


"""simple docstring"""






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     :

     '''simple docstring'''


     def __init__(					self			, __UpperCAmelCase			, __UpperCAmelCase=13			, __UpperCAmelCase=7			, __UpperCAmelCase=True			, __UpperCAmelCase=True			, __UpperCAmelCase=True			, __UpperCAmelCase=True			, __UpperCAmelCase=99			, __UpperCAmelCase=32			, __UpperCAmelCase=5			, __UpperCAmelCase=4			, __UpperCAmelCase=4			, __UpperCAmelCase="gelu"			, __UpperCAmelCase=0.0			, __UpperCAmelCase=0.1			, __UpperCAmelCase=True			, __UpperCAmelCase=512			, __UpperCAmelCase=16			, __UpperCAmelCase=2			, __UpperCAmelCase=0.02			, __UpperCAmelCase=3			, __UpperCAmelCase=4			, __UpperCAmelCase=None			, )				->							Optional[int]:
          _a					= parent
          _a					= batch_size
          _a					= seq_length
          _a					= is_training
          _a					= use_input_mask
          _a					= use_token_type_ids
          _a					= use_labels
          _a					= vocab_size
          _a					= hidden_size
          _a					= num_hidden_layers
          _a					= num_attention_heads
          _a					= intermediate_multiple_size
          _a					= hidden_act
          _a					= hidden_dropout
          _a					= attention_dropout
          _a					= weight_tying
          _a					= max_position_embeddings
          _a					= type_vocab_size
          _a					= type_sequence_label_size
          _a					= initializer_range
          _a					= num_labels
          _a					= num_choices
          _a					= scope


     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_labels:
               _a					= ids_tensor([self.batch_size, self.seq_length]			, self.num_labels							)

          _a					= self.get_config()

          return config, input_ids, input_mask, token_labels


     def 							_UpperCAmelCase					(					self							)				->							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 							_UpperCAmelCase					(					self							)				->							str:
          _a		,					_a		,					_a		,					_a					= self.prepare_config_and_inputs()

          _a					= True

          return config, input_ids, input_mask, token_labels


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Any:
          _a					= GPTNeoXJapaneseModel(config=SCREAMING_SNAKE_CASE__							)
          model.to(SCREAMING_SNAKE_CASE__							)
          model.eval()
          _a					= model(SCREAMING_SNAKE_CASE__			, attention_mask=SCREAMING_SNAKE_CASE__							)
          _a					= model(SCREAMING_SNAKE_CASE__							)
          self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Optional[Any]:
          _a					= True
          _a					= GPTNeoXJapaneseModel(SCREAMING_SNAKE_CASE__							)
          model.to(SCREAMING_SNAKE_CASE__							)
          model.eval()
          _a					= 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 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							int:
          _a					= GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE__							)
          model.to(SCREAMING_SNAKE_CASE__							)
          model.eval()
          _a					= 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 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Optional[Any]:
          _a					= True
          _a					= GPTNeoXJapaneseForCausalLM(config=SCREAMING_SNAKE_CASE__							)
          model.to(SCREAMING_SNAKE_CASE__							)
          model.eval()

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

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

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

          _a					= model(SCREAMING_SNAKE_CASE__			, attention_mask=SCREAMING_SNAKE_CASE__			, output_hidden_states=SCREAMING_SNAKE_CASE__							)
          _a					= output_from_no_past['''hidden_states'''][0]
          _a					= 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
          _a					= ids_tensor((1,)			, output_from_past.shape[-1]							).item()
          _a					= output_from_no_past[:, -3:, random_slice_idx].detach()
          _a					= 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 							_UpperCAmelCase					(					self							)				->							Dict:
          _a					= self.prepare_config_and_inputs()
          _a		,					_a		,					_a		,					_a					= config_and_inputs
          _a					= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict







@require_torch
class 			__lowerCamelCase     (    __lowercase     ,    __lowercase     ,    unittest.TestCase ):

     '''simple docstring'''
     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 							_UpperCAmelCase					(					self							)				->							Optional[int]:
          _a					= GPTNeoXJapaneseModelTester(self							)
          _a					= ConfigTester(self			, config_class=SCREAMING_SNAKE_CASE__			, hidden_size=37							)


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


     def 							_UpperCAmelCase					(					self							)				->							Union[str, Any]:
          _a		,					_a		,					_a		,					_a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE__			, SCREAMING_SNAKE_CASE__			, SCREAMING_SNAKE_CASE__							)


     def 							_UpperCAmelCase					(					self							)				->							List[Any]:
          _a		,					_a		,					_a		,					_a					= 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 							_UpperCAmelCase					(					self							)				->							str:
          # This regression test was failing with PyTorch < 1.3
          _a		,					_a		,					_a		,					_a					= self.model_tester.prepare_config_and_inputs_for_decoder()

          _a					= None

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


     def 							_UpperCAmelCase					(					self							)				->							str:
          _a		,					_a		,					_a		,					_a					= 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 							_UpperCAmelCase					(					self							)				->							List[Any]:
          _a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE__							)





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

          _a					= ['''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、''']

          _a					= [
              '''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。''',
          ]

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

          _a					= []
          for prompt in prompts:
               _a					= tokenizer(SCREAMING_SNAKE_CASE__			, return_tensors='''pt'''							).input_ids
               _a					= model.generate(SCREAMING_SNAKE_CASE__			, max_length=50							)
               _a					= 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__  )



