



"""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     A__     (						unittest.TestCase							):




	'''simple docstring'''





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

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size)

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

				__lowerCAmelCase   :						Optional[int]				= None
				if self.use_token_type_ids:
							__lowerCAmelCase   :						Optional[Any]				= ids_tensor([self.batch_size, self.seq_length] ,    self.type_vocab_size)

				__lowerCAmelCase   :						Union[str, Any]				= 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=_SCREAMING_SNAKE_CASE ,    initializer_range=self.initializer_range ,    )

				return config, input_ids, token_type_ids, attention_mask

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= self.prepare_config_and_inputs()
				__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= config_and_inputs
				__lowerCAmelCase   :						Union[str, 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[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= self.prepare_config_and_inputs()
				__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Union[str, Any]				= config_and_inputs

				__lowerCAmelCase   :						int				= True
				__lowerCAmelCase   :						str				= floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
				__lowerCAmelCase   :						Tuple				= 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     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			True

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

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

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= FlaxRobertaPreLayerNormModelTester(self)







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Union[str, Any]:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
							__lowerCAmelCase   :						Dict				= model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" ,    from_pt=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Any				= model(np.ones((1, 1)))
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)






@require_flax
class     A__     (						unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" ,    from_pt=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,    dtype=jnp.intaa)
				__lowerCAmelCase   :						int				= model(_SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase   :						List[Any]				= [1, 11, 5_0265]
				self.assertEqual(list(output.shape) ,    _SCREAMING_SNAKE_CASE)
				# compare the actual values for a slice.
				__lowerCAmelCase   :						Union[str, Any]				= 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] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-4))







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" ,    from_pt=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[Any]				= np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,    dtype=jnp.intaa)
				__lowerCAmelCase   :						Optional[Any]				= model(_SCREAMING_SNAKE_CASE)[0]
				# compare the actual values for a slice.
				__lowerCAmelCase   :						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] ,    _SCREAMING_SNAKE_CASE ,    atol=1e-4))




"""simple docstring"""






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__snake_case							:     str   =					pytest.mark.integration


@pytest.mark.parametrize("path"      ,["paws", "csv"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			inspect_dataset(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"			)
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"			)
@pytest.mark.parametrize("path"      ,["accuracy"]			)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			inspect_metric(__snake_case      ,__snake_case			)
			__lowerCAmelCase   :						str				= path + ".py"
			assert script_name in os.listdir(__snake_case			)
			assert "__pycache__" not in os.listdir(__snake_case			)


@pytest.mark.parametrize(
    "path, config_name, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[int]:
			__lowerCAmelCase   :						str				= get_dataset_config_info(__snake_case      ,config_name=__snake_case			)
			assert info.config_name == config_name
			assert list(info.splits.keys()			) == expected_splits


@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     List[str]:
			with pytest.raises(__snake_case			):
						get_dataset_config_info(__snake_case      ,config_name=__snake_case			)


@pytest.mark.parametrize(
    "path, expected"      ,[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						Dict				= get_dataset_config_names(__snake_case			)
			assert expected in config_names


@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"      ,[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Optional[Any]:
			__lowerCAmelCase   :						List[Any]				= get_dataset_infos(__snake_case			)
			assert list(infos.keys()			) == expected_configs
			__lowerCAmelCase   :						List[Any]				= expected_configs[0]
			assert expected_config in infos
			__lowerCAmelCase   :						Tuple				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits_in_first_config


@pytest.mark.parametrize(
    "path, expected_config, expected_splits"      ,[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Union[str, Any]:
			__lowerCAmelCase   :						Tuple				= get_dataset_infos(__snake_case			)
			assert expected_config in infos
			__lowerCAmelCase   :						int				= infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()			) == expected_splits





@pytest.mark.parametrize(
    "path, config_name, expected_exception"      ,[
        ("paws", None, ValueError),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
			with pytest.raises(__snake_case			):
						get_dataset_split_names(__snake_case      ,config_name=__snake_case			)