






"""simple docstring"""







import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
			    FlaxRoFormerForMaskedLM,
			    FlaxRoFormerForMultipleChoice,
			    FlaxRoFormerForQuestionAnswering,
			    FlaxRoFormerForSequenceClassification,
			    FlaxRoFormerForTokenClassification,
			    FlaxRoFormerModel,
			)


class        __UpperCAmelCase(  unittest.TestCase				):




						"""simple docstring"""




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




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: Tuple=			ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size     )

												lowercase__							: int=			None
												if self.use_attention_mask:
																		lowercase__							: Union[str, Any]=			random_attention_mask([self.batch_size, self.seq_length]     )

												lowercase__							: List[str]=			None
												if self.use_token_type_ids:
																		lowercase__							: str=			ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size     )

												lowercase__							: Optional[Any]=			RoFormerConfig(
												    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=snake_case__			,    initializer_range=self.initializer_range			,    )

												return config, input_ids, token_type_ids, attention_mask




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: str=			self.prepare_config_and_inputs()
												lowercase__,      lowercase__,      lowercase__,      lowercase__							: Dict=			config_and_inputs
												lowercase__							: Union[str, Any]=			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
												return config, inputs_dict


@require_flax
class        __UpperCAmelCase(  __lowerCamelCase    ,    unittest.TestCase				):




						"""simple docstring"""




						__lowerCamelCase										=   True

						__lowerCamelCase										=   (
						    (
						        FlaxRoFormerModel,
						        FlaxRoFormerForMaskedLM,
						        FlaxRoFormerForSequenceClassification,
						        FlaxRoFormerForTokenClassification,
						        FlaxRoFormerForMultipleChoice,
						        FlaxRoFormerForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)




						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: List[str]=			FlaxRoFormerModelTester(self     )




						@slow
						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''






												for model_class_name in self.all_model_classes:
																		lowercase__							: List[Any]=			model_class_name.from_pretrained("junnyu/roformer_chinese_small"			,    from_pt=snake_case__     )
																		lowercase__							: Dict=			model(np.ones((1, 1)     )     )
																		self.assertIsNotNone(snake_case__     )


@require_flax
class        __UpperCAmelCase(  unittest.TestCase				):




						"""simple docstring"""




						@slow
						def 						UpperCAmelCase_			(  self     ):
												'''simple docstring'''
												lowercase__							: List[str]=			FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"     )
												lowercase__							: Optional[int]=			jnp.array([[0, 1, 2, 3, 4, 5]]     )
												lowercase__							: Dict=			model(snake_case__     )[0]

												lowercase__							: Optional[int]=			50000

												lowercase__							: List[str]=			(1, 6, vocab_size)
												self.assertEqual(output.shape			,    snake_case__     )

												lowercase__							: int=			jnp.array(
												    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]     )

												self.assertTrue(jnp.allclose(output[:, :3, :3]			,    snake_case__			,    atol=1e-4     )     )








from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      str:







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				if weight not in (0, 1):
							raise ValueError('''Edge weight must be either 0 or 1.'''			)

				if to_vertex < 0 or to_vertex >= self.size:
							raise ValueError('''Vertex indexes must be in [0; size).'''			)

				self._graph[from_vertex].append(Edge(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

				while queue:
							SCREAMING_SNAKE_CASE     						= queue.popleft()
							SCREAMING_SNAKE_CASE     						= distances[current_vertex]
							if current_distance is None:
										continue

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= new_distance
										if edge.weight == 0:
													queue.appendleft(edge.destination_vertex			)
										else:
													queue.append(edge.destination_vertex			)

				if distances[finish_vertex] is None:
							raise ValueError('''No path from start_vertex to finish_vertex.'''			)

				return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()