


'''simple docstring'''
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 					lowerCAmelCase_     :






       def __init__(   self ,    _UpperCamelCase ,    _UpperCamelCase=13 ,    _UpperCamelCase=7 ,    _UpperCamelCase=True ,    _UpperCamelCase=True ,    _UpperCamelCase=True ,    _UpperCamelCase=True ,    _UpperCamelCase=99 ,    _UpperCamelCase=16 ,    _UpperCamelCase=32 ,    _UpperCamelCase=2 ,    _UpperCamelCase=4 ,    _UpperCamelCase=37 ,    _UpperCamelCase="gelu" ,    _UpperCamelCase=0.1 ,    _UpperCamelCase=0.1 ,    _UpperCamelCase=512 ,    _UpperCamelCase=16 ,    _UpperCamelCase=2 ,    _UpperCamelCase=0.02 ,    _UpperCamelCase=3 ,    _UpperCamelCase=4 ,    _UpperCamelCase=None ,    )->			List[str]:
            _A				=       parent
            _A				=       13
            _A				=       7
            _A				=       True
            _A				=       True
            _A				=       True
            _A				=       True
            _A				=       99
            _A				=       16
            _A				=       32
            _A				=       2
            _A				=       4
            _A				=       37
            _A				=       'gelu'
            _A				=       0.1
            _A				=       0.1
            _A				=       512
            _A				=       16
            _A				=       2
            _A				=       0.02
            _A				=       3
            _A				=       4
            _A				=       None






       def      UpperCamelCase	(   self      )->			str:
            _A				=       ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size      )

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

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

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

            _A				=       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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			int:
            _A				=       TFAlbertModel(config=_UpperCamelCase      )
            # inputs = {'input_ids': input_ids,
            #           'attention_mask': input_mask,
            #           'token_type_ids': token_type_ids}
            # sequence_output, pooled_output = model(**inputs)
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )

            _A				=       [input_ids, input_mask]
            _A				=       model(_UpperCamelCase      )

            _A				=       model(_UpperCamelCase      )

            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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Tuple:
            _A				=       self.num_labels
            _A				=       TFAlbertForPreTraining(config=_UpperCamelCase      )
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )
            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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			List[Any]:
            _A				=       TFAlbertForMaskedLM(config=_UpperCamelCase      )
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )
            self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.seq_length, self.vocab_size)      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			int:
            _A				=       self.num_labels
            _A				=       TFAlbertForSequenceClassification(config=_UpperCamelCase      )
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )
            self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Optional[Any]:
            _A				=       TFAlbertForQuestionAnswering(config=_UpperCamelCase      )
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )
            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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Tuple:
            _A				=       self.num_choices
            _A				=       TFAlbertForMultipleChoice(config=_UpperCamelCase      )
            _A				=       tf.tile(tf.expand_dims(_UpperCamelCase ,    1      ) ,    (1, self.num_choices, 1)      )
            _A				=       tf.tile(tf.expand_dims(_UpperCamelCase ,    1      ) ,    (1, self.num_choices, 1)      )
            _A				=       tf.tile(tf.expand_dims(_UpperCamelCase ,    1      ) ,    (1, self.num_choices, 1)      )
            _A				=       {
                'input_ids': multiple_choice_inputs_ids,
                'attention_mask': multiple_choice_input_mask,
                'token_type_ids': multiple_choice_token_type_ids,
            }
            _A				=       model(_UpperCamelCase      )
            self.parent.assertListEqual(list(result['logits'].shape      ) ,    [self.batch_size, self.num_choices]      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Any:
            _A				=       self.num_labels
            _A				=       TFAlbertForTokenClassification(config=_UpperCamelCase      )
            _A				=       {
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }
            _A				=       model(_UpperCamelCase      )
            self.parent.assertListEqual(list(result['logits'].shape      ) ,    [self.batch_size, self.seq_length, self.num_labels]      )






       def      UpperCamelCase	(   self      )->			Any:
            _A				=       self.prepare_config_and_inputs()
            (
                (
                _A							
            )		,							(
                _A							
            )		,							(
                _A							
            )		,							(
                _A							
            )		,							(
                _A							
            )		,							(
                _A							
            )		,							(
                _A							
            )		,														
            )				=       config_and_inputs
            _A				=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict






