

"""simple docstring"""




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 			A						(							unittest.TestCase			):







    """simple docstring"""





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





        '''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 						snake_case__					(				self			:	str		)->   Any:





        '''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__   				=       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_=lowercase_,)

        return config, input_ids, attention_mask


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





        '''simple docstring'''







        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 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							(
        (
            FlaxDistilBertModel,
            FlaxDistilBertForMaskedLM,
            FlaxDistilBertForMultipleChoice,
            FlaxDistilBertForQuestionAnswering,
            FlaxDistilBertForSequenceClassification,
            FlaxDistilBertForTokenClassification,
            FlaxDistilBertForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )


    def 						snake_case__					(				self			:	str		)->   int:





        '''simple docstring'''







        A__   				=       FlaxDistilBertModelTester(self		)


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





        '''simple docstring'''







        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(lowercase_		)




@require_flax
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    @slow
    def 						snake_case__					(				self			:	Any		)->   Any:





        '''simple docstring'''







        A__   				=       FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'		)
        A__   				=       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]]		)
        A__   				=       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
        A__   				=       model(lowercase_,attention_mask=lowercase_		)[0]
        A__   				=       (1, 1_1, 7_6_8)
        self.assertEqual(output.shape,lowercase_		)
        A__   				=       np.array([[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]		)

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




from __future__ import annotations

from typing import Any
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list				)							->				int:





    '''simple docstring'''






    if not postfix_notation:
        return 0

    A__   				=       {'+', '-', '*', '/'}
    A__   				=       []

    for token in postfix_notation:
        if token in operations:
            A__				,						A__   				=       stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(SCREAMING_SNAKE_CASE__				)				)

    return stack.pop()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

