


"""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 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=13 ,     lowerCamelCase__=7 ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=True ,     lowerCamelCase__=99 ,     lowerCamelCase__=32 ,     lowerCamelCase__=5 ,     lowerCamelCase__=4 ,     lowerCamelCase__=37 ,     lowerCamelCase__="gelu" ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=0.1 ,     lowerCamelCase__=512 ,     lowerCamelCase__=16 ,     lowerCamelCase__=2 ,     lowerCamelCase__=0.02 ,     lowerCamelCase__=4 ,     )      ->							Optional[int]:
    lowercase__       :       Dict				=						parent
    lowercase__       :       Tuple				=						batch_size
    lowercase__       :       List[Any]				=						seq_length
    lowercase__       :       Union[str, Any]				=						is_training
    lowercase__       :       int				=						use_attention_mask
    lowercase__       :       Dict				=						use_token_type_ids
    lowercase__       :       int				=						use_labels
    lowercase__       :       Dict				=						vocab_size
    lowercase__       :       List[Any]				=						hidden_size
    lowercase__       :       Any				=						num_hidden_layers
    lowercase__       :       Optional[int]				=						num_attention_heads
    lowercase__       :       int				=						intermediate_size
    lowercase__       :       str				=						hidden_act
    lowercase__       :       List[Any]				=						hidden_dropout_prob
    lowercase__       :       Tuple				=						attention_probs_dropout_prob
    lowercase__       :       Tuple				=						max_position_embeddings
    lowercase__       :       List[str]				=						type_vocab_size
    lowercase__       :       List[str]				=						type_sequence_label_size
    lowercase__       :       str				=						initializer_range
    lowercase__       :       Any				=						num_choices


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       List[Any]				=						ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

    lowercase__       :       Dict				=						None
    if self.use_attention_mask:
     lowercase__       :       Tuple				=						random_attention_mask([self.batch_size, self.seq_length]						)

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

    lowercase__       :       Tuple				=						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 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       str				=						self.prepare_config_and_inputs()
    lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       str				=						config_and_inputs
    lowercase__       :       Any				=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
    return config, inputs_dict







   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       Any				=						self.prepare_config_and_inputs()
    lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       Tuple				=						config_and_inputs

    lowercase__       :       List[Any]				=						True
    lowercase__       :       Optional[Any]				=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
    lowercase__       :       Dict				=						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 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Any									=    True

   _a   :       Dict									=    (
       (
           FlaxRobertaPreLayerNormModel,
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       List[str]				=						FlaxRobertaPreLayerNormModelTester(self						)







   @slow
   def 					UpperCAmelCase__(						self						)      ->							int:
    for model_class_name in self.all_model_classes:
     lowercase__       :       Optional[Any]				=						model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,     from_pt=lowerCamelCase__						)
     lowercase__       :       List[Any]				=						model(np.ones((1, 1)						)						)
     self.assertIsNotNone(lowerCamelCase__						)

@require_flax
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       List[Any]				=						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,     from_pt=lowerCamelCase__						)

    lowercase__       :       Any				=						np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,     dtype=jnp.intaa						)
    lowercase__       :       List[str]				=						model(lowerCamelCase__						)[0]
    lowercase__       :       Any				=						[1, 11, 5_0265]
    self.assertEqual(list(output.shape						) ,     lowerCamelCase__						)
    # compare the actual values for a slice.
    lowercase__       :       Dict				=						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 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Any				=						FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40""" ,     from_pt=lowerCamelCase__						)

    lowercase__       :       Dict				=						np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,     dtype=jnp.intaa						)
    lowercase__       :       str				=						model(lowerCamelCase__						)[0]
    # compare the actual values for a slice.
    lowercase__       :       Optional[int]				=						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						)						)



"""simple docstring"""


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case    					=					{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case    					=					[
       'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'InformerForPrediction',
       'InformerModel',
       'InformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   __snake_case    					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)