






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 ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=1_3       ,					__lowerCAmelCase=7       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=9_9       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=5       ,					__lowerCAmelCase=4       ,					__lowerCAmelCase=3_7       ,					__lowerCAmelCase="gelu"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=5_1_2       ,					__lowerCAmelCase=1_6       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=4       ,					):
								"""simple docstring"""
								__magic_name__		:str        =      parent
								__magic_name__		:Dict        =      batch_size
								__magic_name__		:Dict        =      seq_length
								__magic_name__		:str        =      is_training
								__magic_name__		:Any        =      use_attention_mask
								__magic_name__		:Dict        =      use_token_type_ids
								__magic_name__		:Any        =      use_labels
								__magic_name__		:Union[str, Any]        =      vocab_size
								__magic_name__		:int        =      hidden_size
								__magic_name__		:Tuple        =      num_hidden_layers
								__magic_name__		:Dict        =      num_attention_heads
								__magic_name__		:Optional[int]        =      intermediate_size
								__magic_name__		:Tuple        =      hidden_act
								__magic_name__		:str        =      hidden_dropout_prob
								__magic_name__		:Union[str, Any]        =      attention_probs_dropout_prob
								__magic_name__		:int        =      max_position_embeddings
								__magic_name__		:List[str]        =      type_vocab_size
								__magic_name__		:Optional[int]        =      type_sequence_label_size
								__magic_name__		:Union[str, Any]        =      initializer_range
								__magic_name__		:int        =      num_choices







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size     )

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

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

								__magic_name__		: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=__lowerCAmelCase       ,					initializer_range=self.initializer_range       ,					)

								return config, input_ids, token_type_ids, attention_mask







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.prepare_config_and_inputs()
								__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Tuple        =      config_and_inputs
								__magic_name__		:Optional[int]        =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      self.prepare_config_and_inputs()
								__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[int]        =      config_and_inputs

								__magic_name__		:List[Any]        =      True
								__magic_name__		:Any        =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
								__magic_name__		:str        =      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_				(      lowerCamelCase			,				unittest.TestCase ):
				a__					       =  True

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







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      FlaxRobertaPreLayerNormModelTester(self     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_class_name in self.all_model_classes:
												__magic_name__		:Any        =      model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,					from_pt=__lowerCAmelCase     )
												__magic_name__		:Optional[int]        =      model(np.ones((1, 1)     )     )
												self.assertIsNotNone(__lowerCAmelCase     )







@require_flax
class 						lowerCamelCase_				(      unittest.TestCase ):







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,					from_pt=__lowerCAmelCase     )

								__magic_name__		:Tuple        =      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       ,					dtype=jnp.intaa     )
								__magic_name__		:Union[str, Any]        =      model(__lowerCAmelCase     )[0]
								__magic_name__		:Tuple        =      [1, 1_1, 5_0_2_6_5]
								self.assertEqual(list(output.shape     )       ,					__lowerCAmelCase     )
								# compare the actual values for a slice.
								__magic_name__		:int        =      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 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,					from_pt=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       ,					dtype=jnp.intaa     )
								__magic_name__		:Optional[int]        =      model(__lowerCAmelCase     )[0]
								# compare the actual values for a slice.
								__magic_name__		: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 typing import List

from .keymap import KEYMAP, get_character

def      __lowercase					( snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:int        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += [key]
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator

def      __lowercase					( *snake_case ):







				"""simple docstring"""






				def decorator(snake_case ):
								__magic_name__		:Union[str, Any]        =      getattr(snake_case,  '''handle_key''',  [] )
								handle += keys
								setattr(snake_case,  '''handle_key''',  snake_case )
								return func

				return decorator
class 						lowerCamelCase_				(      lowerCamelCase ):







				def __new__(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      super().__new__(cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								if not hasattr(__lowerCAmelCase       ,					'''key_handler'''     ):
												setattr(__lowerCAmelCase       ,					'''key_handler'''       ,					{}     )
								setattr(__lowerCAmelCase       ,					'''handle_input'''       ,					KeyHandler.handle_input     )

								for value in attrs.values():
												__magic_name__		:Dict        =      getattr(__lowerCAmelCase       ,					'''handle_key'''       ,					[]     )
												for key in handled_keys:
																__magic_name__		:List[Any]        =      value
								return new_cls







				@staticmethod
				def 			A	(						cls     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      get_character()
								if char != KEYMAP["undefined"]:
												__magic_name__		:Dict        =      ord(__lowerCAmelCase     )
								__magic_name__		:str        =      cls.key_handler.get(__lowerCAmelCase     )
								if handler:
												__magic_name__		:Optional[Any]        =      char
												return handler(cls     )
								else:
												return None







def      __lowercase					( cls ):







				"""simple docstring"""






				return KeyHandler(cls.__name__,  cls.__bases__,  cls.__dict__.copy() )






