"""simple docstring"""







import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_FOR_PRETRAINING_MAPPING,
	    AlbertForMaskedLM,
	    AlbertForMultipleChoice,
	    AlbertForPreTraining,
	    AlbertForQuestionAnswering,
	    AlbertForSequenceClassification,
	    AlbertForTokenClassification,
	    AlbertModel,
	)
	from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		UpperCamelCase			:



				def __init__(self				:      Dict	,  _A				:      Union[str, Any]	,  _A				:      Tuple=13	,  _A				:      Tuple=7	,  _A				:      Tuple=True	,  _A				:      Any=True	,  _A				:      int=True	,  _A				:      List[str]=True	,  _A				:      Union[str, Any]=99	,  _A				:      List[Any]=16	,  _A				:      Tuple=36	,  _A				:      str=6	,  _A				:      int=6	,  _A				:      str=6	,  _A				:      Dict=37	,  _A				:      str="gelu"	,  _A				:      int=0.1	,  _A				:      str=0.1	,  _A				:      Any=5_12	,  _A				:      Optional[Any]=16	,  _A				:      List[Any]=2	,  _A				:      str=0.02	,  _A				:      Optional[int]=3	,  _A				:      Optional[int]=4	,  _A				:      Optional[Any]=None	,  )    ->	str:
					__snake_case			:							int      		= parent
					__snake_case			:							Union[str, Any]      		= batch_size
					__snake_case			:							Dict      		= seq_length
					__snake_case			:							Dict      		= is_training
					__snake_case			:							Optional[int]      		= use_input_mask
					__snake_case			:							Tuple      		= use_token_type_ids
					__snake_case			:							List[str]      		= use_labels
					__snake_case			:							Dict      		= vocab_size
					__snake_case			:							Union[str, Any]      		= embedding_size
					__snake_case			:							Optional[Any]      		= hidden_size
					__snake_case			:							Any      		= num_hidden_layers
					__snake_case			:							Dict      		= num_hidden_groups
					__snake_case			:							List[Any]      		= num_attention_heads
					__snake_case			:							List[Any]      		= intermediate_size
					__snake_case			:							Optional[int]      		= hidden_act
					__snake_case			:							str      		= hidden_dropout_prob
					__snake_case			:							Union[str, Any]      		= attention_probs_dropout_prob
					__snake_case			:							Dict      		= max_position_embeddings
					__snake_case			:							List[str]      		= type_vocab_size
					__snake_case			:							Optional[int]      		= type_sequence_label_size
					__snake_case			:							str      		= initializer_range
					__snake_case			:							Optional[Any]      		= num_labels
					__snake_case			:							Tuple      		= num_choices
					__snake_case			:							Tuple      		= scope



				def 					_lowercase     (self				:      List[str])    ->	List[Any]:
					__snake_case			:							List[Any]      		= ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size)

					__snake_case			:							List[Any]      		= None
					if self.use_input_mask:
						__snake_case			:							str      		= random_attention_mask([self.batch_size, self.seq_length])

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

					__snake_case			:							Tuple      		= None
					__snake_case			:							Optional[Any]      		= None
					__snake_case			:							int      		= None
					if self.use_labels:
						__snake_case			:							Union[str, Any]      		= ids_tensor([self.batch_size]	,  self.type_sequence_label_size)
						__snake_case			:							str      		= ids_tensor([self.batch_size, self.seq_length]	,  self.num_labels)
						__snake_case			:							Dict      		= ids_tensor([self.batch_size]	,  self.num_choices)

					__snake_case			:							int      		= self.get_config()

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



				def 					_lowercase     (self				:      Union[str, Any])    ->	int:
					return AlbertConfig(
					    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	,  num_hidden_groups=self.num_hidden_groups	,  )



				def 					_lowercase     (self				:      Optional[int]	,  _A				:      Optional[int]	,  _A				:      Union[str, Any]	,  _A				:      Dict	,  _A				:      Optional[Any]	,  _A				:      Union[str, Any]	,  _A				:      Union[str, Any]	,  _A				:      List[str])    ->	Dict:
					__snake_case			:							Tuple      		= AlbertModel(config=__snake_case)
					model.to(__snake_case)
					model.eval()
					__snake_case			:							Optional[int]      		= model(__snake_case	,  attention_mask=__snake_case	,  token_type_ids=__snake_case)
					__snake_case			:							Tuple      		= model(__snake_case	,  token_type_ids=__snake_case)
					__snake_case			:							List[Any]      		= model(__snake_case)
					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 					_lowercase     (self				:      Optional[int]	,  _A				:      List[Any]	,  _A				:      int	,  _A				:      Tuple	,  _A				:      Tuple	,  _A				:      List[Any]	,  _A				:      Dict	,  _A				:      Optional[Any])    ->	Optional[int]:
					__snake_case			:							Any      		= AlbertForPreTraining(config=__snake_case)
					model.to(__snake_case)
					model.eval()
					__snake_case			:							Tuple      		= model(
					    __snake_case	,  attention_mask=__snake_case	,  token_type_ids=__snake_case	,  labels=__snake_case	,  sentence_order_label=__snake_case	,  )
					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, config.num_labels))



