



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

				"""simple docstring"""

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

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

				"""simple docstring"""

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

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

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

				__lowerCAmelCase :    List[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=_SCREAMING_SNAKE_CASE			,     initializer_range=self.initializer_range			,     )

				return config, input_ids, token_type_ids, attention_mask

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

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			self.prepare_config_and_inputs()
				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    Any				=			config_and_inputs
				__lowerCAmelCase :    Optional[int]				=			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







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

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			self.prepare_config_and_inputs()
				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    Any				=			config_and_inputs

				__lowerCAmelCase :    Optional[Any]				=			True
				__lowerCAmelCase :    Dict				=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
				__lowerCAmelCase :    Union[str, 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      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])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			FlaxRobertaPreLayerNormModelTester(self)







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			List[Any]:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
							__lowerCAmelCase :    Optional[int]				=			model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"			,     from_pt=_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    str				=			model(np.ones((1, 1)))
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)






@require_flax
class      A__  (    unittest.TestCase				):




	'''simple docstring'''





	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			int:

				"""simple docstring"""

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

				__lowerCAmelCase :    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 :    Any				=			[1, 11, 5_0265]
				self.assertEqual(list(output.shape)			,     _SCREAMING_SNAKE_CASE)
				# compare the actual values for a slice.
				__lowerCAmelCase :    List[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:						Any)							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"			,     from_pt=_SCREAMING_SNAKE_CASE)

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






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case				:  Tuple =       {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       [
	    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AutoformerForPrediction',
	    'AutoformerModel',
	    'AutoformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	__snake_case				:  Optional[int] =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)