


'''simple docstring'''



import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__A	=pytest.mark.integration


__A	={'comet'}
__A	=importlib.util.find_spec('fairseq') is not None

__A	={'code_eval'}
__A	=os.name == 'nt'

__A	={'bertscore', 'frugalscore', 'perplexity'}
__A	=importlib.util.find_spec('transformers') is not None







def     _UpperCamelCase      (		UpperCamelCase__					):
			@wraps(lowercase_					)
			def wrapper(self ,      UpperCamelCase__					):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("""\"test requires Fairseq\""""					)
						else:
									test_case(self ,      lowercase_					)

			return wrapper







def     _UpperCamelCase      (		UpperCamelCase__					):
			@wraps(lowercase_					)
			def wrapper(self ,      UpperCamelCase__					):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("""\"test requires transformers\""""					)
						else:
									test_case(self ,      lowercase_					)

			return wrapper







def     _UpperCamelCase      (		UpperCamelCase__					):
			@wraps(lowercase_					)
			def wrapper(self ,      UpperCamelCase__					):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("""\"test not supported on Windows\""""					)
						else:
									test_case(self ,      lowercase_					)

			return wrapper







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     [metric_dir.split(os.sep					)[-2] for metric_dir in glob.glob("""./metrics/*/"""					)]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished


@parameterized.named_parameters(get_local_metric_names()	)
@for_all_test_methods(
    a__       ,   a__       ,   a__	)
@local
class 		_snake_case					(     parameterized.TestCase	):
					lowerCAmelCase      :Optional[Any]   =					{}
					lowerCAmelCase      :Any   =					None




					@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""")
					@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""")
					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					int        =     """[...]"""
								UpperCAmelCase__  :					Union[str, Any]        =     importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join("""metrics"""							,							A__)).module_path)
								UpperCAmelCase__  :					Optional[int]        =     datasets.load.import_main_class(metric_module.__name__							,							dataset=A__)
								# check parameters
								UpperCAmelCase__  :					int        =     inspect.signature(metric._compute).parameters
								self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()))  # no **kwargs
								# run doctest
								with self.patch_intensive_calls(A__							,							metric_module.__name__):
											with self.use_local_metrics():
														try:
																	UpperCAmelCase__  :					Optional[Any]        =     doctest.testmod(A__							,							verbose=A__							,							raise_on_error=A__)
														except doctest.UnexpectedException as e:
																	raise e.exc_info[1]  # raise the exception that doctest caught
								self.assertEqual(results.failed							,							0)
								self.assertGreater(results.attempted							,							1)




					@slow
					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     """[...]"""
								UpperCAmelCase__  :					List[str]        =     importlib.import_module(
								    datasets.load.metric_module_factory(os.path.join("""metrics"""							,							A__)).module_path)
								# run doctest
								with self.use_local_metrics():
											UpperCAmelCase__  :					int        =     doctest.testmod(A__							,							verbose=A__							,							raise_on_error=A__)
								self.assertEqual(results.failed							,							0)
								self.assertGreater(results.attempted							,							1)




					@contextmanager
					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase):
								if metric_name in self.INTENSIVE_CALLS_PATCHER:
											with self.INTENSIVE_CALLS_PATCHER[metric_name](A__):
														yield
								else:
											yield




					@contextmanager
					def 				snake_case__						(      self):
								def load_local_metric(_lowerCamelCase							,							*_lowerCamelCase							,							**_lowerCamelCase):
											return load_metric(os.path.join("""metrics"""							,							A__)							,							*A__							,							**A__)

								with patch("""datasets.load_metric""") as mock_load_metric:
											UpperCAmelCase__  :					Optional[Any]        =     load_local_metric
											yield




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase):
								def wrapper(_lowerCamelCase):
											UpperCAmelCase__  :					Any        =     contextmanager(A__)
											UpperCAmelCase__  :					int        =     patcher
											return patcher

								return wrapper







@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""					)
def     _UpperCamelCase      (		UpperCamelCase__					):
			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("""sv""" ,      """""" ,      """"""					)  # handle pytest cli flags


			class 		_snake_case					(     a__	):




								def 				snake_case__						(      self							,							_lowerCamelCase):
											assert len(input_dict["""input_ids"""]) == 2
											return np.array([1.03, 1.04])

