


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 _lowerCamelCase:
       lowercase_					:      Tuple       					=     MBartConfig
       lowercase_					:      Optional[Any]       					=     {}
       lowercase_					:      List[Any]       					=     """gelu"""
       def __init__(   self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=7, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=99, lowerCamelCase=32, lowerCamelCase=2, lowerCamelCase=4, lowerCamelCase=37, lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=20, lowerCamelCase=2, lowerCamelCase=1, lowerCamelCase=0, )			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				str 						=						parent
           _lowercase			:				str 						=						batch_size
           _lowercase			:				Union[str, Any] 						=						seq_length
           _lowercase			:				Dict 						=						is_training
           _lowercase			:				str 						=						use_labels
           _lowercase			:				List[Any] 						=						vocab_size
           _lowercase			:				Dict 						=						hidden_size
           _lowercase			:				Tuple 						=						num_hidden_layers
           _lowercase			:				int 						=						num_attention_heads
           _lowercase			:				int 						=						intermediate_size
           _lowercase			:				Tuple 						=						hidden_dropout_prob
           _lowercase			:				int 						=						attention_probs_dropout_prob
           _lowercase			:				Union[str, Any] 						=						max_position_embeddings
           _lowercase			:				Optional[Any] 						=						eos_token_id
           _lowercase			:				Optional[int] 						=						pad_token_id
           _lowercase			:				Any 						=						bos_token_id
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

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

           _lowercase			:				Optional[int] 						=						ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

           _lowercase			:				Dict 						=						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, )
           _lowercase			:				str 						=						prepare_mbart_inputs_dict(lowerCamelCase, lowerCamelCase, lowerCamelCase)
           return config, inputs_dict




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  Any:




           """simple docstring"""

           _lowercase			:				str 						=						TFMBartModel(config=lowerCamelCase).get_decoder()
           _lowercase			:				Union[str, Any] 						=						inputs_dict['input_ids']

           _lowercase			:				str 						=						input_ids[:1, :]
           _lowercase			:				Optional[int] 						=						inputs_dict['attention_mask'][:1, :]
           _lowercase			:				Tuple 						=						inputs_dict['head_mask']
           _lowercase			:				Tuple 						=						1

           # first forward pass
           _lowercase			:				Union[str, Any] 						=						model(lowerCamelCase, attention_mask=lowerCamelCase, head_mask=lowerCamelCase, use_cache=lowerCamelCase)

           _lowercase						,							_lowercase			:				Union[str, Any] 						=						outputs.to_tuple()
           _lowercase			:				Optional[int] 						=						past_key_values[1]



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					lowerCamelCase_=None				,					)       ->		Optional[Any]:
    if attention_mask is None:
        _lowercase			:				Dict 						=						tf.cast(tf.math.not_equal(lowerCamelCase_				,					config.pad_token_id					)				,					tf.inta					)
    if decoder_attention_mask is None:
        _lowercase			:				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:
        _lowercase			:				List[Any] 						=						tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
    if decoder_head_mask is None:
        _lowercase			:				int 						=						tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
    if cross_attn_head_mask is None:
        _lowercase			:				Tuple 						=						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 _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Optional[int]       					=     (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
       lowercase_					:      Optional[Any]       					=     (TFMBartForConditionalGeneration,) if is_tf_available() else ()
       lowercase_					:      Optional[int]       					=     (
           {
               """conversational""": TFMBartForConditionalGeneration,
               """feature-extraction""": TFMBartModel,
               """summarization""": TFMBartForConditionalGeneration,
               """text2text-generation""": TFMBartForConditionalGeneration,
               """translation""": TFMBartForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       lowercase_					:      Optional[Any]       					=     True
       lowercase_					:      Union[str, Any]       					=     False
       lowercase_					:      Dict       					=     False
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
               # Exception encountered when calling layer '...'
               return True

           return False
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				List[str] 						=						TFMBartModelTester(self)
           _lowercase			:				List[Any] 						=						ConfigTester(self, config_class=lowerCamelCase)
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           self.config_tester.run_common_tests()




       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						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 _lowerCamelCase(		unittest.TestCase  ):
       lowercase_					:      Tuple       					=     [
           """ UN Chief Says There Is No Military Solution in Syria""",
       ]
       lowercase_					:      int       					=     [
           """Şeful ONU declară că nu există o soluţie militară în Siria""",
       ]
       lowercase_					:      Optional[Any]       					=     """facebook/mbart-large-en-ro"""
       @cached_property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           return AutoTokenizer.from_pretrained(self.model_name)
       @cached_property
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
           return model
       def      UpperCamelCase	(   self, **lowerCamelCase)			->  List[str]:




           """simple docstring"""

           _lowercase			:				str 						=						self.translate_src_text(**lowerCamelCase)
           self.assertListEqual(self.expected_text, lowerCamelCase)
       def      UpperCamelCase	(   self, **lowerCamelCase)			->  List[str]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						self.tokenizer(self.src_text, **lowerCamelCase, return_tensors='tf')
           _lowercase			:				int 						=						self.model.generate(
               model_inputs.input_ids, attention_mask=model_inputs.attention_mask, num_beams=2)
           _lowercase			:				List[Any] 						=						self.tokenizer.batch_decode(lowerCamelCase, skip_special_tokens=lowerCamelCase)
           return generated_words




       @slow
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           self._assert_generated_batch_equal_expected()






'''simple docstring'''






from __future__ import annotations

__UpperCAmelCase       							=				list[list[int]]

# assigning initial values to the grid
__UpperCAmelCase       							=				[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
__UpperCAmelCase       							=				[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]


def    _snake_case  (	A					,       A					,       A					,       A			)		->       bool:
							for i in range(9			):
														if grid[row][i] == n or grid[i][column] == n:
																					return False

							for i in range(3			):
														for j in range(3			):
																					if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																												return False

							return True


def    _snake_case  (	A			)		->       tuple[int, int] | None:
							for i in range(9			):
														for j in range(9			):
																					if grid[i][j] == 0:
																												return i, j
							return None


def    _snake_case  (	A			)		->       Matrix | None:
							if location := find_empty_location(A			):
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      location
							else:
														# If the location is ``None``, then the grid is solved.
														return grid

							for digit in range(1					,       10			):
														if is_safe(A					,       A					,       A					,       A			):
																					lowerCAmelCase__			  =      digit

																					if sudoku(A			) is not None:
																												return grid

																					lowerCAmelCase__			  =      0

							return None


def    _snake_case  (	A			)		->       None:
							for row in grid:
														for cell in row:
																					print(A					,       end=''' '''			)
														print()


if __name__ == "__main__":
		# make a copy of grid so that you can compare with the unmodified grid
		for example_grid in (initial_grid, no_solution):
				print('''\nExample grid:\n''' + '''=''' * 20)
				print_solution(example_grid)
				print('''\nExample grid solution:''')
				__UpperCAmelCase       							=				sudoku(example_grid)
				if solution is not None:
						print_solution(solution)
				else:
						print('''Cannot find a solution.''')