



from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
 from transformers.models.albert.modeling_tf_albert import (
     TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFAlbertForMaskedLM,
     TFAlbertForMultipleChoice,
     TFAlbertForPreTraining,
     TFAlbertForQuestionAnswering,
     TFAlbertForSequenceClassification,
     TFAlbertForTokenClassification,
     TFAlbertModel,
 )
class        UpperCAmelCase__  :



 def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=9_9		,	lowercase=1_6		,	lowercase=3_2		,	lowercase=2		,	lowercase=4		,	lowercase=3_7		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=5_1_2		,	lowercase=1_6		,	lowercase=2		,	lowercase=0.02		,	lowercase=3		,	lowercase=4		,	lowercase=None		,	)  ->							Tuple:
  __UpperCamelCase				      =  parent
  __UpperCamelCase				      =  1_3
  __UpperCamelCase				      =  7
  __UpperCamelCase				      =  True
  __UpperCamelCase				      =  True
  __UpperCamelCase				      =  True
  __UpperCamelCase				      =  True
  __UpperCamelCase				      =  9_9
  __UpperCamelCase				      =  1_6
  __UpperCamelCase				      =  3_2
  __UpperCamelCase				      =  2
  __UpperCamelCase				      =  4
  __UpperCamelCase				      =  3_7
  __UpperCamelCase				      =  """gelu"""
  __UpperCamelCase				      =  0.1
  __UpperCamelCase				      =  0.1
  __UpperCamelCase				      =  5_1_2
  __UpperCamelCase				      =  1_6
  __UpperCamelCase				      =  2
  __UpperCamelCase				      =  0.02
  __UpperCamelCase				      =  3
  __UpperCamelCase				      =  4
  __UpperCamelCase				      =  None



 def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			)

  __UpperCamelCase				      =  None
  if self.use_input_mask:
   __UpperCamelCase				      =  random_attention_mask([self.batch_size, self.seq_length]			)

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

  __UpperCamelCase				      =  None
  __UpperCamelCase				      =  None
  __UpperCamelCase				      =  None
  if self.use_labels:
   __UpperCamelCase				      =  ids_tensor([self.batch_size]		,	self.type_sequence_label_size			)
   __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.num_labels			)
   __UpperCamelCase				      =  ids_tensor([self.batch_size]		,	self.num_choices			)

  __UpperCamelCase				      =  AlbertConfig(
      vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	embedding_size=self.embedding_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		,	initializer_range=self.initializer_range		,	)

  return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							int:
  __UpperCamelCase				      =  TFAlbertModel(config=lowercase			)
  # inputs = {'input_ids': input_ids,
  #           'attention_mask': input_mask,
  #           'token_type_ids': token_type_ids}
  # sequence_output, pooled_output = model(**inputs)
  __UpperCamelCase				      =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  __UpperCamelCase				      =  model(lowercase			)

  __UpperCamelCase				      =  [input_ids, input_mask]
  __UpperCamelCase				      =  model(lowercase			)

  __UpperCamelCase				      =  model(lowercase			)

  self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)			)
  self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Union[str, Any]:
  __UpperCamelCase				      =  self.num_labels
  __UpperCamelCase				      =  TFAlbertForPreTraining(config=lowercase			)
  __UpperCamelCase				      =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.prediction_logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)			)
  self.parent.assertEqual(result.sop_logits.shape		,	(self.batch_size, self.num_labels)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							int:
  __UpperCamelCase				      =  TFAlbertForMaskedLM(config=lowercase			)
  __UpperCamelCase				      =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Union[str, Any]:
  __UpperCamelCase				      =  self.num_labels
  __UpperCamelCase				      =  TFAlbertForSequenceClassification(config=lowercase			)
  __UpperCamelCase				      =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Optional[Any]:
  __UpperCamelCase				      =  TFAlbertForQuestionAnswering(config=lowercase			)
  __UpperCamelCase				      =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertEqual(result.start_logits.shape		,	(self.batch_size, self.seq_length)			)
  self.parent.assertEqual(result.end_logits.shape		,	(self.batch_size, self.seq_length)			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Union[str, Any]:
  __UpperCamelCase				      =  self.num_choices
  __UpperCamelCase				      =  TFAlbertForMultipleChoice(config=lowercase			)
  __UpperCamelCase				      =  tf.tile(tf.expand_dims(lowercase		,	1			)		,	(1, self.num_choices, 1)			)
  __UpperCamelCase				      =  tf.tile(tf.expand_dims(lowercase		,	1			)		,	(1, self.num_choices, 1)			)
  __UpperCamelCase				      =  tf.tile(tf.expand_dims(lowercase		,	1			)		,	(1, self.num_choices, 1)			)
  __UpperCamelCase				      =  {
      """input_ids""": multiple_choice_inputs_ids,
      """attention_mask""": multiple_choice_input_mask,
      """token_type_ids""": multiple_choice_token_type_ids,
  }
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertListEqual(list(result["""logits"""].shape			)		,	[self.batch_size, self.num_choices]			)



