



'''simple docstring'''





import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
							import jax.numpy as jnp

							from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
							    FlaxRobertaPreLayerNormForCausalLM,
							    FlaxRobertaPreLayerNormForMaskedLM,
							    FlaxRobertaPreLayerNormForMultipleChoice,
							    FlaxRobertaPreLayerNormForQuestionAnswering,
							    FlaxRobertaPreLayerNormForSequenceClassification,
							    FlaxRobertaPreLayerNormForTokenClassification,
							    FlaxRobertaPreLayerNormModel,
							)

class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def __init__(	self      :			Tuple	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Tuple=13	,	__lowerCamelCase      :			Any=7	,	__lowerCamelCase      :			Tuple=True	,	__lowerCamelCase      :			List[str]=True	,	__lowerCamelCase      :			List[str]=True	,	__lowerCamelCase      :			Any=True	,	__lowerCamelCase      :			int=99	,	__lowerCamelCase      :			Optional[Any]=32	,	__lowerCamelCase      :			int=5	,	__lowerCamelCase      :			List[str]=4	,	__lowerCamelCase      :			Union[str, Any]=37	,	__lowerCamelCase      :			Any="gelu"	,	__lowerCamelCase      :			Any=0.1	,	__lowerCamelCase      :			Union[str, Any]=0.1	,	__lowerCamelCase      :			Tuple=5_12	,	__lowerCamelCase      :			Any=16	,	__lowerCamelCase      :			Any=2	,	__lowerCamelCase      :			Any=0.02	,	__lowerCamelCase      :			Any=4	,	)      ->				Optional[Any]:
									A							:					Any              =						parent
									A							:					List[str]              =						batch_size
									A							:					List[Any]              =						seq_length
									A							:					Union[str, Any]              =						is_training
									A							:					Union[str, Any]              =						use_attention_mask
									A							:					Dict              =						use_token_type_ids
									A							:					List[str]              =						use_labels
									A							:					Tuple              =						vocab_size
									A							:					str              =						hidden_size
									A							:					Dict              =						num_hidden_layers
									A							:					Optional[Any]              =						num_attention_heads
									A							:					Optional[Any]              =						intermediate_size
									A							:					Union[str, Any]              =						hidden_act
									A							:					Union[str, Any]              =						hidden_dropout_prob
									A							:					List[Any]              =						attention_probs_dropout_prob
									A							:					List[str]              =						max_position_embeddings
									A							:					str              =						type_vocab_size
									A							:					Any              =						type_sequence_label_size
									A							:					Any              =						initializer_range
									A							:					int              =						num_choices







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Dict:
									A							:					str              =						ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size )

									A							:					Optional[int]              =						None
									if self.use_attention_mask:
												A							:					Optional[Any]              =						random_attention_mask([self.batch_size, self.seq_length] )

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

									A							:					str              =						RobertaPreLayerNormConfig(
									    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 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Optional[int]:
									A							:					int              =						self.prepare_config_and_inputs()
									A							:					Dict              =						config_and_inputs
									A							:					List[Any]              =						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
									return config, inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Optional[int]:
									A							:					Dict              =						self.prepare_config_and_inputs()
									A							:					Tuple              =						config_and_inputs

									A							:					Union[str, Any]              =						True
									A							:					List[Any]              =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
									A							:					List[Any]              =						ids_tensor([self.batch_size, self.seq_length]	,	vocab_size=2 )

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







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 	lowerCamelCase_    ( _A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  True

						a__   =  (
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									A							:					int              =						FlaxRobertaPreLayerNormModelTester(self )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									for model_class_name in self.all_model_classes:
												A							:					str              =						model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,	from_pt=__lowerCamelCase )
												A							:					List[str]              =						model(np.ones((1, 1) ) )
												self.assertIsNotNone(__lowerCamelCase )







@require_flax
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Dict:
									A							:					str              =						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,	from_pt=__lowerCamelCase )

									A							:					Tuple              =						np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,	dtype=jnp.intaa )
									A							:					Union[str, Any]              =						model(__lowerCamelCase )[0]
									A							:					Optional[int]              =						[1, 11, 5_02_65]
									self.assertEqual(list(output.shape )	,	__lowerCamelCase )
									# compare the actual values for a slice.
									A							:					Dict              =						np.array(
									    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]	,	dtype=np.floataa )
									self.assertTrue(np.allclose(output[:, :3, :3]	,	__lowerCamelCase	,	atol=1e-4 ) )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Union[str, Any]:
									A							:					str              =						FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,	from_pt=__lowerCamelCase )

									A							:					List[Any]              =						np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,	dtype=jnp.intaa )
									A							:					List[Any]              =						model(__lowerCamelCase )[0]
									# compare the actual values for a slice.
									A							:					List[Any]              =						np.array(
									    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]	,	dtype=np.floataa )
									self.assertTrue(np.allclose(output[:, :3, :3]	,	__lowerCamelCase	,	atol=1e-4 ) )




from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate

@dataclass(frozen=_A						)
class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
						a__   =  field(default="question-answering-extractive"						,metadata={"include_in_asdict_even_if_is_default": True}						)
						a__   =  Features({"question": Value("string"						), "context": Value("string"						)}						)
						a__   =  Features(
						    {
						        "answers": Sequence(
						            {
						                "text": Value("string"						),
						                "answer_start": Value("int32"						),
						            }						)
						    }						)
						a__   =  "question"
						a__   =  "context"
						a__   =  "answers"







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict[str, str]:
									return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}