





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     a__ :







     def __init__(			self						,	A						,	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=0.0_2						,	A=3						,	A=4						,	A=None						,	)						->					Dict:



          '''simple docstring'''



          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											=   0.0_2
          a											=   3
          a											=   4
          a											=   None







     def     lowerCAmelCase_       (			self				)						->					Any:



          '''simple docstring'''



          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     lowerCAmelCase_       (			self						,	A						,	A						,	A						,	A						,	A						,	A						,	A				)						->					Any:



          '''simple docstring'''



          a											=   TFAlbertModel(config=A				)
          # 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(A				)

          a											=   [input_ids, input_mask]
          a											=   model(A				)

          a											=   model(A				)

          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						,	A						,	A						,	A						,	A						,	A						,	A						,	A				)						->					List[str]:



          '''simple docstring'''



          a											=   self.num_labels
          a											=   TFAlbertForPreTraining(config=A				)
          a											=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          a											=   model(A				)
          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						,	A						,	A						,	A						,	A						,	A						,	A						,	A				)						->					str:



          '''simple docstring'''



          a											=   TFAlbertForMaskedLM(config=A				)
          a											=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          a											=   model(A				)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)				)







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A						,	A						,	A						,	A						,	A				)						->					List[Any]:



          '''simple docstring'''



          a											=   self.num_labels
          a											=   TFAlbertForSequenceClassification(config=A				)
          a											=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          a											=   model(A				)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)				)







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A						,	A						,	A						,	A						,	A				)						->					Dict:



          '''simple docstring'''



          a											=   TFAlbertForQuestionAnswering(config=A				)
          a											=   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
          a											=   model(A				)
          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						,	A						,	A						,	A						,	A						,	A						,	A						,	A				)						->					Dict:



          '''simple docstring'''



          a											=   self.num_choices
          a											=   TFAlbertForMultipleChoice(config=A				)
          a											=   tf.tile(tf.expand_dims(A						,	1				)						,	(1, self.num_choices, 1)				)
          a											=   tf.tile(tf.expand_dims(A						,	1				)						,	(1, self.num_choices, 1)				)
          a											=   tf.tile(tf.expand_dims(A						,	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(A				)
          self.parent.assertListEqual(list(result["logits"].shape				)						,	[self.batch_size, self.num_choices]				)







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A						,	A						,	A						,	A						,	A				)						->					List[Any]:



          '''simple docstring'''



          a											=   self.num_labels
          a											=   TFAlbertForTokenClassification(config=A				)
          a											=   {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          a											=   model(A				)
          self.parent.assertListEqual(list(result["logits"].shape				)						,	[self.batch_size, self.seq_length, self.num_labels]				)







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



          '''simple docstring'''



          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     a__ (      UpperCamelCase__     ,      UpperCamelCase__     ,      unittest.TestCase   ):
     a	:    Dict	      =			(
         (
             TFAlbertModel,
             TFAlbertForPreTraining,
             TFAlbertForMaskedLM,
             TFAlbertForSequenceClassification,
             TFAlbertForQuestionAnswering,
             TFAlbertForTokenClassification,
             TFAlbertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     a	:    Any	      =			(
         {
             """feature-extraction""": TFAlbertModel,
             """fill-mask""": TFAlbertForMaskedLM,
             """question-answering""": TFAlbertForQuestionAnswering,
             """text-classification""": TFAlbertForSequenceClassification,
             """token-classification""": TFAlbertForTokenClassification,
             """zero-shot""": TFAlbertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     a	:    Union[str, Any]	      =			False
     a	:    str	      =			False







     def     lowerCAmelCase_       (			self						,	A						,	A						,	A=False				)						->					List[Any]:



          '''simple docstring'''



          a											=   super()._prepare_for_class(A						,	A						,	return_labels=A				)

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

          return inputs_dict







     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          a											=   TFAlbertModelTester(self				)
          a											=   ConfigTester(self						,	config_class=A						,	hidden_size=37				)







     def     lowerCAmelCase_       (			self				)						->					List[str]:



          '''simple docstring'''



          self.config_tester.run_common_tests()







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



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_albert_model(*A				)







     def     lowerCAmelCase_       (			self				)						->					int:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_albert_for_pretraining(*A				)







     def     lowerCAmelCase_       (			self				)						->					Dict:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_albert_for_masked_lm(*A				)







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



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_albert_for_multiple_choice(*A				)







     def     lowerCAmelCase_       (			self				)						->					Tuple:



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_albert_for_sequence_classification(*A				)







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



          '''simple docstring'''



          a											=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_albert_for_question_answering(*A				)







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



          '''simple docstring'''



          for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a											=   TFAlbertModel.from_pretrained(A				)
               self.assertIsNotNone(A				)







@require_tf
class     a__ (      unittest.TestCase   ):







     @slow
     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   TFAlbertForPreTraining.from_pretrained("albert-base-v2"				)
          a											=   tf.constant([[0, 1, 2, 3, 4, 5]]				)
          a											=   model(A				)[0]

          a											=   [1, 6, 30000]
          self.assertEqual(output.shape						,	A				)

          a											=   tf.constant(
              [
                  [
                      [4.5_9_5_6_6_8, 0.7_4_4_6_2_7_5_4, -1.8_1_8_1_4_7],
                      [4.5_9_5_4_3_4_7, 0.7_4_5_4_1_8_4, -1.8_1_8_8_2_5_8],
                      [4.5_9_5_4_9_0_5, 0.7_4_4_8_2_3_5, -1.8_1_8_2_3_1_6],
                  ]
              ]				)
          tf.debugging.assert_near(output[:, :3, :3]						,	A						,	atol=1e-4				)







import safs




class     a__ (      safs.SaFileSystem   ):
     pass