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Optional[Any]:
  __UpperCamelCase				      =  self.num_labels
  __UpperCamelCase				      =  TFAlbertForTokenClassification(config=lowercase			)
  __UpperCamelCase				      =  {
      """input_ids""": input_ids,
      """attention_mask""": input_mask,
      """token_type_ids""": token_type_ids,
  }
  __UpperCamelCase				      =  model(lowercase			)
  self.parent.assertListEqual(list(result["""logits"""].shape			)		,	[self.batch_size, self.seq_length, self.num_labels]			)




 def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  __UpperCamelCase				      =  self.prepare_config_and_inputs()
  (
      (
      __UpperCamelCase		
  )      ,		(
      __UpperCamelCase		
  )      ,		(
      __UpperCamelCase		
  )      ,		(
      __UpperCamelCase		
  )      ,		(
      __UpperCamelCase		
  )      ,		(
      __UpperCamelCase		
  )      ,		(
      __UpperCamelCase		
  )      ,				
  )				      =  config_and_inputs
  __UpperCamelCase				      =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
  return config, inputs_dict


@require_tf
class        UpperCAmelCase__  (    UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				(
     (
         TFAlbertModel,
         TFAlbertForPreTraining,
         TFAlbertForMaskedLM,
         TFAlbertForSequenceClassification,
         TFAlbertForQuestionAnswering,
         TFAlbertForTokenClassification,
         TFAlbertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 __SCREAMING_SNAKE_CASE					      =				(
     {
         '''feature-extraction''': TFAlbertModel,
         '''fill-mask''': TFAlbertForMaskedLM,
         '''question-answering''': TFAlbertForQuestionAnswering,
         '''text-classification''': TFAlbertForSequenceClassification,
         '''token-classification''': TFAlbertForTokenClassification,
         '''zero-shot''': TFAlbertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __SCREAMING_SNAKE_CASE					      =				False
 __SCREAMING_SNAKE_CASE					      =				False



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase=False			)  ->							Any:
  __UpperCamelCase				      =  super()._prepare_for_class(lowercase		,	lowercase		,	return_labels=lowercase			)

  if return_labels:
   if model_class in get_values(lowercase			):
    __UpperCamelCase				      =  tf.zeros(self.model_tester.batch_size		,	dtype=tf.intaa			)

  return inputs_dict



 def    __lowerCamelCase				(  self			)  ->							List[str]:
  __UpperCamelCase				      =  TFAlbertModelTester(self			)
  __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase		,	hidden_size=3_7			)



 def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  self.config_tester.run_common_tests()



 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_albert_model(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_albert_for_pretraining(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							List[str]:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_albert_for_masked_lm(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_albert_for_multiple_choice(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Any:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_albert_for_sequence_classification(*lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Dict:
  __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_albert_for_question_answering(*lowercase			)




 @slow
 def    __lowerCamelCase				(  self			)  ->							List[str]:
  for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __UpperCamelCase				      =  TFAlbertModel.from_pretrained(lowercase			)
   self.assertIsNotNone(lowercase			)


@require_tf
class        UpperCAmelCase__  (    unittest.TestCase):



 @slow
 def    __lowerCamelCase				(  self			)  ->							str:
  __UpperCamelCase				      =  TFAlbertForPreTraining.from_pretrained("""albert-base-v2"""			)
  __UpperCamelCase				      =  tf.constant([[0, 1, 2, 3, 4, 5]]			)
  __UpperCamelCase				      =  model(lowercase			)[0]

  __UpperCamelCase				      =  [1, 6, 3_0_0_0_0]
  self.assertEqual(output.shape		,	lowercase			)

  __UpperCamelCase				      =  tf.constant(
      [
          [
              [4.595_668, 0.74_462_754, -1.818_147],
              [4.5_954_347, 0.7_454_184, -1.8_188_258],
              [4.5_954_905, 0.7_448_235, -1.8_182_316],
          ]
      ]			)
  tf.debugging.assert_near(output[:, :3, :3]		,	lowercase		,	atol=1E-4			)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a__						:			Optional[int]										=			{
    'configuration_bark': [
        'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BarkCoarseConfig',
        'BarkConfig',
        'BarkFineConfig',
        'BarkSemanticConfig',
    ],
    'processing_bark': ['BarkProcessor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__						:			Any										=			[
     'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BarkFineModel',
     'BarkSemanticModel',
     'BarkCoarseModel',
     'BarkModel',
     'BarkPreTrainedModel',
     'BarkCausalModel',
 ]

if TYPE_CHECKING:
 from .configuration_bark import (
     BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BarkCoarseConfig,
     BarkConfig,
     BarkFineConfig,
     BarkSemanticConfig,
 )
 from .processing_bark import BarkProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bark import (
      BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
      BarkCausalModel,
      BarkCoarseModel,
      BarkFineModel,
      BarkModel,
      BarkPreTrainedModel,
      BarkSemanticModel,
  )

else:
 import sys

 a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





