

"""simple docstring"""
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     UpperCamelCase_ :
	__magic_name__			   =       MBartConfig
	__magic_name__			   =       {}
	__magic_name__			   =       '''gelu'''





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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Any:
				UpperCAmelCase_  :				Any      =      ids_tensor([self.batch_size, self.seq_length - 1]			,			self.vocab_size		)
				UpperCAmelCase_  :				Dict      =      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)			,			1		)
				UpperCAmelCase_  :				Tuple      =      tf.concat([input_ids, eos_tensor]			,			axis=1		)

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

				UpperCAmelCase_  :				List[Any]      =      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			,			)
				UpperCAmelCase_  :				str      =      prepare_mbart_inputs_dict(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
				return config, inputs_dict





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				List[Any]		)		->    Optional[int]:
				UpperCAmelCase_  :				List[str]      =      TFMBartModel(config=lowerCAmelCase_		).get_decoder()
				UpperCAmelCase_  :				int      =      inputs_dict["input_ids"]

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

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

				UpperCAmelCase_  :				Optional[int]      =      outputs.to_tuple()
				UpperCAmelCase_  :				Dict      =      past_key_values[1]





def        snake_case   (     A__			,A__			,A__			,A__=None			,A__=None			,A__=None			,A__=None			,A__=None			,):
			if attention_mask is None:
						UpperCAmelCase_  :				Tuple      =      tf.cast(tf.math.not_equal(A__			,config.pad_token_id		)			,tf.inta		)
			if decoder_attention_mask is None:
						UpperCAmelCase_  :				str      =      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:
						UpperCAmelCase_  :				Any      =      tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
			if decoder_head_mask is None:
						UpperCAmelCase_  :				List[Any]      =      tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
			if cross_attn_head_mask is None:
						UpperCAmelCase_  :				int      =      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     UpperCamelCase_ (__A       ,		__A       ,		unittest.TestCase  ):
	__magic_name__			   =       (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	__magic_name__			   =       (TFMBartForConditionalGeneration,) if is_tf_available() else ()
	__magic_name__			   =       (
	    {
	        '''conversational''': TFMBartForConditionalGeneration,
	        '''feature-extraction''': TFMBartModel,
	        '''summarization''': TFMBartForConditionalGeneration,
	        '''text2text-generation''': TFMBartForConditionalGeneration,
	        '''translation''': TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	__magic_name__			   =       True
	__magic_name__			   =       False
	__magic_name__			   =       False





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

				return False





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      TFMBartModelTester(self		)
				UpperCAmelCase_  :				Union[str, Any]      =      ConfigTester(self			,			config_class=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    str:
				self.config_tester.run_common_tests()





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Any:
				UpperCAmelCase_  :				str      =      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     UpperCamelCase_ (unittest.TestCase  ):
	__magic_name__			   =       [
	    ''' UN Chief Says There Is No Military Solution in Syria''',
	]
	__magic_name__			   =       [
	    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
	]
	__magic_name__			   =       '''facebook/mbart-large-en-ro'''





	@cached_property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Union[str, Any]:
				return AutoTokenizer.from_pretrained(self.model_name		)





	@cached_property
	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Dict:
				UpperCAmelCase_  :				List[str]      =      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
				return model





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			**lowerCAmelCase_      :				List[Any]		)		->    str:
				UpperCAmelCase_  :				List[Any]      =      self.translate_src_text(**lowerCAmelCase_		)
				self.assertListEqual(self.expected_text			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			**lowerCAmelCase_      :				Dict		)		->    List[str]:
				UpperCAmelCase_  :				Tuple      =      self.tokenizer(self.src_text			,			**lowerCAmelCase_			,			return_tensors="tf"		)
				UpperCAmelCase_  :				Tuple      =      self.model.generate(
				    model_inputs.input_ids			,			attention_mask=model_inputs.attention_mask			,			num_beams=2		)
				UpperCAmelCase_  :				List[str]      =      self.tokenizer.batch_decode(lowerCAmelCase_			,			skip_special_tokens=lowerCAmelCase_		)
				return generated_words





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Optional[Any]:
				self._assert_generated_batch_equal_expected()











"""simple docstring"""
from __future__ import annotations

import time

lowerCamelCase_       			=   list[tuple[int, int]]

lowerCamelCase_       			=   [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase_       			=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right







class     UpperCamelCase_ :





	def __init__(   self      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Node | None		)		->    List[str]:
				UpperCAmelCase_  :				List[Any]      =      pos_x
				UpperCAmelCase_  :				Dict      =      pos_y
				UpperCAmelCase_  :				List[Any]      =      (pos_y, pos_x)
				UpperCAmelCase_  :				int      =      goal_x
				UpperCAmelCase_  :				int      =      goal_y
				UpperCAmelCase_  :				Union[str, Any]      =      parent







class     UpperCamelCase_ :





