




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




    '''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 							__magic_name__							(   self						:	Any				)				->							List[Any]:




    '''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=snake_case_     ,			initializer_range=self.initializer_range     ,			)

    return config, input_ids, token_type_ids, attention_mask




   def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




    '''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 							__magic_name__							(   self						:	Optional[Any]				)				->							int:




    '''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_  (						A_,       unittest.TestCase							):
   lowercase__						 =				True

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




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




    '''simple docstring'''

    A__	=    FlaxRobertaPreLayerNormModelTester(self				)




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




    '''simple docstring'''

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






@require_flax
class      UpperCAmelCase_  (						unittest.TestCase							):




   @slow
   def 							__magic_name__							(   self						:	List[str]				)				->							List[Any]:




    '''simple docstring'''

    A__	=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,			from_pt=snake_case_				)

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




   @slow
   def 							__magic_name__							(   self						:	Tuple				)				->							List[str]:




    '''simple docstring'''

    A__	=    FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,			from_pt=snake_case_				)

    A__	=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,			dtype=jnp.intaa				)
    A__	=    model(snake_case_				)[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]     ,			snake_case_     ,			atol=1e-4				)				)











"""simple docstring"""



SCREAMING_SNAKE_CASE    =	"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

SCREAMING_SNAKE_CASE    =	[{"type": "code", "content": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE    =	{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





