

'''simple docstring'''

import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.bert.modeling_flax_bert import (
       FlaxBertForMaskedLM,
       FlaxBertForMultipleChoice,
       FlaxBertForNextSentencePrediction,
       FlaxBertForPreTraining,
       FlaxBertForQuestionAnswering,
       FlaxBertForSequenceClassification,
       FlaxBertForTokenClassification,
       FlaxBertModel,
   )
class     __lowerCAmelCase					(   unittest.TestCase			):






       """simple docstring"""





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		List[str]	,			lowerCAmelCase__       :		Dict=13	,			lowerCAmelCase__       :		List[Any]=7	,			lowerCAmelCase__       :		Optional[Any]=True	,			lowerCAmelCase__       :		int=True	,			lowerCAmelCase__       :		int=True	,			lowerCAmelCase__       :		Tuple=True	,			lowerCAmelCase__       :		Tuple=99	,			lowerCAmelCase__       :		Optional[int]=32	,			lowerCAmelCase__       :		Dict=5	,			lowerCAmelCase__       :		Tuple=4	,			lowerCAmelCase__       :		Union[str, Any]=37	,			lowerCAmelCase__       :		Tuple="gelu"	,			lowerCAmelCase__       :		Union[str, Any]=0.1	,			lowerCAmelCase__       :		Dict=0.1	,			lowerCAmelCase__       :		List[str]=512	,			lowerCAmelCase__       :		Tuple=16	,			lowerCAmelCase__       :		Optional[Any]=2	,			lowerCAmelCase__       :		Dict=0.02	,			lowerCAmelCase__       :		Tuple=4	,			)   ->					List[str]:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Dict					)   ->					Union[str, Any]:

            '''simple docstring'''




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

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

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

            _UpperCamelCase       =	BertConfig(
                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=_SCREAMING_SNAKE_CASE	,			initializer_range=self.initializer_range	,			)

            return config, input_ids, token_type_ids, attention_mask





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

            '''simple docstring'''




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







       def snake_case__						(							self       :		Tuple					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	self.prepare_config_and_inputs()
            _UpperCamelCase       =	config_and_inputs

            _UpperCamelCase       =	True
            _UpperCamelCase       =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
            _UpperCamelCase       =	ids_tensor([self.batch_size, self.seq_length]	,			vocab_size=2					)

            return (
                config,
                input_ids,
                attention_mask,
                encoder_hidden_states,
                encoder_attention_mask,
            )

@require_flax
class     __lowerCAmelCase					(   SCREAMING_SNAKE_CASE__						,						unittest.TestCase			):






       """simple docstring"""
       _snake_case     :					List[str]						    =   True

       _snake_case     :					List[str]						    =   (
           (
               FlaxBertModel,
               FlaxBertForPreTraining,
               FlaxBertForMaskedLM,
               FlaxBertForMultipleChoice,
               FlaxBertForQuestionAnswering,
               FlaxBertForNextSentencePrediction,
               FlaxBertForSequenceClassification,
               FlaxBertForTokenClassification,
               FlaxBertForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )





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

            '''simple docstring'''




            _UpperCamelCase       =	FlaxBertModelTester(self					)







       @slow
       def snake_case__						(							self       :		Union[str, Any]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	FlaxBertModel.from_pretrained('''bert-base-cased'''					)
            _UpperCamelCase       =	model(np.ones((1, 1)					)					)
            self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)










'''simple docstring'''

from __future__ import annotations







def  a__    (    lowercase :					list[int], lowercase :					list[int], lowercase :					int				)		->		tuple[float, list[float]]:





     """simple docstring"""

     _UpperCamelCase       =	list(range(len(lowercase				)				)				)
     _UpperCamelCase       =	[v / w for v, w in zip(lowercase, lowercase				)]
     index.sort(key=lambda lowercase				: ratio[i], reverse=lowercase				)

     _UpperCamelCase       =	0
     _UpperCamelCase       =	[0] * len(lowercase				)
     for i in index:
          if weight[i] <= capacity:
               _UpperCamelCase       =	1
               max_value += value[i]
               capacity -= weight[i]
          else:
               _UpperCamelCase       =	capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
   import doctest

   doctest.testmod()



