


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  MBartConfig
							__UpperCAmelCase   :							Optional[int]					  =  {}
							__UpperCAmelCase   :							Union[str, Any]					  =  "gelu"

							def __init__(							self	:  List[str]   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Union[str, Any]=13   ,     lowerCamelCase	:  List[Any]=7   ,     lowerCamelCase	:  Dict=True   ,     lowerCamelCase	:  List[Any]=False   ,     lowerCamelCase	:  List[Any]=99   ,     lowerCamelCase	:  Optional[Any]=32   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  List[str]=4   ,     lowerCamelCase	:  Optional[int]=37   ,     lowerCamelCase	:  Optional[int]=0.1   ,     lowerCamelCase	:  Optional[int]=0.1   ,     lowerCamelCase	:  Optional[Any]=20   ,     lowerCamelCase	:  List[Any]=2   ,     lowerCamelCase	:  Optional[Any]=1   ,     lowerCamelCase	:  int=0   ,     )     ->   Union[str, Any]:
														__snake_case		:      str										=       parent
														__snake_case		:      List[Any]										=       batch_size
														__snake_case		:      Optional[Any]										=       seq_length
														__snake_case		:      List[Any]										=       is_training
														__snake_case		:      List[Any]										=       use_labels
														__snake_case		:      Any										=       vocab_size
														__snake_case		:      Optional[Any]										=       hidden_size
														__snake_case		:      Optional[int]										=       num_hidden_layers
														__snake_case		:      Optional[int]										=       num_attention_heads
														__snake_case		:      List[Any]										=       intermediate_size
														__snake_case		:      int										=       hidden_dropout_prob
														__snake_case		:      str										=       attention_probs_dropout_prob
														__snake_case		:      Tuple										=       max_position_embeddings
														__snake_case		:      Any										=       eos_token_id
														__snake_case		:      Dict										=       pad_token_id
														__snake_case		:      Union[str, Any]										=       bos_token_id

							def    __snake_case (							self	:  Any    )     ->   List[str]:
														__snake_case		:      int										=       ids_tensor([self.batch_size, self.seq_length - 1]   ,     self.vocab_size    )
														__snake_case		:      Dict										=       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )   ,     1    )
														__snake_case		:      Any										=       tf.concat([input_ids, eos_tensor]   ,     axis=1    )

														__snake_case		:      Union[str, Any]										=       ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size    )

														__snake_case		:      Optional[int]										=       self.config_cls(
														    vocab_size=self.vocab_size   ,     d_model=self.hidden_size   ,     encoder_layers=self.num_hidden_layers   ,     decoder_layers=self.num_hidden_layers   ,     encoder_attention_heads=self.num_attention_heads   ,     decoder_attention_heads=self.num_attention_heads   ,     encoder_ffn_dim=self.intermediate_size   ,     decoder_ffn_dim=self.intermediate_size   ,     dropout=self.hidden_dropout_prob   ,     attention_dropout=self.attention_probs_dropout_prob   ,     max_position_embeddings=self.max_position_embeddings   ,     eos_token_ids=[2]   ,     bos_token_id=self.bos_token_id   ,     pad_token_id=self.pad_token_id   ,     decoder_start_token_id=self.pad_token_id   ,     **self.config_updates   ,     )
														__snake_case		:      Tuple										=       prepare_mbart_inputs_dict(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )
														return config, inputs_dict






							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Union[str, Any]    )     ->   Any:
														__snake_case		:      Optional[int]										=       TFMBartModel(config=lowerCamelCase    ).get_decoder()
														__snake_case		:      List[str]										=       inputs_dict["input_ids"]

														__snake_case		:      List[Any]										=       input_ids[:1, :]
														__snake_case		:      Dict										=       inputs_dict["attention_mask"][:1, :]
														__snake_case		:      Dict										=       inputs_dict["head_mask"]
														__snake_case		:      int										=       1

														# first forward pass
														__snake_case		:      Any										=       model(lowerCamelCase   ,     attention_mask=lowerCamelCase   ,     head_mask=lowerCamelCase   ,     use_cache=lowerCamelCase    )

