"""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    __snake_case       (     unittest.TestCase				):



      def __init__(						self    ,			lowercase    ,			lowercase=13    ,			lowercase=7    ,			lowercase=True    ,			lowercase=True    ,			lowercase=True    ,			lowercase=True    ,			lowercase=99    ,			lowercase=32    ,			lowercase=5    ,			lowercase=4    ,			lowercase=37    ,			lowercase="gelu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=5_12    ,			lowercase=16    ,			lowercase=2    ,			lowercase=0.02    ,			lowercase=4    ,			)	->			Tuple:





        '''simple docstring'''





        a__:     Any										=	parent
        a__:     int										=	batch_size
        a__:     str										=	seq_length
        a__:     Union[str, Any]										=	is_training
        a__:     str										=	use_attention_mask
        a__:     Tuple										=	use_token_type_ids
        a__:     List[Any]										=	use_labels
        a__:     str										=	vocab_size
        a__:     int										=	hidden_size
        a__:     Tuple										=	num_hidden_layers
        a__:     Any										=	num_attention_heads
        a__:     Optional[int]										=	intermediate_size
        a__:     List[str]										=	hidden_act
        a__:     List[str]										=	hidden_dropout_prob
        a__:     Optional[Any]										=	attention_probs_dropout_prob
        a__:     List[Any]										=	max_position_embeddings
        a__:     Dict										=	type_vocab_size
        a__:     Optional[Any]										=	type_sequence_label_size
        a__:     List[str]										=	initializer_range
        a__:     Tuple										=	num_choices



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     int										=	ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

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

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

        a__:     Dict										=	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=lowercase    ,			initializer_range=self.initializer_range    ,			)

        return config, input_ids, token_type_ids, attention_mask



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





        '''simple docstring'''





        a__:     Optional[Any]										=	self.prepare_config_and_inputs()
        a__							,    a__							,    a__							,    a__:     Tuple										=	config_and_inputs
        a__:     Any										=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
        return config, inputs_dict



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Optional[int]										=	self.prepare_config_and_inputs()
        a__							,    a__							,    a__							,    a__:     Any										=	config_and_inputs

        a__:     Optional[int]										=	True
        a__:     List[Any]										=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        a__:     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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		True

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



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Optional[Any]										=	FlaxRobertaPreLayerNormModelTester(self)



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





        '''simple docstring'''





        for model_class_name in self.all_model_classes:
          a__:     List[str]										=	model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,			from_pt=lowercase)
          a__:     List[str]										=	model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)







@require_flax
class    __snake_case       (     unittest.TestCase				):



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     List[str]										=	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,			from_pt=lowercase)

        a__:     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)
        a__:     Optional[int]										=	model(lowercase)[0]
        a__:     Dict										=	[1, 11, 5_02_65]
        self.assertEqual(list(output.shape)    ,			lowercase)
        # compare the actual values for a slice.
        a__:     Optional[int]										=	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]    ,			lowercase    ,			atol=1e-4))



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





        '''simple docstring'''





        a__:     Dict										=	FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'    ,			from_pt=lowercase)

        a__:     Dict										=	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)
        a__:     Union[str, Any]										=	model(lowercase)[0]
        # compare the actual values for a slice.
        a__:     str										=	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]    ,			lowercase    ,			atol=1e-4))



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""audio-spectrogram-transformer"""



      def __init__(						self    ,			lowercase=7_68    ,			lowercase=12    ,			lowercase=12    ,			lowercase=30_72    ,			lowercase="gelu"    ,			lowercase=0.0    ,			lowercase=0.0    ,			lowercase=0.02    ,			lowercase=1e-12    ,			lowercase=16    ,			lowercase=True    ,			lowercase=10    ,			lowercase=10    ,			lowercase=10_24    ,			lowercase=1_28    ,			**lowercase    ,			)	->			str:





        '''simple docstring'''





        super().__init__(**lowercase)

        a__:     Any										=	hidden_size
        a__:     int										=	num_hidden_layers
        a__:     Union[str, Any]										=	num_attention_heads
        a__:     Any										=	intermediate_size
        a__:     Union[str, Any]										=	hidden_act
        a__:     int										=	hidden_dropout_prob
        a__:     str										=	attention_probs_dropout_prob
        a__:     str										=	initializer_range
        a__:     Tuple										=	layer_norm_eps
        a__:     Any										=	patch_size
        a__:     int										=	qkv_bias
        a__:     Optional[Any]										=	frequency_stride
        a__:     int										=	time_stride
        a__:     List[str]										=	max_length
        a__:     Tuple										=	num_mel_bins