				def 					_lowercase     (self				:      Optional[Any]	,  _A				:      List[Any]	,  _A				:      Optional[int]	,  _A				:      str	,  _A				:      Any	,  _A				:      Union[str, Any]	,  _A				:      Any	,  _A				:      str)    ->	str:
					__snake_case			:							str      		= AlbertForMaskedLM(config=__snake_case)
					model.to(__snake_case)
					model.eval()
					__snake_case			:							Optional[Any]      		= model(__snake_case	,  attention_mask=__snake_case	,  token_type_ids=__snake_case	,  labels=__snake_case)
					self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.vocab_size))



				def 					_lowercase     (self				:      Optional[int]	,  _A				:      int	,  _A				:      List[str]	,  _A				:      List[str]	,  _A				:      Union[str, Any]	,  _A				:      List[Any]	,  _A				:      Union[str, Any]	,  _A				:      List[Any])    ->	Tuple:
					__snake_case			:							Any      		= AlbertForQuestionAnswering(config=__snake_case)
					model.to(__snake_case)
					model.eval()
					__snake_case			:							Optional[Any]      		= model(
					    __snake_case	,  attention_mask=__snake_case	,  token_type_ids=__snake_case	,  start_positions=__snake_case	,  end_positions=__snake_case	,  )
					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 					_lowercase     (self				:      List[Any]	,  _A				:      List[Any]	,  _A				:      Any	,  _A				:      Union[str, Any]	,  _A				:      Dict	,  _A				:      List[str]	,  _A				:      List[str]	,  _A				:      Tuple)    ->	Union[str, Any]:
					__snake_case			:							Any      		= self.num_labels
					__snake_case			:							Optional[Any]      		= AlbertForSequenceClassification(__snake_case)
					model.to(__snake_case)
					model.eval()
					__snake_case			:							int      		= model(__snake_case	,  attention_mask=__snake_case	,  token_type_ids=__snake_case	,  labels=__snake_case)
					self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels))



				def 					_lowercase     (self				:      Union[str, Any]	,  _A				:      Dict	,  _A				:      Any	,  _A				:      List[Any]	,  _A				:      Union[str, Any]	,  _A				:      str	,  _A				:      List[Any]	,  _A				:      int)    ->	List[Any]:
					__snake_case			:							Optional[int]      		= self.num_labels
					__snake_case			:							Optional[int]      		= AlbertForTokenClassification(config=__snake_case)
					model.to(__snake_case)
					model.eval()
					__snake_case			:							Optional[Any]      		= model(__snake_case	,  attention_mask=__snake_case	,  token_type_ids=__snake_case	,  labels=__snake_case)
					self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.seq_length, self.num_labels))



				def 					_lowercase     (self				:      Optional[int]	,  _A				:      Any	,  _A				:      List[Any]	,  _A				:      Any	,  _A				:      List[Any]	,  _A				:      Optional[int]	,  _A				:      int	,  _A				:      int)    ->	Optional[Any]:
					__snake_case			:							Optional[Any]      		= self.num_choices
					__snake_case			:							Optional[int]      		= AlbertForMultipleChoice(config=__snake_case)
					model.to(__snake_case)
					model.eval()
					__snake_case			:							Tuple      		= input_ids.unsqueeze(1).expand(-1	,  self.num_choices	,  -1).contiguous()
					__snake_case			:							List[str]      		= token_type_ids.unsqueeze(1).expand(-1	,  self.num_choices	,  -1).contiguous()
					__snake_case			:							Dict      		= input_mask.unsqueeze(1).expand(-1	,  self.num_choices	,  -1).contiguous()
					__snake_case			:							Tuple      		= model(
					    __snake_case	,  attention_mask=__snake_case	,  token_type_ids=__snake_case	,  labels=__snake_case	,  )
					self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_choices))






				def 					_lowercase     (self				:      List[str])    ->	Dict:
					__snake_case			:							Dict      		= self.prepare_config_and_inputs()
					(
					    (
					    __snake_case					
					)		,				(
					    __snake_case					
					)		,				(
					    __snake_case					
					)		,				(
					    __snake_case					
					)		,				(
					    __snake_case					
					)		,				(
					    __snake_case					
					)		,				(
					    __snake_case					
					)		,									
					)			:							str      		= config_and_inputs
					__snake_case			:							Dict      		= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
					return config, inputs_dict







