

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size)

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

									SCREAMING_SNAKE_CASE_: List[Any]  		=       None
									if self.use_token_type_ids:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size)

									SCREAMING_SNAKE_CASE_: 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=lowerCAmelCase__   ,						initializer_range=self.initializer_range   ,						)

									return config, input_ids, token_type_ids, attention_mask



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       config_and_inputs
									SCREAMING_SNAKE_CASE_: List[str]  		=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
									return config, inputs_dict




			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       config_and_inputs

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       True
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
									SCREAMING_SNAKE_CASE_: List[Any]  		=       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				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							int										=     True

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       FlaxRobertaPreLayerNormModelTester(self)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									for model_class_name in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: str  		=       model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,						from_pt=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: List[Any]  		=       model(np.ones((1, 1)))
															self.assertIsNotNone(lowerCAmelCase__)







@require_flax
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,						from_pt=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   ,						dtype=jnp.intaa)
									SCREAMING_SNAKE_CASE_: Dict  		=       model(lowerCAmelCase__)[0]
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_: List[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]   ,						lowerCAmelCase__   ,						atol=1E-4))









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]   	=						{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any   	=						[
						    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """WavLMForAudioFrameClassification""",
						    """WavLMForCTC""",
						    """WavLMForSequenceClassification""",
						    """WavLMForXVector""",
						    """WavLMModel""",
						    """WavLMPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase							:  Union[str, Any]   	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






