

"""simple docstring"""

import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
    from test_module.custom_tokenization_fast import CustomTokenizerFast






class 			__a					(						unittest.TestCase       ):





       """simple docstring"""


       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				0






       @slow
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
         lowerCAmelCase__   :				List[str] 			=				AutoTokenizer.from_pretrained(snake_case   )
         self.assertIsNotNone(snake_case   )
         self.assertIsInstance(snake_case			,					(BertTokenizer, BertTokenizerFast)   )
         self.assertGreater(len(snake_case   )			,					0   )

        for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
         lowerCAmelCase__   :				Dict 			=				AutoTokenizer.from_pretrained(snake_case   )
         self.assertIsNotNone(snake_case   )
         self.assertIsInstance(snake_case			,					(GPTaTokenizer, GPTaTokenizerFast)   )
         self.assertGreater(len(snake_case   )			,					0   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				AutoTokenizer.from_pretrained(snake_case   )
        self.assertIsInstance(snake_case			,					(BertTokenizer, BertTokenizerFast)   )
        self.assertEqual(tokenizer.vocab_size			,					12   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				AutoTokenizer.from_pretrained(snake_case   )
        self.assertIsInstance(snake_case			,					(RobertaTokenizer, RobertaTokenizerFast)   )
        self.assertEqual(tokenizer.vocab_size			,					20   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				AutoConfig.from_pretrained(snake_case   )
        self.assertIsInstance(snake_case			,					snake_case   )
        # Check that tokenizer_type ≠ model_type
        lowerCAmelCase__   :				Optional[int] 			=				AutoTokenizer.from_pretrained(snake_case			,					config=snake_case   )
        self.assertIsInstance(snake_case			,					(BertTokenizer, BertTokenizerFast)   )
        self.assertEqual(tokenizer.vocab_size			,					12   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with tempfile.TemporaryDirectory() as tmp_dir:
         shutil.copy("./tests/fixtures/vocab.txt"			,					os.path.join(snake_case			,					"vocab.txt"   )   )

         lowerCAmelCase__   :				Union[str, Any] 			=				AutoTokenizer.from_pretrained(snake_case			,					tokenizer_type="bert"			,					use_fast=snake_case   )
         self.assertIsInstance(snake_case			,					snake_case   )

        with tempfile.TemporaryDirectory() as tmp_dir:
         shutil.copy("./tests/fixtures/vocab.json"			,					os.path.join(snake_case			,					"vocab.json"   )   )
         shutil.copy("./tests/fixtures/merges.txt"			,					os.path.join(snake_case			,					"merges.txt"   )   )

         lowerCAmelCase__   :				Tuple 			=				AutoTokenizer.from_pretrained(snake_case			,					tokenizer_type="gpt2"			,					use_fast=snake_case   )
         self.assertIsInstance(snake_case			,					snake_case   )






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with tempfile.TemporaryDirectory() as tmp_dir:
         shutil.copy("./tests/fixtures/vocab.txt"			,					os.path.join(snake_case			,					"vocab.txt"   )   )

         lowerCAmelCase__   :				Optional[Any] 			=				AutoTokenizer.from_pretrained(snake_case			,					tokenizer_type="bert"   )
         self.assertIsInstance(snake_case			,					snake_case   )

        with tempfile.TemporaryDirectory() as tmp_dir:
         shutil.copy("./tests/fixtures/vocab.json"			,					os.path.join(snake_case			,					"vocab.json"   )   )
         shutil.copy("./tests/fixtures/merges.txt"			,					os.path.join(snake_case			,					"merges.txt"   )   )

         lowerCAmelCase__   :				int 			=				AutoTokenizer.from_pretrained(snake_case			,					tokenizer_type="gpt2"   )
         self.assertIsInstance(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with pytest.raises(snake_case   ):
         AutoTokenizer.from_pretrained("./"			,					tokenizer_type="xxx"   )






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
         lowerCAmelCase__   :				List[Any] 			=				tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased"   )
         self.assertIsInstance(snake_case			,					(BertTokenizer, BertTokenizerFast)   )

         if isinstance(snake_case			,					snake_case   ):
          self.assertEqual(tokenizer.basic_tokenizer.do_lower_case			,					snake_case   )
         else:
          self.assertEqual(tokenizer.do_lower_case			,					snake_case   )

         self.assertEqual(tokenizer.model_max_length			,					512   )






