






'''simple docstring'''







import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.albert.modeling_flax_albert import (
			    FlaxAlbertForMaskedLM,
			    FlaxAlbertForMultipleChoice,
			    FlaxAlbertForPreTraining,
			    FlaxAlbertForQuestionAnswering,
			    FlaxAlbertForSequenceClassification,
			    FlaxAlbertForTokenClassification,
			    FlaxAlbertModel,
			)

class _lowercase	(				unittest.TestCase		):




							def __init__(							self:				str			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Optional[Any]=13			,							UpperCamelCase__:				Optional[Any]=7			,							UpperCamelCase__:				List[Any]=True			,							UpperCamelCase__:				List[str]=True			,							UpperCamelCase__:				str=True			,							UpperCamelCase__:				Optional[int]=True			,							UpperCamelCase__:				Tuple=99			,							UpperCamelCase__:				Dict=32			,							UpperCamelCase__:				str=5			,							UpperCamelCase__:				Tuple=4			,							UpperCamelCase__:				str=37			,							UpperCamelCase__:				str="gelu"			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				List[Any]=0.1			,							UpperCamelCase__:				List[str]=512			,							UpperCamelCase__:				List[Any]=16			,							UpperCamelCase__:				str=2			,							UpperCamelCase__:				Union[str, Any]=0.02			,							UpperCamelCase__:				Any=4			,							):
														lowerCamelCase__					:	Any         =       parent
														lowerCamelCase__					:	List[str]         =       batch_size
														lowerCamelCase__					:	Union[str, Any]         =       seq_length
														lowerCamelCase__					:	List[str]         =       is_training
														lowerCamelCase__					:	Optional[Any]         =       use_attention_mask
														lowerCamelCase__					:	Any         =       use_token_type_ids
														lowerCamelCase__					:	Dict         =       use_labels
														lowerCamelCase__					:	List[str]         =       vocab_size
														lowerCamelCase__					:	Dict         =       hidden_size
														lowerCamelCase__					:	Any         =       num_hidden_layers
														lowerCamelCase__					:	List[Any]         =       num_attention_heads
														lowerCamelCase__					:	List[Any]         =       intermediate_size
														lowerCamelCase__					:	str         =       hidden_act
														lowerCamelCase__					:	Optional[int]         =       hidden_dropout_prob
														lowerCamelCase__					:	int         =       attention_probs_dropout_prob
														lowerCamelCase__					:	List[Any]         =       max_position_embeddings
														lowerCamelCase__					:	Union[str, Any]         =       type_vocab_size
														lowerCamelCase__					:	str         =       type_sequence_label_size
														lowerCamelCase__					:	int         =       initializer_range
														lowerCamelCase__					:	List[Any]         =       num_choices




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size     )

														lowerCamelCase__					:	int         =       None
														if self.use_attention_mask:
																					lowerCamelCase__					:	List[Any]         =       random_attention_mask([self.batch_size, self.seq_length]     )

														lowerCamelCase__					:	str         =       None
														if self.use_token_type_ids:
																					lowerCamelCase__					:	Optional[int]         =       ids_tensor([self.batch_size, self.seq_length]			,							self.type_vocab_size     )

														lowerCamelCase__					:	List[Any]         =       AlbertConfig(
														    vocab_size=self.vocab_size			,							hidden_size=self.hidden_size			,							num_hidden_layers=self.num_hidden_layers			,							num_attention_heads=self.num_attention_heads			,							intermediate_size=self.intermediate_size			,							hidden_act=self.hidden_act			,							hidden_dropout_prob=self.hidden_dropout_prob			,							attention_probs_dropout_prob=self.attention_probs_dropout_prob			,							max_position_embeddings=self.max_position_embeddings			,							type_vocab_size=self.type_vocab_size			,							is_decoder=UpperCamelCase__			,							initializer_range=self.initializer_range			,							)

														return config, input_ids, token_type_ids, attention_mask




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	int         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	str         =       config_and_inputs
														lowerCamelCase__					:	Tuple         =       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
														return config, inputs_dict



