




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	):

    '''simple docstring'''






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





           '''simple docstring'''

           A__ 					=       parent
           A__ 					=       batch_size
           A__ 					=       seq_length
           A__ 					=       is_training
           A__ 					=       use_attention_mask
           A__ 					=       use_token_type_ids
           A__ 					=       use_labels
           A__ 					=       vocab_size
           A__ 					=       hidden_size
           A__ 					=       num_hidden_layers
           A__ 					=       num_attention_heads
           A__ 					=       intermediate_size
           A__ 					=       hidden_act
           A__ 					=       hidden_dropout_prob
           A__ 					=       attention_probs_dropout_prob
           A__ 					=       max_position_embeddings
           A__ 					=       type_vocab_size
           A__ 					=       type_sequence_label_size
           A__ 					=       initializer_range
           A__ 					=       num_choices


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





           '''simple docstring'''

           A__ 					=       ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size)

           A__ 					=       None
           if self.use_attention_mask:
                  A__ 					=       random_attention_mask([self.batch_size, self.seq_length])

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

           A__ 					=       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 :      Tuple)		->int:





           '''simple docstring'''

           A__ 					=       self.prepare_config_and_inputs()
           A__					,				A__					,				A__					,				A__ 					=       config_and_inputs
           A__ 					=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       self.prepare_config_and_inputs()
           A__					,				A__					,				A__					,				A__ 					=       config_and_inputs

           A__ 					=       True
           A__ 					=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
           A__ 					=       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	):

    '''simple docstring'''






    UpperCAmelCase__				    = True

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


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->Optional[int]:





           '''simple docstring'''

           A__ 					=       FlaxRobertaPreLayerNormModelTester(self)





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





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__ 					=       model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''   ,					from_pt=UpperCAmelCase__)
                  A__ 					=       model(np.ones((1, 1)))
                  self.assertIsNotNone(UpperCAmelCase__)


@require_flax
class 							UpperCamelCase_       (					unittest.TestCase	):

    '''simple docstring'''






    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''   ,					from_pt=UpperCAmelCase__)

           A__ 					=       np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   ,					dtype=jnp.intaa)
           A__ 					=       model(UpperCAmelCase__)[0]
           A__ 					=       [1, 11, 50_265]
           self.assertEqual(list(output.shape)   ,					UpperCAmelCase__)
           # compare the actual values for a slice.
           A__ 					=       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]   ,					UpperCAmelCase__   ,					atol=1e-4))





    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      str)		->Any:





           '''simple docstring'''

           A__ 					=       FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''   ,					from_pt=UpperCAmelCase__)

           A__ 					=       np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]   ,					dtype=jnp.intaa)
           A__ 					=       model(UpperCAmelCase__)[0]
           # compare the actual values for a slice.
           A__ 					=       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]   ,					UpperCAmelCase__   ,					atol=1e-4))






"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch

def 				_snake_case      (   __snake_case							:      List[str]   ):




   """simple docstring"""







   for param in module.parameters():
      _lowerCamelCase	:					Optional[Any]   =		False

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		"""cuda""" if torch.cuda.is_available() else """cpu"""
   if torch.backends.mps.is_available() and torch.backends.mps.is_built():
      _lowerCamelCase	:					Any   =		"""mps"""
   if device == "mps":
      print(
          """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
          """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
          """ with generations."""   )
   return device

def 				_snake_case      (   __snake_case							:      Union[str, Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		plt.imshow(__snake_case   )
   fig.axes.get_xaxis().set_visible(__snake_case   )
   fig.axes.get_yaxis().set_visible(__snake_case   )
   plt.show()





def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		datetime.now()
   _lowerCamelCase	:					Optional[Any]   =		current_time.strftime("""%H:%M:%S"""   )
   return timestamp

