






"""simple docstring"""

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
			import tensorflow as tf

if is_keras_nlp_available():
			from transformers.models.gpta import TFGPTaTokenizer


lowercase    			=       ['''gpt2''']
lowercase    			=       '''gpt2'''

if is_tf_available():

			class 						A_							(      tf.Module  ):







							def __init__(							self							:						str						,    __lowerCamelCase							:						List[Any]	)				->					Optional[Any]:
													super().__init__()
													__magic_name__            =     tokenizer
													__magic_name__            =     AutoConfig.from_pretrained(__lowerCamelCase	)
													__magic_name__            =     TFGPTaLMHeadModel.from_config(__lowerCamelCase	)







							@tf.function(input_signature=(tf.TensorSpec((None,)						,    tf.string						,    name="text"	),)	)
							def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						Any	)				->					List[Any]:
													__magic_name__            =     self.tokenizer(__lowerCamelCase	)
													__magic_name__            =     tokenized["input_ids"].to_tensor()

													__magic_name__            =     tf.cast(input_ids_dense > 0						,    tf.intaa	)
													# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

													__magic_name__            =     self.model(input_ids=__lowerCamelCase						,    attention_mask=__lowerCamelCase	)["logits"]

													return outputs

@require_tf
@require_keras_nlp
class 						A_							(      unittest.TestCase  ):







				def     _snake_case					(							self							:						Any	)				->					Dict:
										super().setUp()

										__magic_name__            =     [GPTaTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in (TOKENIZER_CHECKPOINTS)]
										__magic_name__            =     [TFGPTaTokenizer.from_pretrained(__lowerCamelCase	) for checkpoint in TOKENIZER_CHECKPOINTS]
										assert len(self.tokenizers	) == len(self.tf_tokenizers	)

										__magic_name__            =     [
										    "This is a straightforward English test sentence.",
										    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
										    "Now we're going to add some Chinese: 一 二 三 一二三",
										    "And some much more rare Chinese: 齉 堃 齉堃",
										    "Je vais aussi écrire en français pour tester les accents",
										    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
										]
										__magic_name__            =     list(zip(self.test_sentences						,    self.test_sentences[::-1]	)	)







				def     _snake_case					(							self							:						int	)				->					Dict:
										for tokenizer, tf_tokenizer in zip(self.tokenizers						,    self.tf_tokenizers	):
																for test_inputs in self.test_sentences:
																						__magic_name__            =     tokenizer([test_inputs]						,    return_tensors="tf"	)
																						__magic_name__            =     tf_tokenizer([test_inputs]	)

																						for key in python_outputs.keys():
																												# convert them to numpy to avoid messing with ragged tensors
																												__magic_name__            =     python_outputs[key].numpy()
																												__magic_name__            =     tf_outputs[key].numpy()

																												self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape	)	)
																												self.assertTrue(tf.reduce_all(tf.cast(__lowerCamelCase						,    tf.intaa	) == tf_outputs_values	)	)







				@slow
				def     _snake_case					(							self							:						List[Any]	)				->					int:
										for tf_tokenizer in self.tf_tokenizers:
																__magic_name__            =     tf.function(__lowerCamelCase	)
																for test_inputs in self.test_sentences:
																						__magic_name__            =     tf.constant(__lowerCamelCase	)
																						__magic_name__            =     compiled_tokenizer(__lowerCamelCase	)
																						__magic_name__            =     tf_tokenizer(__lowerCamelCase	)

																						for key in eager_outputs.keys():
																												self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]	)	)







				@slow
				def     _snake_case					(							self							:						List[Any]	)				->					Dict:
										for tf_tokenizer in self.tf_tokenizers:
																__magic_name__            =     ModelToSave(tokenizer=__lowerCamelCase	)
																__magic_name__            =     tf.convert_to_tensor([self.test_sentences[0]]	)
																__magic_name__            =     model.serving(__lowerCamelCase	)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																						__magic_name__            =     Path(__lowerCamelCase	) / "saved.model"
																						tf.saved_model.save(__lowerCamelCase						,    __lowerCamelCase						,    signatures={"serving_default": model.serving}	)
																						__magic_name__            =     tf.saved_model.load(__lowerCamelCase	)
																__magic_name__            =     loaded_model.signatures["serving_default"](__lowerCamelCase	)["output_0"]
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertTrue(tf.reduce_all(out == loaded_output	)	)







				@slow
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										for tf_tokenizer in self.tf_tokenizers:
																__magic_name__            =     tf.convert_to_tensor([self.test_sentences[0]]	)
																__magic_name__            =     tf_tokenizer(__lowerCamelCase	)  # Build model with some sample inputs

																__magic_name__            =     tf_tokenizer.get_config()
																__magic_name__            =     TFGPTaTokenizer.from_config(__lowerCamelCase	)
																__magic_name__            =     model_from_config(__lowerCamelCase	)

																for key in from_config_output.keys():
																						self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]	)	)







				@slow
				def     _snake_case					(							self							:						Any	)				->					Tuple:
										for tf_tokenizer in self.tf_tokenizers:
																# for the test to run
																__magic_name__            =     1_2_3_1_2_3

																for max_length in [3, 5, 1_0_2_4]:
																						__magic_name__            =     tf.convert_to_tensor([self.test_sentences[0]]	)
																						__magic_name__            =     tf_tokenizer(__lowerCamelCase						,    max_length=__lowerCamelCase	)

																						__magic_name__            =     out["input_ids"].numpy().shape[1]

																						assert out_length == max_length











"""simple docstring"""

from __future__ import annotations

lowercase    			=       []






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]] ,						__lowerCamelCase:int ,						__lowerCamelCase:int   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






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






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]]   ):

						'''simple docstring'''






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


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


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