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
         with self.assertRaisesRegex(
             snake_case			,					"julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"			,					):
          lowerCAmelCase__   :				Optional[Any] 			=				tokenizer_class.from_pretrained("julien-c/herlolip-not-exists"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				TOKENIZER_MAPPING.values()
        lowerCAmelCase__   :				int 			=				[]

        for slow_tok, fast_tok in tokenizers:
         if slow_tok is not None:
          tokenizer_names.append(slow_tok.__name__   )

         if fast_tok is not None:
          tokenizer_names.append(fast_tok.__name__   )

        for tokenizer_name in tokenizer_names:
         # must find the right class
         tokenizer_class_from_name(snake_case   )






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"			,					use_fast=snake_case   )			,					snake_case   )
        self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"   )			,					snake_case   )






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Dict 			=				AutoTokenizer.from_pretrained("distilbert-base-uncased"			,					do_lower_case=snake_case   )
        lowerCAmelCase__   :				Any 			=				"Hello, world. How are you?"
        lowerCAmelCase__   :				List[str] 			=				tokenizer.tokenize(snake_case   )
        self.assertEqual("[UNK]"			,					tokens[0]   )

        lowerCAmelCase__   :				Dict 			=				AutoTokenizer.from_pretrained("microsoft/mpnet-base"			,					do_lower_case=snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				tokenizer.tokenize(snake_case   )
        self.assertEqual("[UNK]"			,					tokens[0]   )






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config"   )
        self.assertEqual(type(snake_case   )			,					snake_case   )
        self.assertEqual(tokenizer.model_max_length			,					512   )
        self.assertEqual(tokenizer.vocab_size			,					30_000   )
        self.assertEqual(tokenizer.unk_token			,					"[UNK]"   )
        self.assertEqual(tokenizer.padding_side			,					"right"   )
        self.assertEqual(tokenizer.truncation_side			,					"right"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				AutoTokenizer.from_pretrained(snake_case   )
        self.assertIsInstance(snake_case			,					(BertTokenizer, BertTokenizerFast)   )
        with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer.save_pretrained(snake_case   )
         lowerCAmelCase__   :				Optional[int] 			=				AutoTokenizer.from_pretrained(snake_case   )

        self.assertIsInstance(snake_case			,					tokenizer.__class__   )
        self.assertEqual(tokenizera.vocab_size			,					12   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				AutoTokenizer.from_pretrained("ctrl"   )
        # There is no fast CTRL so this always gives us a slow tokenizer.
        self.assertIsInstance(snake_case			,					snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				get_tokenizer_config("bert-base-cased"   )
        lowerCAmelCase__   :				int 			=				config.pop("_commit_hash"			,					snake_case   )
        # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
        self.assertEqual(snake_case			,					{"do_lower_case": False}   )

        # This model does not have a tokenizer_config so we get back an empty dict.
        lowerCAmelCase__   :				Union[str, Any] 			=				get_tokenizer_config(snake_case   )
        self.assertDictEqual(snake_case			,					{}   )

        # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
        lowerCAmelCase__   :				int 			=				AutoTokenizer.from_pretrained(snake_case   )
        with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer.save_pretrained(snake_case   )
         lowerCAmelCase__   :				Optional[Any] 			=				get_tokenizer_config(snake_case   )

        # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
        self.assertEqual(config["tokenizer_class"]			,					"BertTokenizer"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        try:
         AutoConfig.register("custom"			,					snake_case   )

         AutoTokenizer.register(snake_case			,					slow_tokenizer_class=snake_case   )
         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(snake_case   ):
          AutoTokenizer.register(snake_case			,					slow_tokenizer_class=snake_case   )

         lowerCAmelCase__   :				Dict 			=				CustomTokenizer.from_pretrained(snake_case   )
         with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer.save_pretrained(snake_case   )

          lowerCAmelCase__   :				List[str] 			=				AutoTokenizer.from_pretrained(snake_case   )
          self.assertIsInstance(snake_case			,					snake_case   )

        finally:
         if "custom" in CONFIG_MAPPING._extra_content:
          del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in TOKENIZER_MAPPING._extra_content:
          del TOKENIZER_MAPPING._extra_content[CustomConfig]