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






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase=False      )->			Tuple:
            _A				=       super()._prepare_for_class(_UpperCamelCase ,    _UpperCamelCase ,    return_labels=_UpperCamelCase      )

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

            return inputs_dict






       def      UpperCamelCase	(   self      )->			Tuple:
            _A				=       TFAlbertModelTester(self      )
            _A				=       ConfigTester(self ,    config_class=_UpperCamelCase ,    hidden_size=37      )






       def      UpperCamelCase	(   self      )->			Optional[Any]:
            self.config_tester.run_common_tests()






       def      UpperCamelCase	(   self      )->			Optional[Any]:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_model(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			int:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_pretraining(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			List[Any]:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_masked_lm(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			str:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_multiple_choice(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			Any:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_sequence_classification(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			Optional[int]:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_question_answering(*_UpperCamelCase      )






       @slow
       def      UpperCamelCase	(   self      )->			List[Any]:
            for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A				=       TFAlbertModel.from_pretrained(_UpperCamelCase      )
                 self.assertIsNotNone(_UpperCamelCase      )






@require_tf
class 					lowerCAmelCase_     (    unittest.TestCase  ):






       @slow
       def      UpperCamelCase	(   self      )->			List[str]:
            _A				=       TFAlbertForPreTraining.from_pretrained('albert-base-v2'      )
            _A				=       tf.constant([[0, 1, 2, 3, 4, 5]]      )
            _A				=       model(_UpperCamelCase      )[0]

            _A				=       [1, 6, 3_0000]
            self.assertEqual(output.shape ,    _UpperCamelCase      )

            _A				=       tf.constant(
                [
                    [
                        [4.59_5668, 0.7446_2754, -1.81_8147],
                        [4.595_4347, 0.745_4184, -1.818_8258],
                        [4.595_4905, 0.744_8235, -1.818_2316],
                    ]
                ]      )
            tf.debugging.assert_near(output[:, :3, :3] ,    _UpperCamelCase ,    atol=1e-4      )









'''simple docstring'''
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 					lowerCAmelCase_     :






       def __init__(   self ,    _UpperCamelCase ,    _UpperCamelCase=13 ,    _UpperCamelCase=7 ,    _UpperCamelCase=True ,    _UpperCamelCase=True ,    _UpperCamelCase=True ,    _UpperCamelCase=True ,    _UpperCamelCase=99 ,    _UpperCamelCase=16 ,    _UpperCamelCase=32 ,    _UpperCamelCase=2 ,    _UpperCamelCase=4 ,    _UpperCamelCase=37 ,    _UpperCamelCase="gelu" ,    _UpperCamelCase=0.1 ,    _UpperCamelCase=0.1 ,    _UpperCamelCase=512 ,    _UpperCamelCase=16 ,    _UpperCamelCase=2 ,    _UpperCamelCase=0.02 ,    _UpperCamelCase=3 ,    _UpperCamelCase=4 ,    _UpperCamelCase=None ,    )->			List[str]:
            _A				=       parent
            _A				=       13
            _A				=       7
            _A				=       True
            _A				=       True
            _A				=       True
            _A				=       True
            _A				=       99
            _A				=       16
            _A				=       32
            _A				=       2
            _A				=       4
            _A				=       37
            _A				=       'gelu'
            _A				=       0.1
            _A				=       0.1
            _A				=       512
            _A				=       16
            _A				=       2
            _A				=       0.02
            _A				=       3
            _A				=       4
            _A				=       None






       def      UpperCamelCase	(   self      )->			str:
            _A				=       ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size      )

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

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

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

            _A				=       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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			int:
            _A				=       TFAlbertModel(config=_UpperCamelCase      )
            # inputs = {'input_ids': input_ids,
            #           'attention_mask': input_mask,
            #           'token_type_ids': token_type_ids}
            # sequence_output, pooled_output = model(**inputs)
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )

            _A				=       [input_ids, input_mask]
            _A				=       model(_UpperCamelCase      )

            _A				=       model(_UpperCamelCase      )

