




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






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:Optional[int]							=  ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

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

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

         lowercase			: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     SCREAMING_SNAKE_CASE			(      self:    int ):
         lowercase			:Tuple							=  self.prepare_config_and_inputs()
         lowercase		,			lowercase		,			lowercase		,			lowercase			:List[Any]							=  config_and_inputs
         lowercase			: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:    int ):
         lowercase			:List[Any]							=  self.prepare_config_and_inputs()
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  config_and_inputs

         lowercase			:Dict							=  True
         lowercase			:Optional[Any]							=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
         lowercase			: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     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:Optional[int]							=  FlaxRobertaPreLayerNormModelTester(self )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         for model_class_name in self.all_model_classes:
           lowercase			:Optional[int]							=  model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"				,    from_pt=_lowerCAmelCase )
           lowercase			:Any							=  model(np.ones((1, 1) ) )
           self.assertIsNotNone(_lowerCAmelCase )







@require_flax
class       __lowerCAmelCase				(							unittest.TestCase):






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:int							=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"				,    from_pt=_lowerCAmelCase )

         lowercase			:str							=  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 )
         lowercase			:int							=  model(_lowerCAmelCase )[0]
         lowercase			:List[Any]							=  [1, 11, 5_02_65]
         self.assertEqual(list(output.shape )				,    _lowerCAmelCase )
         # compare the actual values for a slice.
         lowercase			:Any							=  np.array(
             [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]				,    dtype=np.floataa )
         self.assertTrue(np.allclose(output[:, :3, :3]				,    _lowerCAmelCase				,    atol=1e-4 ) )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:Dict							=  FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"				,    from_pt=_lowerCAmelCase )

         lowercase			: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 )
         lowercase			:List[str]							=  model(_lowerCAmelCase )[0]
         # compare the actual values for a slice.
         lowercase			:Any							=  np.array(
             [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]				,    dtype=np.floataa )
         self.assertTrue(np.allclose(output[:, :3, :3]				,    _lowerCAmelCase				,    atol=1e-4 ) )









import os

import pytest
from attr import dataclass


_UpperCAmelCase		:  List[str]	=   "us-east-1"  # defaults region




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
       _a           =  {
           '''task_name''': '''mnli''',
           '''per_device_train_batch_size''': 16,
           '''per_device_eval_batch_size''': 16,
           '''do_train''': True,
           '''do_eval''': True,
           '''do_predict''': True,
           '''output_dir''': '''/opt/ml/model''',
           '''overwrite_output_dir''': True,
           '''max_steps''': 500,
           '''save_steps''': 5500,
       }
       _a           =  {**hyperparameters, '''max_steps''': 1000}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if self.framework == "pytorch":
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
               {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
           ]
         else:
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
               {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
           ]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return F"{self.framework}-transfromers-test"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return F"./tests/sagemaker/scripts/{self.framework}"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         if self.framework == "pytorch":
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
         else:
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"      )
def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Union[str, Any]							=  SageMakerTestEnvironment(framework=request.cls.framework      )