@require_flax
class _lowercase	(				_lowercase				,      unittest.TestCase		):
							a				      =       (
							    (
							        FlaxAlbertModel,
							        FlaxAlbertForPreTraining,
							        FlaxAlbertForMaskedLM,
							        FlaxAlbertForMultipleChoice,
							        FlaxAlbertForQuestionAnswering,
							        FlaxAlbertForSequenceClassification,
							        FlaxAlbertForTokenClassification,
							        FlaxAlbertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	int         =       FlaxAlbertModelTester(self     )




							@slow
							def 		lowerCamelCase_				(							self:				str     ):
														for model_class_name in self.all_model_classes:
																					lowerCamelCase__					:	Union[str, Any]         =       model_class_name.from_pretrained("""albert-base-v2"""     )
																					lowerCamelCase__					:	Dict         =       model(np.ones((1, 1)     )     )
																					self.assertIsNotNone(UpperCamelCase__     )



@require_flax
class _lowercase	(				unittest.TestCase		):




							@slow
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	Dict         =       FlaxAlbertModel.from_pretrained("""albert-base-v2"""     )
														lowerCamelCase__					:	Dict         =       np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]     )
														lowerCamelCase__					:	List[str]         =       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
														lowerCamelCase__					:	int         =       model(UpperCamelCase__			,							attention_mask=UpperCamelCase__     )[0]
														lowerCamelCase__					:	List[Any]         =       (1, 11, 768)
														self.assertEqual(output.shape			,							UpperCamelCase__     )
														lowerCamelCase__					:	int         =       np.array(
														    [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]     )

														self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]			,							UpperCamelCase__			,							atol=1e-4     )     )









'''simple docstring'''







from heapq import heappop, heappush

import numpy as np





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			)      ->       tuple[float | int, list[tuple[int, int]]]:
							lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       grid.shape
							lowerCamelCase__					:	List[str]         =       [-1, 1, 0, 0]
							lowerCamelCase__					:	Dict         =       [0, 0, -1, 1]
							if allow_diagonal:
														dx += [-1, -1, 1, 1]
														dy += [-1, 1, -1, 1]

							lowerCamelCase__  ,		lowerCamelCase__					:	Any         =       [(0, source)], set()
							lowerCamelCase__					:	Tuple         =       np.full((rows, cols)				,			np.inf      )
							lowerCamelCase__					:	List[str]         =       0
							lowerCamelCase__					:	Optional[int]         =       np.empty((rows, cols)				,			dtype=UpperCamelCase      )
							lowerCamelCase__					:	str         =       None

							while queue:
														((lowerCamelCase__)  ,		(lowerCamelCase__))					:	List[str]         =       heappop(UpperCamelCase      )
														if (x, y) in visited:
																					continue
														visited.add((x, y)      )

														if (x, y) == destination:
																					lowerCamelCase__					:	Optional[int]         =       []
																					while (x, y) != source:
																												path.append((x, y)      )
																												lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       predecessors[x, y]
																					path.append(UpperCamelCase      )  # add the source manually
																					path.reverse()
																					return matrix[destination], path

														for i in range(len(UpperCamelCase      )      ):
																					lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       x + dx[i], y + dy[i]
																					if 0 <= nx < rows and 0 <= ny < cols:
																												lowerCamelCase__					:	Any         =       grid[nx][ny]
																												if next_node == 1 and matrix[nx, ny] > dist + 1:
																																			heappush(UpperCamelCase				,			(dist + 1, (nx, ny))      )
																																			lowerCamelCase__					:	Union[str, Any]         =       dist + 1
																																			lowerCamelCase__					:	List[str]         =       (x, y)

							return np.inf, []


if __name__ == "__main__":
			import doctest

			doctest.testmod()