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        try:
         AutoConfig.register("custom"			,					snake_case   )

         # Can register in two steps
         AutoTokenizer.register(snake_case			,					slow_tokenizer_class=snake_case   )
         self.assertEqual(TOKENIZER_MAPPING[CustomConfig]			,					(CustomTokenizer, None)   )
         AutoTokenizer.register(snake_case			,					fast_tokenizer_class=snake_case   )
         self.assertEqual(TOKENIZER_MAPPING[CustomConfig]			,					(CustomTokenizer, CustomTokenizerFast)   )

         del TOKENIZER_MAPPING._extra_content[CustomConfig]
         # Can register in one step
         AutoTokenizer.register(
             snake_case			,					slow_tokenizer_class=snake_case			,					fast_tokenizer_class=snake_case   )
         self.assertEqual(TOKENIZER_MAPPING[CustomConfig]			,					(CustomTokenizer, CustomTokenizerFast)   )

         # Trying to register something existing in the Transformers library will raise an error
         with self.assertRaises(snake_case   ):
          AutoTokenizer.register(snake_case			,					fast_tokenizer_class=snake_case   )

         # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
         # and that model does not have a tokenizer.json
         with tempfile.TemporaryDirectory() as tmp_dir:
          lowerCAmelCase__   :				Dict 			=				BertTokenizerFast.from_pretrained(snake_case   )
          bert_tokenizer.save_pretrained(snake_case   )
          lowerCAmelCase__   :				Union[str, Any] 			=				CustomTokenizerFast.from_pretrained(snake_case   )

         with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer.save_pretrained(snake_case   )

          lowerCAmelCase__   :				List[Any] 			=				AutoTokenizer.from_pretrained(snake_case   )
          self.assertIsInstance(snake_case			,					snake_case   )

          lowerCAmelCase__   :				Any 			=				AutoTokenizer.from_pretrained(snake_case			,					use_fast=snake_case   )
          self.assertIsInstance(snake_case			,					snake_case   )

        finally:
         if "custom" in CONFIG_MAPPING._extra_content:
          del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in TOKENIZER_MAPPING._extra_content:
          del TOKENIZER_MAPPING._extra_content[CustomConfig]






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with self.assertRaises(snake_case   ):
         lowerCAmelCase__   :				Dict 			=				AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"   )
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(snake_case   ):
         lowerCAmelCase__   :				List[Any] 			=				AutoTokenizer.from_pretrained(
             "hf-internal-testing/test_dynamic_tokenizer"			,					trust_remote_code=snake_case   )

        lowerCAmelCase__   :				Any 			=				AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"			,					trust_remote_code=snake_case   )
        self.assertTrue(tokenizer.special_attribute_present   )
        # Test tokenizer can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer.save_pretrained(snake_case   )
         lowerCAmelCase__   :				Tuple 			=				AutoTokenizer.from_pretrained(snake_case			,					trust_remote_code=snake_case   )
        self.assertTrue(reloaded_tokenizer.special_attribute_present   )

        if is_tokenizers_available():
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizerFast"   )
         self.assertEqual(reloaded_tokenizer.__class__.__name__			,					"NewTokenizerFast"   )

         # Test we can also load the slow version
         lowerCAmelCase__   :				str 			=				AutoTokenizer.from_pretrained(
             "hf-internal-testing/test_dynamic_tokenizer"			,					trust_remote_code=snake_case			,					use_fast=snake_case   )
         self.assertTrue(tokenizer.special_attribute_present   )
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizer"   )
         # Test tokenizer can be reloaded.
         with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer.save_pretrained(snake_case   )
          lowerCAmelCase__   :				List[str] 			=				AutoTokenizer.from_pretrained(snake_case			,					trust_remote_code=snake_case			,					use_fast=snake_case   )
         self.assertEqual(reloaded_tokenizer.__class__.__name__			,					"NewTokenizer"   )
         self.assertTrue(reloaded_tokenizer.special_attribute_present   )
        else:
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizer"   )
         self.assertEqual(reloaded_tokenizer.__class__.__name__			,					"NewTokenizer"   )






