





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   A			(unittest.TestCase    ):
			'''simple docstring'''




			def __init__(			self   :    List[str]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    List[str]=13							,	__lowerCAmelCase   :    int=7							,	__lowerCAmelCase   :    Tuple=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    Dict=True							,	__lowerCAmelCase   :    Union[str, Any]=True							,	__lowerCAmelCase   :    List[Any]=99							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Optional[Any]=5							,	__lowerCAmelCase   :    Tuple=4							,	__lowerCAmelCase   :    Any=37							,	__lowerCAmelCase   :    str="gelu"							,	__lowerCAmelCase   :    Optional[Any]=0.1							,	__lowerCAmelCase   :    Union[str, Any]=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    int=16							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    List[Any]=0.0_2							,	__lowerCAmelCase   :    Tuple=4							,	)	->	Dict:







					"""simple docstring"""






					A__        =   parent
					A__        =   batch_size
					A__        =   seq_length
					A__        =   is_training
					A__        =   use_attention_mask
					A__        =   use_token_type_ids
					A__        =   use_labels
					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   intermediate_size
					A__        =   hidden_act
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   type_sequence_label_size
					A__        =   initializer_range
					A__        =   num_choices






			def    a_       (			self   :    Any   )	->	str:







					"""simple docstring"""






					A__        =   ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size   )

					A__        =   None
					if self.use_attention_mask:
							A__        =   random_attention_mask([self.batch_size, self.seq_length]   )

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

					A__        =   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=__lowerCAmelCase							,	initializer_range=self.initializer_range							,	)

					return config, input_ids, token_type_ids, attention_mask







			def    a_       (			self   :    Optional[int]   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   self.prepare_config_and_inputs()
					A__      ,			A__      ,			A__      ,			A__        =   config_and_inputs
					A__        =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
					return config, inputs_dict


@require_flax
class   A			(SCREAMING_SNAKE_CASE   ,     unittest.TestCase    ):
			'''simple docstring'''




			__lowerCamelCase	:       str							=    (
			    (
			        FlaxAlbertModel,
			        FlaxAlbertForPreTraining,
			        FlaxAlbertForMaskedLM,
			        FlaxAlbertForMultipleChoice,
			        FlaxAlbertForQuestionAnswering,
			        FlaxAlbertForSequenceClassification,
			        FlaxAlbertForTokenClassification,
			        FlaxAlbertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def    a_       (			self   :    str   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   FlaxAlbertModelTester(self   )







			@slow
			def    a_       (			self   :    int   )	->	Tuple:







					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							A__        =   model_class_name.from_pretrained("""albert-base-v2"""   )
							A__        =   model(np.ones((1, 1)   )   )
							self.assertIsNotNone(__lowerCAmelCase   )


@require_flax
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			@slow
			def    a_       (			self   :    Dict   )	->	List[Any]:







					"""simple docstring"""






					A__        =   FlaxAlbertModel.from_pretrained("""albert-base-v2"""   )
					A__        =   np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]   )
					A__        =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
					A__        =   model(__lowerCAmelCase							,	attention_mask=__lowerCAmelCase   )[0]
					A__        =   (1, 11, 7_68)
					self.assertEqual(output.shape							,	__lowerCAmelCase   )
					A__        =   np.array(
					    [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]   )

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










def    __lowerCamelCase					(							__a   :str    )		->				list:






		"""simple docstring"""

		A__        =   [0] * len(__a    )

		for i in range(1    ,     len(__a    )    ):
				# use last results for better performance - dynamic programming
				A__        =   prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						A__        =   prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				A__        =   j

		return prefix_result







def    __lowerCamelCase					(							__a   :str    )		->				int:






		"""simple docstring"""

		return max(prefix_function(__a    )    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