														__snake_case		:      List[Any]										=       outputs.to_tuple()
														__snake_case		:      str										=       past_key_values[1]







def     lowerCAmelCase_					( __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase    ,     __lowerCamelCase=None    ,     __lowerCamelCase=None    ,     __lowerCamelCase=None    ,     __lowerCamelCase=None    ,     __lowerCamelCase=None    ,     ):
							if attention_mask is None:
														__snake_case		:      Any										=       tf.cast(tf.math.not_equal(__lowerCamelCase    ,     config.pad_token_id     )    ,     tf.inta     )
							if decoder_attention_mask is None:
														__snake_case		:      List[Any]										=       tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape    ,     dtype=tf.inta     ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]    ,     config.pad_token_id     )    ,     tf.inta     ),
														    ]    ,     axis=-1    ,     )
							if head_mask is None:
														__snake_case		:      int										=       tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
							if decoder_head_mask is None:
														__snake_case		:      Union[str, Any]										=       tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
							if cross_attn_head_mask is None:
														__snake_case		:      List[Any]										=       tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

@require_tf
class     a		(_lowerCAmelCase					,    _lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							__UpperCAmelCase   :							List[Any]					  =  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							__UpperCAmelCase   :							List[Any]					  =  (
							    {
							        "conversational": TFMBartForConditionalGeneration,
							        "feature-extraction": TFMBartModel,
							        "summarization": TFMBartForConditionalGeneration,
							        "text2text-generation": TFMBartForConditionalGeneration,
							        "translation": TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							__UpperCAmelCase   :							List[str]					  =  True
							__UpperCAmelCase   :							int					  =  False
							__UpperCAmelCase   :							Tuple					  =  False

							def    __snake_case (							self	:  Optional[int]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  str   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Dict    )     ->   Union[str, Any]:
														if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																					# Exception encountered when calling layer '...'
																					return True

														return False

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[int]:
														__snake_case		:      Dict										=       TFMBartModelTester(self    )
														__snake_case		:      Tuple										=       ConfigTester(self   ,     config_class=lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   int:
														self.config_tester.run_common_tests()






							def    __snake_case (							self	:  Optional[int]    )     ->   Optional[Any]:
														__snake_case		:      Union[str, Any]										=       self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase    )

@require_sentencepiece
@require_tokenizers
@require_tf
class     a		(unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							str					  =  [
							    " UN Chief Says There Is No Military Solution in Syria",
							]
							__UpperCAmelCase   :							int					  =  [
							    "Şeful ONU declară că nu există o soluţie militară în Siria",
							]
							__UpperCAmelCase   :							Dict					  =  "facebook/mbart-large-en-ro"

							@cached_property
							def    __snake_case (							self	:  Optional[int]    )     ->   Dict:
														return AutoTokenizer.from_pretrained(self.model_name    )

							@cached_property
							def    __snake_case (							self	:  str    )     ->   str:
														__snake_case		:      Dict										=       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
														return model

							def    __snake_case (							self	:  Tuple   ,     **lowerCamelCase	:  Dict    )     ->   Optional[Any]:
														__snake_case		:      Union[str, Any]										=       self.translate_src_text(**lowerCamelCase    )
														self.assertListEqual(self.expected_text   ,     lowerCamelCase    )

							def    __snake_case (							self	:  str   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Optional[int]:
														__snake_case		:      Optional[int]										=       self.tokenizer(self.src_text   ,     **lowerCamelCase   ,     return_tensors="tf"    )
														__snake_case		:      Any										=       self.model.generate(
														    model_inputs.input_ids   ,     attention_mask=model_inputs.attention_mask   ,     num_beams=2    )
														__snake_case		:      Optional[int]										=       self.tokenizer.batch_decode(lowerCamelCase   ,     skip_special_tokens=lowerCamelCase    )
														return generated_words






							@slow
							def    __snake_case (							self	:  List[str]    )     ->   Optional[Any]:
														self._assert_generated_batch_equal_expected()





from __future__ import annotations

_snake_case					:		Union[str, Any]      =    []







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							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							,     __lowerCamelCase     ):
							if row >= len(__lowerCamelCase     ):
														solution.append(__lowerCamelCase     )
														printboard(__lowerCamelCase     )
														print()
														return True
							for i in range(len(__lowerCamelCase     )     ):
														if is_safe(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
																					__snake_case		:      Optional[Any]										=       1
																					solve(__lowerCamelCase							,     row + 1     )
																					__snake_case		:      Union[str, Any]										=       0
							return False







def     lowerCAmelCase_					( __lowerCamelCase     ):
							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"))
_snake_case					:		List[str]      =    8
_snake_case					:		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))

