase_	:				Optional[Any]													=	intermediate_multiple_size
        lowercase_	:				Tuple													=	hidden_act
        lowercase_	:				Tuple													=	hidden_dropout
        lowercase_	:				Dict													=	attention_dropout
        lowercase_	:				Any													=	weight_tying
        lowercase_	:				Optional[int]													=	max_position_embeddings
        lowercase_	:				Tuple													=	type_vocab_size
        lowercase_	:				Union[str, Any]													=	type_sequence_label_size
        lowercase_	:				Union[str, Any]													=	initializer_range
        lowercase_	:				Dict													=	num_labels
        lowercase_	:				Optional[int]													=	num_choices
        lowercase_	:				List[Any]													=	scope
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Dict													=	ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size			)

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

        lowercase_	:				Optional[int]													=	None
        if self.use_labels:
             lowercase_	:				Union[str, Any]													=	ids_tensor([self.batch_size, self.seq_length]     ,self.num_labels			)

        lowercase_	:				Dict													=	self.get_config()

        return config, input_ids, input_mask, token_labels
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        return GPTNeoXJapaneseConfig(
            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_multiple_size=self.intermediate_multiple_size     ,hidden_act=self.hidden_act     ,hidden_dropout=self.hidden_dropout     ,attention_dropout=self.attention_dropout     ,weight_tying=self.weight_tying     ,max_position_embeddings=self.max_position_embeddings     ,type_vocab_size=self.type_vocab_size     ,is_decoder=__UpperCamelCase     ,initializer_range=self.initializer_range     ,)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	self.prepare_config_and_inputs()

        lowercase_	:				Any													=	True

        return config, input_ids, input_mask, token_labels
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        lowercase_	:				Dict													=	GPTNeoXJapaneseModel(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Dict													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase			)
        lowercase_	:				str													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				int													=	True
        lowercase_	:				Any													=	GPTNeoXJapaneseModel(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Tuple													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase			)
        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Any													=	GPTNeoXJapaneseForCausalLM(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Optional[int]													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Any													=	True
        lowercase_	:				Dict													=	GPTNeoXJapaneseForCausalLM(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()

        # first forward pass
        lowercase_	:				str													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase     ,use_cache=__UpperCamelCase			)
        lowercase_	:				List[Any]													=	outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        lowercase_	:				Tuple													=	ids_tensor((self.batch_size, 3)     ,config.vocab_size			)
        lowercase_	:				Union[str, Any]													=	ids_tensor((self.batch_size, 3)     ,vocab_size=2			)

        # append to next input_ids and
        lowercase_	:				Optional[int]													=	torch.cat([input_ids, next_tokens]     ,dim=-1			)
        lowercase_	:				List[str]													=	torch.cat([input_mask, next_mask]     ,dim=-1			)

        lowercase_	:				List[Any]													=	model(__UpperCamelCase     ,attention_mask=__UpperCamelCase     ,output_hidden_states=__UpperCamelCase			)
        lowercase_	:				str													=	output_from_no_past['hidden_states'][0]
        lowercase_	:				Optional[Any]													=	model(
            __UpperCamelCase     ,attention_mask=__UpperCamelCase     ,past_key_values=__UpperCamelCase     ,output_hidden_states=__UpperCamelCase     ,)['hidden_states'][0]

        # select random slice
        lowercase_	:				Tuple													=	ids_tensor((1,)     ,output_from_past.shape[-1]			).item()
        lowercase_	:				List[Any]													=	output_from_no_past[:, -3:, random_slice_idx].detach()
        lowercase_	:				Optional[Any]													=	output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]			)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase     ,__UpperCamelCase     ,atol=1e-3			)			)







   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.prepare_config_and_inputs()
        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Tuple													=	config_and_inputs
        lowercase_	:				Dict													=	{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_torch
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      unittest.TestCase  ):
   lowercase							=  (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
   lowercase							=  (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
   lowercase							=  (
       {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
       if is_torch_available()
       else {}
   )
   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	GPTNeoXJapaneseModelTester(self			)
        lowercase_	:				List[Any]													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Optional[Any]													=	self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				int													=	self.model_tester.prepare_config_and_inputs_for_decoder()

        lowercase_	:				List[str]													=	None

        self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_							,  lowercase_							,  lowercase_							,  lowercase_	:				Dict													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*__UpperCamelCase			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Dict													=	'abeja/gpt-neox-japanese-2.7b'

        lowercase_	:				str													=	['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']

        lowercase_	:				int													=	[
            'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
            '100年後に必要とされる会社は、「人」が中心の会社です。',
            'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
            '国境の長いトンネルを抜けると、そこは雪国だった。',
            '美味しい日本食といえば、やっぱりお寿司ですよね。',
        ]

        lowercase_	:				Dict													=	GPTNeoXJapaneseTokenizer.from_pretrained(__UpperCamelCase			)
        lowercase_	:				Dict													=	GPTNeoXJapaneseForCausalLM.from_pretrained(__UpperCamelCase			)

        lowercase_	:				str													=	[]
        for prompt in prompts:
             lowercase_	:				Tuple													=	tokenizer(__UpperCamelCase     ,return_tensors='pt'			).input_ids
             lowercase_	:				Optional[int]													=	model.generate(__UpperCamelCase     ,max_length=50			)
             lowercase_	:				Union[str, Any]													=	tokenizer.batch_decode(__UpperCamelCase     ,skip_special_tokens=__UpperCamelCase			)
             predicted_outputs += generated_string
        self.assertListEqual(__UpperCamelCase     ,__UpperCamelCase			)


"""simple docstring"""







from __future__ import annotations

__SCREAMING_SNAKE_CASE    	=[]



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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     if row >= len(__SCREAMING_SNAKE_CASE      ):
          solution.append(__SCREAMING_SNAKE_CASE      )
          printboard(__SCREAMING_SNAKE_CASE      )
          print()
          return True
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          if is_safe(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ):
               lowercase_	:				int													=	1
               solve(__SCREAMING_SNAKE_CASE     ,			row + 1      )
               lowercase_	:				Dict													=	0
     return False



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list[list[int]]      ):
     for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
          for j in range(len(__SCREAMING_SNAKE_CASE      )      ):
               if board[i][j] == 1:
                    print('Q'     ,			end=' '      )
               else:
                    print('.'     ,			end=' '      )
          print()


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







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

