





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				:	Optional[int]      						=  {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase				:	str      						=  [
				    '''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

				lowercase				:	Optional[Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









import unittest

import numpy as np

from transformers import BertConfig, 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():
    from transformers.models.bert.modeling_flax_bert import (
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
    )





class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   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=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=4 ,  )      ->						Tuple:







          '''simple docstring'''

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

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

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

          a__						:     Dict			=    None
          if self.use_attention_mask:
                 a__						:     Optional[int]			=    random_attention_mask([self.batch_size, self.seq_length])

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

          a__						:     Dict			=    BertConfig(
              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 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

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

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

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

          a__						:     str			=    True
          a__						:     Any			=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          a__						:     List[str]			=    ids_tensor([self.batch_size, self.seq_length] ,  vocab_size=2)

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )





@require_flax
class 			A__   (							__UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Optional[Any]	  =  True

   __A       :    Tuple	  =  (
       (
           FlaxBertModel,
           FlaxBertForPreTraining,
           FlaxBertForMaskedLM,
           FlaxBertForMultipleChoice,
           FlaxBertForQuestionAnswering,
           FlaxBertForNextSentencePrediction,
           FlaxBertForSequenceClassification,
           FlaxBertForTokenClassification,
           FlaxBertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[Any]			=    FlaxBertModelTester(self)

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







          '''simple docstring'''

          a__						:     Union[str, Any]			=    FlaxBertModel.from_pretrained('bert-base-cased')
          a__						:     Optional[Any]			=    model(np.ones((1, 1)))
          self.assertIsNotNone(lowercase)


