






'''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 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    def __init__(					self     :     List[str]				,     __lowerCamelCase     :     Any				,     __lowerCamelCase     :     Dict=13				,     __lowerCamelCase     :     List[Any]=7				,     __lowerCamelCase     :     str=True				,     __lowerCamelCase     :     Dict=True				,     __lowerCamelCase     :     int=True				,     __lowerCamelCase     :     Optional[Any]=True				,     __lowerCamelCase     :     Tuple=99				,     __lowerCamelCase     :     Optional[int]=32				,     __lowerCamelCase     :     str=5				,     __lowerCamelCase     :     Dict=4				,     __lowerCamelCase     :     str=37				,     __lowerCamelCase     :     Dict="gelu"				,     __lowerCamelCase     :     Optional[int]=0.1				,     __lowerCamelCase     :     Tuple=0.1				,     __lowerCamelCase     :     Dict=512				,     __lowerCamelCase     :     Tuple=16				,     __lowerCamelCase     :     str=2				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     Union[str, Any]=4				,     )							->							Tuple:

     '''simple docstring'''




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

    def    a__				(					self     :     Dict     )							->							Tuple:

     '''simple docstring'''




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

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

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

     lowerCamelCase__  =     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    a__				(					self     :     List[Any]     )							->							Tuple:

     '''simple docstring'''




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




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

     '''simple docstring'''




     lowerCamelCase__  =     self.prepare_config_and_inputs()
     lowerCamelCase__  =     config_and_inputs

     lowerCamelCase__  =     True
     lowerCamelCase__  =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
     lowerCamelCase__  =     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 				lowercase       (							__lowerCAmelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							True

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

    def    a__				(					self     :     int     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     FlaxRobertaPreLayerNormModelTester(self     )




    @slow
    def    a__				(					self     :     int     )							->							List[str]:

     '''simple docstring'''




     for model_class_name in self.all_model_classes:
      lowerCamelCase__  =     model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"				,     from_pt=lowerCamelCase__     )
      lowerCamelCase__  =     model(np.ones((1, 1)     )     )
      self.assertIsNotNone(lowerCamelCase__     )
@require_flax
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    @slow
    def    a__				(					self     :     str     )							->							str:

     '''simple docstring'''




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

     lowerCamelCase__  =     np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,     dtype=jnp.intaa     )
     lowerCamelCase__  =     model(lowerCamelCase__     )[0]
     lowerCamelCase__  =     [1, 11, 50265]
     self.assertEqual(list(output.shape     )				,     lowerCamelCase__     )
     # compare the actual values for a slice.
     lowerCamelCase__  =     np.array(
         [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,     dtype=np.floataa     )
     self.assertTrue(np.allclose(output[:, :3, :3]				,     lowerCamelCase__				,     atol=1E-4     )     )




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

     '''simple docstring'''




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

     lowerCamelCase__  =     np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,     dtype=jnp.intaa     )
     lowerCamelCase__  =     model(lowerCamelCase__     )[0]
     # compare the actual values for a slice.
     lowerCamelCase__  =     np.array(
         [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,     dtype=np.floataa     )
     self.assertTrue(np.allclose(output[:, :3, :3]				,     lowerCamelCase__				,     atol=1E-4     )     )










'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A							:							Optional[Any]  							=		logging.get_logger(__name__)

__A							:							int  							=		{
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"vit"

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     Tuple=768				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     int=3072				,     __lowerCamelCase     :     Optional[Any]="gelu"				,     __lowerCamelCase     :     Optional[int]=0.0				,     __lowerCamelCase     :     Tuple=0.0				,     __lowerCamelCase     :     Optional[Any]=0.0_2				,     __lowerCamelCase     :     int=1E-12				,     __lowerCamelCase     :     Tuple=224				,     __lowerCamelCase     :     int=16				,     __lowerCamelCase     :     Optional[int]=3				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     str=16				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     patch_size
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     qkv_bias
     lowerCamelCase__  =     encoder_stride
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

    @property
    def    a__				(					self     :     Optional[int]     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]     )




    @property
    def    a__				(					self     :     Optional[int]     )							->							float:

     '''simple docstring'''




     return 1E-4