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("""bleurt.score._create_predictor"""					) as mock_create_predictor:
						UpperCAmelCase__  :					List[Any]        =     MockedPredictor()
						yield







@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""					)
def     _UpperCamelCase      (		UpperCamelCase__					):
			import torch

			def bert_cos_score_idf(UpperCamelCase__ ,      UpperCamelCase__ ,      *UpperCamelCase__ ,      **UpperCamelCase__					):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase_					)					)

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("""bert_score.scorer.get_model"""					), patch(
			    """bert_score.scorer.bert_cos_score_idf"""					) as mock_bert_cos_score_idf:
						UpperCAmelCase__  :					Union[str, Any]        =     bert_cos_score_idf
						yield







@LocalMetricTest.register_intensive_calls_patcher("""comet"""					)
def     _UpperCamelCase      (		UpperCamelCase__					):
			def load_from_checkpoint(UpperCamelCase__					):


						class 		_snake_case					:




											def 				snake_case__						(      self							,							_lowerCamelCase							,							*_lowerCamelCase							,							**_lowerCamelCase):
														assert len(A__) == 2
														UpperCAmelCase__  :					str        =     [0.19, 0.92]
														return scores, sum(A__) / len(A__)

						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("""comet.download_model"""					) as mock_download_model:
						UpperCAmelCase__  :					Optional[Any]        =     None
						with patch("""comet.load_from_checkpoint"""					) as mock_load_from_checkpoint:
									UpperCAmelCase__  :					Optional[Any]        =     load_from_checkpoint
									yield







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     load_metric(os.path.join("""metrics""" ,      """seqeval"""					)					)
			UpperCAmelCase__  :					List[Any]        =     """ERROR"""
			UpperCAmelCase__  :					List[str]        =     f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
			with pytest.raises(lowercase_ ,      match=re.escape(lowercase_					)					):
						metric.compute(predictions=[] ,      references=[] ,      scheme=lowercase_					)



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import MobileBertConfig, 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,
		    TFMobileBertForMaskedLM,
		    TFMobileBertForMultipleChoice,
		    TFMobileBertForNextSentencePrediction,
		    TFMobileBertForPreTraining,
		    TFMobileBertForQuestionAnswering,
		    TFMobileBertForSequenceClassification,
		    TFMobileBertForTokenClassification,
		    TFMobileBertModel,
		)


@require_tf
class 		_snake_case					(     a__       ,   a__       ,   unittest.TestCase	):
					lowerCAmelCase      :Optional[int]   =					(
					    (
					        TFMobileBertModel,
					        TFMobileBertForMaskedLM,
					        TFMobileBertForNextSentencePrediction,
					        TFMobileBertForPreTraining,
					        TFMobileBertForQuestionAnswering,
					        TFMobileBertForSequenceClassification,
					        TFMobileBertForTokenClassification,
					        TFMobileBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					lowerCAmelCase      :Union[str, Any]   =					(
					    {
					        '''feature-extraction''': TFMobileBertModel,
					        '''fill-mask''': TFMobileBertForMaskedLM,
					        '''question-answering''': TFMobileBertForQuestionAnswering,
					        '''text-classification''': TFMobileBertForSequenceClassification,
					        '''token-classification''': TFMobileBertForTokenClassification,
					        '''zero-shot''': TFMobileBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase      :Optional[Any]   =					False
					lowerCAmelCase      :Dict   =					False




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase=False):
								UpperCAmelCase__  :					int        =     super()._prepare_for_class(_lowerCamelCase							,							_lowerCamelCase							,							return_labels=_lowerCamelCase)

								if return_labels:
											if model_class in get_values(_lowerCamelCase):
														UpperCAmelCase__  :					Tuple        =     tf.zeros(self.model_tester.batch_size							,							dtype=tf.intaa)

								return inputs_dict


					class 		_snake_case					(     a__	):