	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				tuple[int, int]			,			lowerCAmelCase_      :				tuple[int, int]		)		->    int:
				UpperCAmelCase_  :				Dict      =      Node(start[1]			,			start[0]			,			goal[1]			,			goal[0]			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      Node(goal[1]			,			goal[0]			,			goal[1]			,			goal[0]			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      [self.start]
				UpperCAmelCase_  :				List[Any]      =      False





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Path | None:
				while self.node_queue:
							UpperCAmelCase_  :				Optional[int]      =      self.node_queue.pop(0		)

							if current_node.pos == self.target.pos:
										UpperCAmelCase_  :				int      =      True
										return self.retrace_path(lowerCAmelCase_		)

							UpperCAmelCase_  :				Union[str, Any]      =      self.get_successors(lowerCAmelCase_		)

							for node in successors:
										self.node_queue.append(lowerCAmelCase_		)

				if not self.reached:
							return [self.start.pos]
				return None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Node		)		->    list[Node]:
				UpperCAmelCase_  :				Union[str, Any]      =      []
				for action in delta:
							UpperCAmelCase_  :				Optional[Any]      =      parent.pos_x + action[1]
							UpperCAmelCase_  :				Tuple      =      parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(lowerCAmelCase_		) - 1):
										continue

							if grid[pos_y][pos_x] != 0:
										continue

							successors.append(
							    Node(lowerCAmelCase_			,			lowerCAmelCase_			,			self.target.pos_y			,			self.target.pos_x			,			lowerCAmelCase_		)		)
				return successors





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Node | None		)		->    Path:
				UpperCAmelCase_  :				Any      =      node
				UpperCAmelCase_  :				Any      =      []
				while current_node is not None:
							path.append((current_node.pos_y, current_node.pos_x)		)
							UpperCAmelCase_  :				int      =      current_node.parent
				path.reverse()
				return path







class     UpperCamelCase_ :





	def __init__(   self      :				str			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Tuple		)		->    Tuple:
				UpperCAmelCase_  :				Union[str, Any]      =      BreadthFirstSearch(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      BreadthFirstSearch(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      False





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Path | None:
				while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
							UpperCAmelCase_  :				List[Any]      =      self.fwd_bfs.node_queue.pop(0		)
							UpperCAmelCase_  :				Tuple      =      self.bwd_bfs.node_queue.pop(0		)

							if current_bwd_node.pos == current_fwd_node.pos:
										UpperCAmelCase_  :				Optional[int]      =      True
										return self.retrace_bidirectional_path(
										    lowerCAmelCase_			,			lowerCAmelCase_		)

							UpperCAmelCase_  :				Any      =      current_bwd_node
							UpperCAmelCase_  :				Optional[int]      =      current_fwd_node

							UpperCAmelCase_  :				Any      =      {
							    self.fwd_bfs: self.fwd_bfs.get_successors(lowerCAmelCase_		),
							    self.bwd_bfs: self.bwd_bfs.get_successors(lowerCAmelCase_		),
							}

							for bfs in [self.fwd_bfs, self.bwd_bfs]:
										for node in successors[bfs]:
													bfs.node_queue.append(lowerCAmelCase_		)

				if not self.reached:
							return [self.fwd_bfs.start.pos]
				return None





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				Node			,			lowerCAmelCase_      :				Node		)		->    Path:
				UpperCAmelCase_  :				Tuple      =      self.fwd_bfs.retrace_path(lowerCAmelCase_		)
				UpperCAmelCase_  :				str      =      self.bwd_bfs.retrace_path(lowerCAmelCase_		)
				bwd_path.pop()
				bwd_path.reverse()
				UpperCAmelCase_  :				int      =      fwd_path + bwd_path
				return path


if __name__ == "__main__":
			# all coordinates are given in format [y,x]
			import doctest

			doctest.testmod()
			lowerCamelCase_       			=   (0, 0)
			lowerCamelCase_       			=   (len(grid) - 1, len(grid[0]) - 1)
			for elem in grid:
						print(elem)

			lowerCamelCase_       			=   time.time()
			lowerCamelCase_       			=   BreadthFirstSearch(init, goal)
			lowerCamelCase_       			=   bfs.search()
			lowerCamelCase_       			=   time.time() - start_bfs_time

			print('''Unidirectional BFS computation time : ''', bfs_time)

			lowerCamelCase_       			=   time.time()
			lowerCamelCase_       			=   BidirectionalBreadthFirstSearch(init, goal)
			lowerCamelCase_       			=   bd_bfs.search()
			lowerCamelCase_       			=   time.time() - start_bd_bfs_time

			print('''Bidirectional BFS computation time : ''', bd_bfs_time)



