



'''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 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			def __init__(							self     : Optional[Any]							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Optional[int]=1_3							,     lowerCAmelCase__     : Union[str, Any]=7							,     lowerCAmelCase__     : Any=True							,     lowerCAmelCase__     : Optional[Any]=True							,     lowerCAmelCase__     : List[Any]=True							,     lowerCAmelCase__     : Optional[int]=True							,     lowerCAmelCase__     : List[str]=9_9							,     lowerCAmelCase__     : Dict=3_2							,     lowerCAmelCase__     : Union[str, Any]=5							,     lowerCAmelCase__     : List[str]=4							,     lowerCAmelCase__     : Any=3_7							,     lowerCAmelCase__     : Any="gelu"							,     lowerCAmelCase__     : Optional[int]=0.1							,     lowerCAmelCase__     : List[str]=0.1							,     lowerCAmelCase__     : str=5_1_2							,     lowerCAmelCase__     : int=1_6							,     lowerCAmelCase__     : int=2							,     lowerCAmelCase__     : List[Any]=0.02							,     lowerCAmelCase__     : int=4							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		parent
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		batch_size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		seq_length
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		is_training
					__SCREAMING_SNAKE_CASE       :		List[str]           =		use_attention_mask
					__SCREAMING_SNAKE_CASE       :		int           =		use_token_type_ids
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		use_labels
					__SCREAMING_SNAKE_CASE       :		List[str]           =		vocab_size
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		hidden_size
					__SCREAMING_SNAKE_CASE       :		Dict           =		num_hidden_layers
					__SCREAMING_SNAKE_CASE       :		Dict           =		num_attention_heads
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		intermediate_size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		hidden_act
					__SCREAMING_SNAKE_CASE       :		Dict           =		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE       :		int           =		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE       :		Any           =		max_position_embeddings
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		type_vocab_size
					__SCREAMING_SNAKE_CASE       :		Dict           =		type_sequence_label_size
					__SCREAMING_SNAKE_CASE       :		Dict           =		initializer_range
					__SCREAMING_SNAKE_CASE       :		Any           =		num_choices

			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size						)

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

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		None
					if self.use_token_type_ids:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		ids_tensor([self.batch_size, self.seq_length]							,     self.type_vocab_size						)

					__SCREAMING_SNAKE_CASE       :		Optional[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=lowerCAmelCase__							,     initializer_range=self.initializer_range							,     )

					return config, input_ids, token_type_ids, attention_mask



			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		config_and_inputs
					__SCREAMING_SNAKE_CASE       :		int           =		{"""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'''

			_A      :		Tuple			    =		(
			    (
			        FlaxAlbertModel,
			        FlaxAlbertForPreTraining,
			        FlaxAlbertForMaskedLM,
			        FlaxAlbertForMultipleChoice,
			        FlaxAlbertForQuestionAnswering,
			        FlaxAlbertForSequenceClassification,
			        FlaxAlbertForTokenClassification,
			        FlaxAlbertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		FlaxAlbertModelTester(self						)



			@slow
			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		model_class_name.from_pretrained("""albert-base-v2"""						)
							__SCREAMING_SNAKE_CASE       :		List[str]           =		model(np.ones((1, 1)						)						)
							self.assertIsNotNone(lowerCAmelCase__						)






@require_flax
class 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			@slow
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		FlaxAlbertModel.from_pretrained("""albert-base-v2"""						)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]						)
					__SCREAMING_SNAKE_CASE       :		str           =		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		model(lowerCAmelCase__							,     attention_mask=lowerCAmelCase__						)[0]
					__SCREAMING_SNAKE_CASE       :		int           =		(1, 1_1, 7_6_8)
					self.assertEqual(output.shape							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		str           =		np.array(
					    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]						)

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




'''simple docstring'''




from __future__ import annotations


def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   list[int]       ,      _lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		list(range(len(_lowerCamelCase					)					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[v / w for v, w in zip(_lowerCamelCase       ,      _lowerCamelCase					)]
		index.sort(key=lambda _lowerCamelCase					: ratio[i]       ,      reverse=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		float           =		0
		__SCREAMING_SNAKE_CASE       :		list[float]           =		[0] * len(_lowerCamelCase					)
		for i in index:
				if weight[i] <= capacity:
						__SCREAMING_SNAKE_CASE       :		str           =		1
						max_value += value[i]
						capacity -= weight[i]
				else:
						__SCREAMING_SNAKE_CASE       :		int           =		capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
						import doctest

						doctest.testmod()