



import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
				    FlaxDistilBertForMaskedLM,
				    FlaxDistilBertForMultipleChoice,
				    FlaxDistilBertForQuestionAnswering,
				    FlaxDistilBertForSequenceClassification,
				    FlaxDistilBertForTokenClassification,
				    FlaxDistilBertModel,
				)



class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __init__(							self      :  List[Any]						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Union[str, Any]=13						, __UpperCamelCase      :  Optional[int]=7						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  Tuple=99						, __UpperCamelCase      :  Tuple=32						, __UpperCamelCase      :  int=5						, __UpperCamelCase      :  Dict=4						, __UpperCamelCase      :  Dict=37						, __UpperCamelCase      :  Dict="gelu"						, __UpperCamelCase      :  int=0.1						, __UpperCamelCase      :  Dict=0.1						, __UpperCamelCase      :  List[str]=512						, __UpperCamelCase      :  Optional[int]=16						, __UpperCamelCase      :  Any=2						, __UpperCamelCase      :  int=0.0_2						, __UpperCamelCase      :  str=4						, )		->     Union[str, Any]:
											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 __UpperCamelCase						(							self      :  Optional[int]       )		->     Any:
											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      						=    DistilBertConfig(
											    vocab_size=self.vocab_size						, dim=self.hidden_size						, n_layers=self.num_hidden_layers						, n_heads=self.num_attention_heads						, hidden_dim=self.intermediate_size						, hidden_act=self.hidden_act						, dropout=self.hidden_dropout_prob						, attention_dropout=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, initializer_range=self.initializer_range						, tie_weights_=__UpperCamelCase						, )

											return config, input_ids, attention_mask


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[Any]:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A      						=    config_and_inputs
											A      						=    {'input_ids': input_ids, 'attention_mask': attention_mask}
											return config, inputs_dict





@require_flax
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     str								=     (
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Union[str, Any]:
											A      						=    FlaxDistilBertModelTester(self       )


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											for model_class_name in self.all_model_classes:
																		A      						=    model_class_name.from_pretrained('distilbert-base-uncased'       )
																		A      						=    model(np.ones((1, 1)       )       )
																		self.assertIsNotNone(__UpperCamelCase       )





@require_flax
class        lowerCAmelCase__					(  unittest.TestCase       ):


				@slow
				def __UpperCamelCase						(							self      :  Dict       )		->     Union[str, Any]:
											A      						=    FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'       )
											A      						=    np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]       )
											A      						=    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
											A      						=    model(__UpperCamelCase						, attention_mask=__UpperCamelCase       )[0]
											A      						=    (1, 11, 768)
											self.assertEqual(output.shape						, __UpperCamelCase       )
											A      						=    np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]       )

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




from bisect import bisect
from itertools import accumulate






def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[Any]				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    sorted(zip(lowerCAmelCase__			,	lowerCAmelCase__				)			,	key=lambda lowerCAmelCase__				: x[0] / x[1]			,	reverse=lowerCAmelCase__				)
							A						,   A      						=    [i[0] for i in r], [i[1] for i in r]
							A      						=    list(accumulate(lowerCAmelCase__				)				)
							A      						=    bisect(lowerCAmelCase__			,	lowerCAmelCase__				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()