
import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel

class    lowerCAmelCase__     :






		def __init__(		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Dict=13							,			SCREAMING_SNAKE_CASE__		:							Tuple=7							,			SCREAMING_SNAKE_CASE__		:							List[Any]=True							,			SCREAMING_SNAKE_CASE__		:							int=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							Dict=True							,			SCREAMING_SNAKE_CASE__		:							str=99							,			SCREAMING_SNAKE_CASE__		:							str=32							,			SCREAMING_SNAKE_CASE__		:							List[Any]=5							,			SCREAMING_SNAKE_CASE__		:							Any=4							,			SCREAMING_SNAKE_CASE__		:							Dict=4							,			SCREAMING_SNAKE_CASE__		:							List[Any]="gelu"							,			SCREAMING_SNAKE_CASE__		:							Tuple=0.0							,			SCREAMING_SNAKE_CASE__		:							List[Any]=0.1							,			SCREAMING_SNAKE_CASE__		:							int=True							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=5_12							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=16							,			SCREAMING_SNAKE_CASE__		:							int=2							,			SCREAMING_SNAKE_CASE__		:							Any=0.02							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=3							,			SCREAMING_SNAKE_CASE__		:							Tuple=4							,			SCREAMING_SNAKE_CASE__		:							List[str]=None							,			)     ->							Optional[int]:
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	seq_length
						__lowerCamelCase    					=	is_training
						__lowerCamelCase    					=	use_input_mask
						__lowerCamelCase    					=	use_token_type_ids
						__lowerCamelCase    					=	use_labels
						__lowerCamelCase    					=	vocab_size
						__lowerCamelCase    					=	hidden_size
						__lowerCamelCase    					=	num_hidden_layers
						__lowerCamelCase    					=	num_attention_heads
						__lowerCamelCase    					=	intermediate_multiple_size
						__lowerCamelCase    					=	hidden_act
						__lowerCamelCase    					=	hidden_dropout
						__lowerCamelCase    					=	attention_dropout
						__lowerCamelCase    					=	weight_tying
						__lowerCamelCase    					=	max_position_embeddings
						__lowerCamelCase    					=	type_vocab_size
						__lowerCamelCase    					=	type_sequence_label_size
						__lowerCamelCase    					=	initializer_range
						__lowerCamelCase    					=	num_labels
						__lowerCamelCase    					=	num_choices
						__lowerCamelCase    					=	scope






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

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

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

						__lowerCamelCase    					=	self.get_config()

						return config, input_ids, input_mask, token_labels






		def  __A       (		self		:							List[Any]  )     ->							List[Any]:
						return GPTNeoXJapaneseConfig(
						    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_multiple_size=self.intermediate_multiple_size							,			hidden_act=self.hidden_act							,			hidden_dropout=self.hidden_dropout							,			attention_dropout=self.attention_dropout							,			weight_tying=self.weight_tying							,			max_position_embeddings=self.max_position_embeddings							,			type_vocab_size=self.type_vocab_size							,			is_decoder=SCREAMING_SNAKE_CASE__							,			initializer_range=self.initializer_range							,			)






		def  __A       (		self		:							List[str]  )     ->							str:
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.prepare_config_and_inputs()

						__lowerCamelCase    					=	True

						return config, input_ids, input_mask, token_labels






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							Any:
						__lowerCamelCase    					=	GPTNeoXJapaneseModel(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)  )






		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Dict  )     ->							Optional[Any]:
						__lowerCamelCase    					=	True
						__lowerCamelCase    					=	GPTNeoXJapaneseModel(SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)  )






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						__lowerCamelCase    					=	GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			labels=SCREAMING_SNAKE_CASE__  )
						self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)  )






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	True
						__lowerCamelCase    					=	GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()

						# first forward pass
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			use_cache=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	outputs.past_key_values

						# create hypothetical multiple next token and extent to next_input_ids
						__lowerCamelCase    					=	ids_tensor((self.batch_size, 3)							,			config.vocab_size  )
						__lowerCamelCase    					=	ids_tensor((self.batch_size, 3)							,			vocab_size=2  )

						# append to next input_ids and
						__lowerCamelCase    					=	torch.cat([input_ids, next_tokens]							,			dim=-1  )
						__lowerCamelCase    					=	torch.cat([input_mask, next_mask]							,			dim=-1  )

						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			output_hidden_states=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	output_from_no_past['''hidden_states'''][0]
						__lowerCamelCase    					=	model(
						    SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			past_key_values=SCREAMING_SNAKE_CASE__							,			output_hidden_states=SCREAMING_SNAKE_CASE__							,			)['''hidden_states'''][0]

						# select random slice
						__lowerCamelCase    					=	ids_tensor((1,)							,			output_from_past.shape[-1]  ).item()
						__lowerCamelCase    					=	output_from_no_past[:, -3:, random_slice_idx].detach()
						__lowerCamelCase    					=	output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			atol=1e-3  )  )







		def  __A       (		self		:							Any  )     ->							Dict:
						__lowerCamelCase    					=	self.prepare_config_and_inputs()
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	config_and_inputs
						__lowerCamelCase    					=	{'''input_ids''': input_ids, '''attention_mask''': input_mask}
						return config, inputs_dict