       @require_tokenizers
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        class 			__a					(						__magic_name__       ):





               """simple docstring"""


               __UpperCamelCase					:      Tuple    =						False






        class 			__a					(						__magic_name__       ):





               """simple docstring"""


               __UpperCamelCase					:      Optional[int]    =						NewTokenizer
               __UpperCamelCase					:      Union[str, Any]    =						False

        try:
         AutoConfig.register("custom"			,					snake_case   )
         AutoTokenizer.register(snake_case			,					slow_tokenizer_class=snake_case   )
         AutoTokenizer.register(snake_case			,					fast_tokenizer_class=snake_case   )
         # If remote code is not set, the default is to use local
         lowerCAmelCase__   :				List[Any] 			=				AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"   )
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizerFast"   )
         self.assertFalse(tokenizer.special_attribute_present   )
         lowerCAmelCase__   :				Optional[int] 			=				AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"			,					use_fast=snake_case   )
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizer"   )
         self.assertFalse(tokenizer.special_attribute_present   )

         # If remote code is disabled, we load the local one.
         lowerCAmelCase__   :				List[Any] 			=				AutoTokenizer.from_pretrained(
             "hf-internal-testing/test_dynamic_tokenizer"			,					trust_remote_code=snake_case   )
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizerFast"   )
         self.assertFalse(tokenizer.special_attribute_present   )
         lowerCAmelCase__   :				Union[str, Any] 			=				AutoTokenizer.from_pretrained(
             "hf-internal-testing/test_dynamic_tokenizer"			,					trust_remote_code=snake_case			,					use_fast=snake_case   )
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizer"   )
         self.assertFalse(tokenizer.special_attribute_present   )

         # If remote is enabled, we load from the Hub
         lowerCAmelCase__   :				str 			=				AutoTokenizer.from_pretrained(
             "hf-internal-testing/test_dynamic_tokenizer"			,					trust_remote_code=snake_case   )
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizerFast"   )
         self.assertTrue(tokenizer.special_attribute_present   )
         lowerCAmelCase__   :				Tuple 			=				AutoTokenizer.from_pretrained(
             "hf-internal-testing/test_dynamic_tokenizer"			,					trust_remote_code=snake_case			,					use_fast=snake_case   )
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizer"   )
         self.assertTrue(tokenizer.special_attribute_present   )

        finally:
         if "custom" in CONFIG_MAPPING._extra_content:
          del CONFIG_MAPPING._extra_content["custom"]
         if CustomConfig in TOKENIZER_MAPPING._extra_content:
          del TOKENIZER_MAPPING._extra_content[CustomConfig]






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				AutoTokenizer.from_pretrained(
            "hf-internal-testing/test_dynamic_tokenizer_legacy"			,					trust_remote_code=snake_case   )
        self.assertTrue(tokenizer.special_attribute_present   )
        if is_tokenizers_available():
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizerFast"   )

         # Test we can also load the slow version
         lowerCAmelCase__   :				List[Any] 			=				AutoTokenizer.from_pretrained(
             "hf-internal-testing/test_dynamic_tokenizer_legacy"			,					trust_remote_code=snake_case			,					use_fast=snake_case   )
         self.assertTrue(tokenizer.special_attribute_present   )
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizer"   )
        else:
         self.assertEqual(tokenizer.__class__.__name__			,					"NewTokenizer"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with self.assertRaisesRegex(
            snake_case			,					"bert-base is not a local folder and is not a valid model identifier"   ):
         lowerCAmelCase__   :				List[str] 			=				AutoTokenizer.from_pretrained("bert-base"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        with self.assertRaisesRegex(
            snake_case			,					R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"   ):
         lowerCAmelCase__   :				Tuple 			=				AutoTokenizer.from_pretrained(snake_case			,					revision="aaaaaa"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"   )
        with RequestCounter() as counter:
         lowerCAmelCase__   :				Dict 			=				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"   )
         self.assertEqual(counter.get_request_count			,					0   )
         self.assertEqual(counter.head_request_count			,					1   )
         self.assertEqual(counter.other_request_count			,					0   )









"""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))






