


'''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  ):





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=13			,     SCREAMING_SNAKE_CASE_=7			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=99			,     SCREAMING_SNAKE_CASE_=32			,     SCREAMING_SNAKE_CASE_=5			,     SCREAMING_SNAKE_CASE_=4			,     SCREAMING_SNAKE_CASE_=37			,     SCREAMING_SNAKE_CASE_="gelu"			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=5_12			,     SCREAMING_SNAKE_CASE_=16			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=0.0_2			,     SCREAMING_SNAKE_CASE_=4			,     )				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[Any]								=		parent
         __lowerCamelCase   :	Optional[int]								=		batch_size
         __lowerCamelCase   :	Union[str, Any]								=		seq_length
         __lowerCamelCase   :	int								=		is_training
         __lowerCamelCase   :	Optional[int]								=		use_attention_mask
         __lowerCamelCase   :	Optional[int]								=		use_token_type_ids
         __lowerCamelCase   :	List[str]								=		use_labels
         __lowerCamelCase   :	Dict								=		vocab_size
         __lowerCamelCase   :	Tuple								=		hidden_size
         __lowerCamelCase   :	Union[str, Any]								=		num_hidden_layers
         __lowerCamelCase   :	List[str]								=		num_attention_heads
         __lowerCamelCase   :	Optional[int]								=		intermediate_size
         __lowerCamelCase   :	Tuple								=		hidden_act
         __lowerCamelCase   :	List[str]								=		hidden_dropout_prob
         __lowerCamelCase   :	Optional[Any]								=		attention_probs_dropout_prob
         __lowerCamelCase   :	Union[str, Any]								=		max_position_embeddings
         __lowerCamelCase   :	Optional[Any]								=		type_vocab_size
         __lowerCamelCase   :	Tuple								=		type_sequence_label_size
         __lowerCamelCase   :	List[Any]								=		initializer_range
         __lowerCamelCase   :	Any								=		num_choices







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Optional[Any]								=		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size	)

         __lowerCamelCase   :	Union[str, Any]								=		None
         if self.use_attention_mask:
              __lowerCamelCase   :	Union[str, Any]								=		random_attention_mask([self.batch_size, self.seq_length]	)

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

         __lowerCamelCase   :	List[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=SCREAMING_SNAKE_CASE_			,     initializer_range=self.initializer_range			,     )

         return config, input_ids, token_type_ids, attention_mask







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	Dict								=		self.prepare_config_and_inputs()
         __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		config_and_inputs
         __lowerCamelCase   :	List[str]								=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	List[str]								=		self.prepare_config_and_inputs()
         __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	List[Any]								=		config_and_inputs

         __lowerCamelCase   :	Optional[Any]								=		True
         __lowerCamelCase   :	Optional[Any]								=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
         __lowerCamelCase   :	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    UpperCAmelCase_  (_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					List[Any]							     =			True

    lowerCamelCase  :					List[Any]							     =			(
        (
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Dict								=		FlaxRobertaPreLayerNormModelTester(self	)







    @slow
    def       lowercase_				(							self	)				->     Any:
         for model_class_name in self.all_model_classes:
              __lowerCamelCase   :	Union[str, Any]								=		model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,     from_pt=SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	Any								=		model(np.ones((1, 1)	)	)
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







@require_flax
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @slow
    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	List[Any]								=		FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,     from_pt=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[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	)
         __lowerCamelCase   :	Union[str, Any]								=		model(SCREAMING_SNAKE_CASE_	)[0]
         __lowerCamelCase   :	Dict								=		[1, 11, 5_02_65]
         self.assertEqual(list(output.shape	)			,     SCREAMING_SNAKE_CASE_	)
         # compare the actual values for a slice.
         __lowerCamelCase   :	List[Any]								=		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]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)







    @slow
    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase   :	int								=		FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,     from_pt=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Union[str, 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	)
         __lowerCamelCase   :	Dict								=		model(SCREAMING_SNAKE_CASE_	)[0]
         # compare the actual values for a slice.
         __lowerCamelCase   :	List[Any]								=		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]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)










'''simple docstring'''



from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
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    bert_japanese,
    bertweet,
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    blenderbot_small,
    blip,
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    canine,
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    clap,
    clip,
    clipseg,
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    conditional_detr,
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    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
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    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)