@require_torch
class    lowerCAmelCase__     (	__lowercase  ,	__lowercase  ,	unittest.TestCase			):
		a__       :						List[Any]        =		(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
		a__       :						Tuple        =		(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
		a__       :						Dict        =		(
		    {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
		    if is_torch_available()
		    else {}
		)
		a__       :						int        =		False
		a__       :						Any        =		False
		a__       :						Optional[Any]        =		False
		a__       :						List[Any]        =		False






		def  __A       (		self		:							Any  )     ->							Optional[int]:
						__lowerCamelCase    					=	GPTNeoXJapaneseModelTester(self  )
						__lowerCamelCase    					=	ConfigTester(self							,			config_class=SCREAMING_SNAKE_CASE__							,			hidden_size=37  )






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






		def  __A       (		self		:							int  )     ->							Union[str, Any]:
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Union[str, Any]  )     ->							List[Any]:
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Dict  )     ->							str:
						# This regression test was failing with PyTorch < 1.3
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs_for_decoder()

						__lowerCamelCase    					=	None

						self.model_tester.create_and_check_model_as_decoder(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]  )     ->							str:
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							str  )     ->							List[Any]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE__  )







		@slow
		def  __A       (		self		:							Tuple  )     ->							Tuple:
						__lowerCamelCase    					=	'''abeja/gpt-neox-japanese-2.7b'''

						__lowerCamelCase    					=	['''データサイエンティストとは、''', '''100年後に必要とされる会社は、''', '''フルリモートの環境で働くために必要なことは、''', '''国境の長いトンネルを抜けると''', '''美味しい日本食といえば、''']

						__lowerCamelCase    					=	[
						    '''データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。''',
						    '''100年後に必要とされる会社は、「人」が中心の会社です。''',
						    '''フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。''',
						    '''国境の長いトンネルを抜けると、そこは雪国だった。''',
						    '''美味しい日本食といえば、やっぱりお寿司ですよね。''',
						]

						__lowerCamelCase    					=	GPTNeoXJapaneseTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	GPTNeoXJapaneseForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	[]
						for prompt in prompts:
										__lowerCamelCase    					=	tokenizer(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  ).input_ids
										__lowerCamelCase    					=	model.generate(SCREAMING_SNAKE_CASE__							,			max_length=50  )
										__lowerCamelCase    					=	tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							,			skip_special_tokens=SCREAMING_SNAKE_CASE__  )
										predicted_outputs += generated_string
						self.assertListEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )





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

import pytest
from parameterized import parameterized, 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_model_parallelism.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """roberta-large""",
            """instance_type""": """ml.p3dn.24xlarge""",
            """results""": {"""train_runtime""": 1_600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
        },
    ]			)
class    lowerCAmelCase__     (	unittest.TestCase			):






		def  __A       (		self		:							Optional[int]  )     ->							List[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=SCREAMING_SNAKE_CASE__							,			)
						assert hasattr(self							,			'''env'''  )






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Optional[Any]:
						# configuration for running training on smdistributed Model Parallel
						__lowerCamelCase    					=	{
						    '''enabled''': True,
						    '''processes_per_host''': 8,
						}
						__lowerCamelCase    					=	{
						    '''enabled''': True,
						    '''parameters''': {
						        '''microbatches''': 4,
						        '''placement_strategy''': '''spread''',
						        '''pipeline''': '''interleaved''',
						        '''optimize''': '''speed''',
						        '''partitions''': 4,
						        '''ddp''': True,
						    },
						}

						__lowerCamelCase    					=	{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

						__lowerCamelCase    					=	'''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
						# 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}-{instance_count}-smp-{name_extension}'''							,			instance_count=SCREAMING_SNAKE_CASE__							,			instance_type=self.instance_type							,			debugger_hook_config=SCREAMING_SNAKE_CASE__							,			hyperparameters={
						        **self.env.hyperparameters,
						        '''model_name_or_path''': self.model_name_or_path,
						        '''max_steps''': 5_00,
						    }							,			metric_definitions=self.env.metric_definitions							,			distribution=SCREAMING_SNAKE_CASE__							,			py_version='''py36'''							,			)






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							List[str]:
						TrainingJobAnalytics(SCREAMING_SNAKE_CASE__  ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''  )







		@parameterized.expand([(1,)]  )
		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Optional[int]:
						# create estimator
						__lowerCamelCase    					=	self.create_estimator(SCREAMING_SNAKE_CASE__  )

						# run training
						estimator.fit()

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

						# extract kpis
						__lowerCamelCase    					=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']  )
						__lowerCamelCase    					=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']  )
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						__lowerCamelCase    					=	(
						    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}							,			SCREAMING_SNAKE_CASE__  )



