


'''simple docstring'''

import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class     __lowerCAmelCase		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Dict       =				MvpTokenizer
      lowerCAmelCase__   :	Any       =				MvpTokenizerFast
      lowerCAmelCase__   :	str       =				True
      lowerCAmelCase__   :	Optional[int]       =				filter_roberta_detectors



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             super().setUp()
             lowercase__      =					[
                 '''l''',
                 '''o''',
                 '''w''',
                 '''e''',
                 '''r''',
                 '''s''',
                 '''t''',
                 '''i''',
                 '''d''',
                 '''n''',
                 '''\u0120''',
                 '''\u0120l''',
                 '''\u0120n''',
                 '''\u0120lo''',
                 '''\u0120low''',
                 '''er''',
                 '''\u0120lowest''',
                 '''\u0120newer''',
                 '''\u0120wider''',
                 '''<unk>''',
             ]
             lowercase__      =					dict(zip(UpperCamelCase  ,		range(len(UpperCamelCase					)					)					)					)
             lowercase__      =					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
             lowercase__      =					{'''unk_token''': '''<unk>'''}

             lowercase__      =					os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES['''vocab_file''']					)
             lowercase__      =					os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES['''merges_file''']					)
             with open(self.vocab_file  ,		'''w'''  ,		encoding='''utf-8'''					) as fp:
                    fp.write(json.dumps(UpperCamelCase					) + '''\n'''					)
             with open(self.merges_file  ,		'''w'''  ,		encoding='''utf-8'''					) as fp:
                    fp.write('''\n'''.join(UpperCamelCase					)					)



      def  UpperCamelCase__						(self       :       Tuple  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             kwargs.update(self.special_tokens_map					)
             return self.tokenizer_class.from_pretrained(self.tmpdirname  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             kwargs.update(self.special_tokens_map					)
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







             return "lower newer", "lower newer"



      @cached_property
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''					)



      @cached_property
      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''					)



      @require_torch
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             lowercase__      =					[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    lowercase__      =					tokenizer(UpperCamelCase  ,		max_length=len(UpperCamelCase					)  ,		padding=UpperCamelCase  ,		return_tensors='''pt'''					)
                    self.assertIsInstance(UpperCamelCase  ,		UpperCamelCase					)

                    self.assertEqual((2, 9)  ,		batch.input_ids.shape					)
                    self.assertEqual((2, 9)  ,		batch.attention_mask.shape					)
                    lowercase__      =					batch.input_ids.tolist()[0]
                    self.assertListEqual(UpperCamelCase  ,		UpperCamelCase					)
                    # Test that special tokens are reset



      @require_torch
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    lowercase__      =					tokenizer(UpperCamelCase  ,		padding=UpperCamelCase  ,		return_tensors='''pt'''					)
                    # check if input_ids are returned and no labels
                    self.assertIn('''input_ids'''  ,		UpperCamelCase					)
                    self.assertIn('''attention_mask'''  ,		UpperCamelCase					)
                    self.assertNotIn('''labels'''  ,		UpperCamelCase					)
                    self.assertNotIn('''decoder_attention_mask'''  ,		UpperCamelCase					)



      @require_torch
      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					[
                 '''Summary of the text.''',
                 '''Another summary.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    lowercase__      =					tokenizer(text_target=UpperCamelCase  ,		max_length=32  ,		padding='''max_length'''  ,		return_tensors='''pt'''					)
                    self.assertEqual(32  ,		targets['''input_ids'''].shape[1]					)



      @require_torch
      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    lowercase__      =					tokenizer(
                        ['''I am a small frog''' * 1024, '''I am a small frog''']  ,		padding=UpperCamelCase  ,		truncation=UpperCamelCase  ,		return_tensors='''pt'''					)
                    self.assertIsInstance(UpperCamelCase  ,		UpperCamelCase					)
                    self.assertEqual(batch.input_ids.shape  ,		(2, 1024)					)



      @require_torch
      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					['''A long paragraph for summarization.''']
             lowercase__      =					[
                 '''Summary of the text.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    lowercase__      =					tokenizer(UpperCamelCase  ,		text_target=UpperCamelCase  ,		return_tensors='''pt'''					)
                    lowercase__      =					inputs['''input_ids''']
                    lowercase__      =					inputs['''labels''']
                    self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
                    self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
                    self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
                    self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             pass






      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
                           lowercase__      =					self.rust_tokenizer_class.from_pretrained(UpperCamelCase  ,		**UpperCamelCase					)
                           lowercase__      =					self.tokenizer_class.from_pretrained(UpperCamelCase  ,		**UpperCamelCase					)
                           lowercase__      =					'''A, <mask> AllenNLP sentence.'''
                           lowercase__      =					tokenizer_r.encode_plus(UpperCamelCase  ,		add_special_tokens=UpperCamelCase  ,		return_token_type_ids=UpperCamelCase					)
                           lowercase__      =					tokenizer_p.encode_plus(UpperCamelCase  ,		add_special_tokens=UpperCamelCase  ,		return_token_type_ids=UpperCamelCase					)

                           # token_type_ids should put 0 everywhere
                           self.assertEqual(sum(tokens_r['''token_type_ids''']					)  ,		sum(tokens_p['''token_type_ids''']					)					)

                           # attention_mask should put 1 everywhere, so sum over length should be 1
                           self.assertEqual(
                               sum(tokens_r['''attention_mask''']					) / len(tokens_r['''attention_mask''']					)  ,		sum(tokens_p['''attention_mask''']					) / len(tokens_p['''attention_mask''']					)  ,		)

                           lowercase__      =					tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
                           lowercase__      =					tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)

                           # Rust correctly handles the space before the mask while python doesnt
                           self.assertSequenceEqual(tokens_p['''input_ids''']  ,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)
                           self.assertSequenceEqual(tokens_r['''input_ids''']  ,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]					)

                           self.assertSequenceEqual(
                               UpperCamelCase  ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)
                           self.assertSequenceEqual(
                               UpperCamelCase  ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)








'''simple docstring'''

from __future__ import annotations

lowerCamelCase				:  List[str]    		=      []

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			bool:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			bool:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE (A			)			->			None:






       """simple docstring"""







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


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

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




