
"""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 			UpperCamelCase      (							unittest.TestCase  ):
     def __init__(     self  ,     snake_case__  ,     snake_case__=13  ,     snake_case__=7  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=99  ,     snake_case__=32  ,     snake_case__=5  ,     snake_case__=4  ,     snake_case__=37  ,     snake_case__="gelu"  ,     snake_case__=0.1  ,     snake_case__=0.1  ,     snake_case__=512  ,     snake_case__=16  ,     snake_case__=2  ,     snake_case__=0.02  ,     snake_case__=4  ,     ):




       """simple docstring"""


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




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

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

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

       _SCREAMING_SNAKE_CASE   :				int							    = 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=_UpperCAmelCase  ,     initializer_range=self.initializer_range  ,     )

       return config, input_ids, token_type_ids, attention_mask
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.prepare_config_and_inputs()
       _SCREAMING_SNAKE_CASE   :				str							    = config_and_inputs
       _SCREAMING_SNAKE_CASE   :				List[str]							    = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict




     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self.prepare_config_and_inputs()
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = config_and_inputs

       _SCREAMING_SNAKE_CASE   :				List[str]							    = True
       _SCREAMING_SNAKE_CASE   :				List[str]							    = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
       _SCREAMING_SNAKE_CASE   :				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 			UpperCamelCase      (							_UpperCamelCase      ,       unittest.TestCase  ):
     A__      =			True

     A__      =			(
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = FlaxRobertaPreLayerNormModelTester(self				)




     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_class_name in self.all_model_classes:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,     from_pt=_UpperCAmelCase				)
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = model(np.ones((1, 1)				)				)
         self.assertIsNotNone(_UpperCAmelCase				)
@require_flax
class 			UpperCamelCase      (							unittest.TestCase  ):
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,     dtype=jnp.intaa				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = model(_UpperCAmelCase				)[0]
       _SCREAMING_SNAKE_CASE   :				Any							    = [1, 11, 50265]
       self.assertEqual(list(output.shape				)  ,     _UpperCAmelCase				)
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				int							    = np.array(
           [[[40.4880, 18.0199, -5.2_367], [-1.8_877, -4.0_885, 10.7085], [-2.2_613, -5.6_110, 7.2_665]]]  ,     dtype=np.floataa				)
       self.assertTrue(np.allclose(output[:, :3, :3]  ,     _UpperCAmelCase  ,     atol=1E-4				)				)




     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE   :				str							    = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,     dtype=jnp.intaa				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model(_UpperCAmelCase				)[0]
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = np.array(
           [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]  ,     dtype=np.floataa				)
       self.assertTrue(np.allclose(output[:, :3, :3]  ,     _UpperCAmelCase  ,     atol=1E-4				)				)





"""simple docstring"""
from typing import List

from .keymap import KEYMAP, get_character





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Optional[int]:

  def decorator(lowerCamelCase__							:							int				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += [key]
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator





def    _lowerCAmelCase    (     *lowerCamelCase__							:							List[str]				)  ->    Tuple:

  def decorator(lowerCamelCase__							:							Dict				):
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += keys
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __new__(     cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = super().__new__(cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       if not hasattr(snake_case__  ,     "key_handler"				):
         setattr(snake_case__  ,     "key_handler"  ,     {}				)
       setattr(snake_case__  ,     "handle_input"  ,     KeyHandler.handle_input				)

       for value in attrs.values():
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = getattr(snake_case__  ,     "handle_key"  ,     []				)
         for key in handled_keys:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = value
       return new_cls




     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = get_character()
       if char != KEYMAP["undefined"]:
         _SCREAMING_SNAKE_CASE   :				Dict							    = ord(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = cls.key_handler.get(snake_case__				)
       if handler:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = char
         return handler(cls				)
       else:
         return None





def    _lowerCAmelCase    (     cls							:							List[Any]				)  ->    str:
  return KeyHandler(cls.__name__,						cls.__bases__,						cls.__dict__.copy()				)