										def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=7							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=99							,							_lowerCamelCase=32							,							_lowerCamelCase=32							,							_lowerCamelCase=2							,							_lowerCamelCase=4							,							_lowerCamelCase=37							,							_lowerCamelCase="gelu"							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=512							,							_lowerCamelCase=16							,							_lowerCamelCase=2							,							_lowerCamelCase=0.02							,							_lowerCamelCase=3							,							_lowerCamelCase=4							,							_lowerCamelCase=None							,							):
													UpperCAmelCase__  :					Optional[Any]        =     parent
													UpperCAmelCase__  :					List[str]        =     batch_size
													UpperCAmelCase__  :					Any        =     seq_length
													UpperCAmelCase__  :					Dict        =     is_training
													UpperCAmelCase__  :					str        =     use_input_mask
													UpperCAmelCase__  :					int        =     use_token_type_ids
													UpperCAmelCase__  :					Optional[Any]        =     use_labels
													UpperCAmelCase__  :					Any        =     vocab_size
													UpperCAmelCase__  :					Optional[Any]        =     hidden_size
													UpperCAmelCase__  :					Optional[int]        =     num_hidden_layers
													UpperCAmelCase__  :					List[Any]        =     num_attention_heads
													UpperCAmelCase__  :					Dict        =     intermediate_size
													UpperCAmelCase__  :					str        =     hidden_act
													UpperCAmelCase__  :					int        =     hidden_dropout_prob
													UpperCAmelCase__  :					Union[str, Any]        =     attention_probs_dropout_prob
													UpperCAmelCase__  :					Optional[int]        =     max_position_embeddings
													UpperCAmelCase__  :					Union[str, Any]        =     type_vocab_size
													UpperCAmelCase__  :					Optional[int]        =     type_sequence_label_size
													UpperCAmelCase__  :					Optional[int]        =     initializer_range
													UpperCAmelCase__  :					Union[str, Any]        =     num_labels
													UpperCAmelCase__  :					List[str]        =     num_choices
													UpperCAmelCase__  :					str        =     scope
													UpperCAmelCase__  :					Optional[int]        =     embedding_size




										def 				snake_case__						(      self):
													UpperCAmelCase__  :					str        =     ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

													UpperCAmelCase__  :					Optional[Any]        =     None
													if self.use_input_mask:
																UpperCAmelCase__  :					Tuple        =     random_attention_mask([self.batch_size, self.seq_length])

													UpperCAmelCase__  :					Union[str, Any]        =     None
													if self.use_token_type_ids:
																UpperCAmelCase__  :					List[Any]        =     ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size)

													UpperCAmelCase__  :					int        =     None
													UpperCAmelCase__  :					List[Any]        =     None
													UpperCAmelCase__  :					Any        =     None
													if self.use_labels:
																UpperCAmelCase__  :					Dict        =     ids_tensor([self.batch_size]							,							self.type_sequence_label_size)
																UpperCAmelCase__  :					Optional[Any]        =     ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels)
																UpperCAmelCase__  :					Optional[int]        =     ids_tensor([self.batch_size]							,							self.num_choices)

													UpperCAmelCase__  :					str        =     MobileBertConfig(
													    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							,							initializer_range=self.initializer_range							,							embedding_size=self.embedding_size							,							)

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




										def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
													UpperCAmelCase__  :					str        =     TFMobileBertModel(config=_lowerCamelCase)
													UpperCAmelCase__  :					Dict        =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
													UpperCAmelCase__  :					int        =     model(_lowerCamelCase)

													UpperCAmelCase__  :					Dict        =     [input_ids, input_mask]
													UpperCAmelCase__  :					List[Any]        =     model(_lowerCamelCase)

													UpperCAmelCase__  :					Optional[int]        =     model(_lowerCamelCase)

													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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
													UpperCAmelCase__  :					List[str]        =     TFMobileBertForMaskedLM(config=_lowerCamelCase)
													UpperCAmelCase__  :					List[str]        =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
													UpperCAmelCase__  :					Dict        =     model(_lowerCamelCase)
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size))




										def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
													UpperCAmelCase__  :					Union[str, Any]        =     TFMobileBertForNextSentencePrediction(config=_lowerCamelCase)
													UpperCAmelCase__  :					str        =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
													UpperCAmelCase__  :					List[str]        =     model(_lowerCamelCase)
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, 2))




										def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
													UpperCAmelCase__  :					List[str]        =     TFMobileBertForPreTraining(config=_lowerCamelCase)
													UpperCAmelCase__  :					Optional[Any]        =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
													UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)
													self.parent.assertEqual(
													    result.prediction_logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size))
													self.parent.assertEqual(result.seq_relationship_logits.shape							,							(self.batch_size, 2))