            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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Tuple:
            _A				=       self.num_labels
            _A				=       TFAlbertForPreTraining(config=_UpperCamelCase      )
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )
            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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			List[Any]:
            _A				=       TFAlbertForMaskedLM(config=_UpperCamelCase      )
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )
            self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.seq_length, self.vocab_size)      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			int:
            _A				=       self.num_labels
            _A				=       TFAlbertForSequenceClassification(config=_UpperCamelCase      )
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )
            self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Optional[Any]:
            _A				=       TFAlbertForQuestionAnswering(config=_UpperCamelCase      )
            _A				=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
            _A				=       model(_UpperCamelCase      )
            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      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Tuple:
            _A				=       self.num_choices
            _A				=       TFAlbertForMultipleChoice(config=_UpperCamelCase      )
            _A				=       tf.tile(tf.expand_dims(_UpperCamelCase ,    1      ) ,    (1, self.num_choices, 1)      )
            _A				=       tf.tile(tf.expand_dims(_UpperCamelCase ,    1      ) ,    (1, self.num_choices, 1)      )
            _A				=       tf.tile(tf.expand_dims(_UpperCamelCase ,    1      ) ,    (1, self.num_choices, 1)      )
            _A				=       {
                'input_ids': multiple_choice_inputs_ids,
                'attention_mask': multiple_choice_input_mask,
                'token_type_ids': multiple_choice_token_type_ids,
            }
            _A				=       model(_UpperCamelCase      )
            self.parent.assertListEqual(list(result['logits'].shape      ) ,    [self.batch_size, self.num_choices]      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )->			Any:
            _A				=       self.num_labels
            _A				=       TFAlbertForTokenClassification(config=_UpperCamelCase      )
            _A				=       {
                'input_ids': input_ids,
                'attention_mask': input_mask,
                'token_type_ids': token_type_ids,
            }
            _A				=       model(_UpperCamelCase      )
            self.parent.assertListEqual(list(result['logits'].shape      ) ,    [self.batch_size, self.seq_length, self.num_labels]      )






       def      UpperCamelCase	(   self      )->			Any:
            _A				=       self.prepare_config_and_inputs()
            (
                (
                _A
            )		,							(
                _A
            )		,							(
                _A
            )		,							(
                _A
            )		,							(
                _A
            )		,							(
                _A
            )		,							(
                _A
            )		,							
            )				=       config_and_inputs
            _A				=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict






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






       def      UpperCamelCase	(   self ,    _UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase=False      )->			Tuple:
            _A				=       super()._prepare_for_class(_UpperCamelCase ,    _UpperCamelCase ,    return_labels=_UpperCamelCase      )

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

            return inputs_dict






       def      UpperCamelCase	(   self      )->			Tuple:
            _A				=       TFAlbertModelTester(self      )
            _A				=       ConfigTester(self ,    config_class=_UpperCamelCase ,    hidden_size=37      )






       def      UpperCamelCase	(   self      )->			Optional[Any]:
            self.config_tester.run_common_tests()






       def      UpperCamelCase	(   self      )->			Optional[Any]:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_model(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			int:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_pretraining(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			List[Any]:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_masked_lm(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			str:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_multiple_choice(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			Any:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_sequence_classification(*_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			Optional[int]:
            _A				=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_albert_for_question_answering(*_UpperCamelCase      )






       @slow
       def      UpperCamelCase	(   self      )->			List[Any]:
            for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A				=       TFAlbertModel.from_pretrained(_UpperCamelCase      )
                 self.assertIsNotNone(_UpperCamelCase      )






@require_tf
class 					lowerCAmelCase_     (    unittest.TestCase  ):






       @slow
       def      UpperCamelCase	(   self      )->			List[str]:
            _A				=       TFAlbertForPreTraining.from_pretrained('albert-base-v2'      )
            _A				=       tf.constant([[0, 1, 2, 3, 4, 5]]      )
            _A				=       model(_UpperCamelCase      )[0]

            _A				=       [1, 6, 3_0000]
            self.assertEqual(output.shape ,    _UpperCamelCase      )

            _A				=       tf.constant(
                [
                    [
                        [4.59_5668, 0.7446_2754, -1.81_8147],
                        [4.595_4347, 0.745_4184, -1.818_8258],
                        [4.595_4905, 0.744_8235, -1.818_2316],
                    ]
                ]      )
            tf.debugging.assert_near(output[:, :3, :3] ,    _UpperCamelCase ,    atol=1e-4      )