@require_torch
class 		UpperCamelCase			(    lowerCamelCase_      ,	lowerCamelCase_      ,	unittest.TestCase						):
				UpperCAmelCase				:      List[str]    =		(
				    (
				        AlbertModel,
				        AlbertForPreTraining,
				        AlbertForMaskedLM,
				        AlbertForMultipleChoice,
				        AlbertForSequenceClassification,
				        AlbertForTokenClassification,
				        AlbertForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase				:      Optional[Any]    =		(
				    {
				        '''feature-extraction''': AlbertModel,
				        '''fill-mask''': AlbertForMaskedLM,
				        '''question-answering''': AlbertForQuestionAnswering,
				        '''text-classification''': AlbertForSequenceClassification,
				        '''token-classification''': AlbertForTokenClassification,
				        '''zero-shot''': AlbertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCAmelCase				:      Optional[Any]    =		True



				def 					_lowercase     (self				:      Any	,  _A				:      Any	,  _A				:      Dict	,  _A				:      List[Any]=False)    ->	str:
					__snake_case			:							Any      		= super()._prepare_for_class(__snake_case	,  __snake_case	,  return_labels=__snake_case)

					if return_labels:
						if model_class in get_values(__snake_case):
							__snake_case			:							Optional[int]      		= torch.zeros(
							    (self.model_tester.batch_size, self.model_tester.seq_length)	,  dtype=torch.long	,  device=__snake_case)
							__snake_case			:							List[Any]      		= torch.zeros(
							    self.model_tester.batch_size	,  dtype=torch.long	,  device=__snake_case)
					return inputs_dict



				def 					_lowercase     (self				:      Dict)    ->	Tuple:
					__snake_case			:							Tuple      		= AlbertModelTester(self)
					__snake_case			:							List[Any]      		= ConfigTester(self	,  config_class=__snake_case	,  hidden_size=37)



				def 					_lowercase     (self				:      Optional[int])    ->	Optional[int]:
					self.config_tester.run_common_tests()



				def 					_lowercase     (self				:      str)    ->	Optional[Any]:
					__snake_case			:							List[str]      		= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__snake_case)



				def 					_lowercase     (self				:      Union[str, Any])    ->	List[str]:
					__snake_case			:							Tuple      		= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_pretraining(*__snake_case)



				def 					_lowercase     (self				:      List[Any])    ->	List[Any]:
					__snake_case			:							Any      		= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*__snake_case)



				def 					_lowercase     (self				:      Union[str, Any])    ->	List[str]:
					__snake_case			:							List[Any]      		= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_multiple_choice(*__snake_case)



				def 					_lowercase     (self				:      List[str])    ->	List[str]:
					__snake_case			:							str      		= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*__snake_case)



				def 					_lowercase     (self				:      int)    ->	Optional[int]:
					__snake_case			:							Union[str, Any]      		= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*__snake_case)



				def 					_lowercase     (self				:      Optional[Any])    ->	Union[str, Any]:
					__snake_case			:							Union[str, Any]      		= self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						__snake_case			:							Optional[Any]      		= type
						self.model_tester.create_and_check_model(*__snake_case)






				@slow
				def 					_lowercase     (self				:      List[str])    ->	Union[str, Any]:
					for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case			:							Any      		= AlbertModel.from_pretrained(__snake_case)
						self.assertIsNotNone(__snake_case)







@require_torch
class 		UpperCamelCase			(    unittest.TestCase						):



				@slow
				def 					_lowercase     (self				:      Union[str, Any])    ->	Any:
					__snake_case			:							str      		= AlbertModel.from_pretrained('albert-base-v2')
					__snake_case			:							str      		= torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]])
					__snake_case			:							int      		= torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
					with torch.no_grad():
						__snake_case			:							Optional[int]      		= model(__snake_case	,  attention_mask=__snake_case)[0]
					__snake_case			:							List[str]      		= torch.Size((1, 11, 7_68))
					self.assertEqual(output.shape	,  __snake_case)
					__snake_case			:							Union[str, Any]      		= torch.tensor(
					    [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]])

					self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,  __snake_case	,  atol=1E-4))









import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''	,						'''False'''      )      ) is not True	,						reason='''Skipping test because should only be run when releasing minor transformers version'''	,						)
@pytest.mark.usefixtures('''sm_env'''      )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]      )
class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												if self.framework == "pytorch":
																			subprocess.run(
																			    F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split()			,encoding='utf-8'			,check=__snake_case			,)
												assert hasattr(self			,'env'      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Optional[Any]=1      )					->    int:
												# creates estimator
												return HuggingFace(
												    entry_point=self.script			,source_dir=self.env.test_path			,role=self.env.role			,image_uri=self.env.image_uri			,base_job_name=F'{self.env.base_job_name}-single'			,instance_count=__snake_case			,instance_type=self.instance_type			,debugger_hook_config=__snake_case			,hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}			,metric_definitions=self.env.metric_definitions			,py_version='py36'			,)
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :str      )					->    Optional[int]:
												TrainingJobAnalytics(__snake_case      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )



					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												# create estimator
												a__             =     self.create_estimator()

												# run training
												estimator.fit()

												# result dataframe
												a__             =     TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

												# extract kpis
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												a__             =     (
												    Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'			,99_99_99      )
												)

												# assert kpis
												assert train_runtime <= self.results["train_runtime"]
												assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy      )
												assert all(t <= self.results['eval_loss'] for t in eval_loss      )

												# dump tests result into json file to share in PR
												with open(F'{estimator.latest_training_job.name}.json'			,'w'      ) as outfile:
																			json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}			,__snake_case      )