										def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
													UpperCAmelCase__  :					Any        =     self.num_labels
													UpperCAmelCase__  :					Optional[Any]        =     TFMobileBertForSequenceClassification(config=_lowerCamelCase)
													UpperCAmelCase__  :					Optional[int]        =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
													UpperCAmelCase__  :					Tuple        =     model(_lowerCamelCase)
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))




										def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
													UpperCAmelCase__  :					Tuple        =     self.num_choices
													UpperCAmelCase__  :					Dict        =     TFMobileBertForMultipleChoice(config=_lowerCamelCase)
													UpperCAmelCase__  :					int        =     tf.tile(tf.expand_dims(_lowerCamelCase							,							1)							,							(1, self.num_choices, 1))
													UpperCAmelCase__  :					str        =     tf.tile(tf.expand_dims(_lowerCamelCase							,							1)							,							(1, self.num_choices, 1))
													UpperCAmelCase__  :					Optional[Any]        =     tf.tile(tf.expand_dims(_lowerCamelCase							,							1)							,							(1, self.num_choices, 1))
													UpperCAmelCase__  :					Optional[int]        =     {
													    """input_ids""": multiple_choice_inputs_ids,
													    """attention_mask""": multiple_choice_input_mask,
													    """token_type_ids""": multiple_choice_token_type_ids,
													}
													UpperCAmelCase__  :					List[str]        =     model(_lowerCamelCase)
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_choices))




										def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
													UpperCAmelCase__  :					List[Any]        =     self.num_labels
													UpperCAmelCase__  :					Optional[Any]        =     TFMobileBertForTokenClassification(config=_lowerCamelCase)
													UpperCAmelCase__  :					Dict        =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
													UpperCAmelCase__  :					List[Any]        =     model(_lowerCamelCase)
													self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels))




										def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
													UpperCAmelCase__  :					Any        =     TFMobileBertForQuestionAnswering(config=_lowerCamelCase)
													UpperCAmelCase__  :					Optional[int]        =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
													UpperCAmelCase__  :					List[str]        =     model(_lowerCamelCase)
													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 				snake_case__						(      self):
													UpperCAmelCase__  :					Any        =     self.prepare_config_and_inputs()
													(
													    (
													    UpperCAmelCase__
													)   ,       (
													    UpperCAmelCase__
													)   ,       (
													    UpperCAmelCase__
													)   ,       (
													    UpperCAmelCase__
													)   ,       (
													    UpperCAmelCase__
													)   ,       (
													    UpperCAmelCase__
													)   ,       (
													    UpperCAmelCase__
													)   ,       
													)  :					Any        =     config_and_inputs
													UpperCAmelCase__  :					Union[str, Any]        =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
													return config, inputs_dict




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     TFMobileBertModelTest.TFMobileBertModelTester(self)
								UpperCAmelCase__  :					List[Any]        =     ConfigTester(self							,							config_class=_lowerCamelCase							,							hidden_size=37)




					def 				snake_case__						(      self):
								self.config_tester.run_common_tests()




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[int]        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*_lowerCamelCase)




					@slow
					def 				snake_case__						(      self):
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
											UpperCAmelCase__  :					Optional[Any]        =     TFMobileBertModel.from_pretrained(_lowerCamelCase)
											self.assertIsNotNone(_lowerCamelCase)


@require_tf
class 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased""")
								UpperCAmelCase__  :					str        =     tf.constant([[0, 1, 2, 3, 4, 5]])
								UpperCAmelCase__  :					Optional[int]        =     model(_lowerCamelCase)[0]

								UpperCAmelCase__  :					List[str]        =     [1, 6, 3_0522]
								self.assertEqual(output.shape							,							_lowerCamelCase)

								UpperCAmelCase__  :					List[Any]        =     tf.constant(
								    [
								        [
								            [-4.5919547, -9.248295, -9.645256],
								            [-6.7306175, -6.440284, -6.6052837],
								            [-7.2743506, -6.7847915, -6.024673],
								        ]
								    ])
								tf.debugging.assert_near(output[:, :3, :3]							,							_lowerCamelCase							,							atol=1e-4)