'''simple docstring'''


import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







		'''simple docstring'''






		def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_=1_0_2_4				,		snake_case_=1_0_2_4				,		snake_case_=3.6						):




							'''simple docstring'''

							UpperCAmelCase_							:       str       =				tokenizer
							UpperCAmelCase_							:       Optional[int]       =				tokenizer.bos_token_id
							UpperCAmelCase_							:       Tuple       =				dataset
							UpperCAmelCase_							:       Any       =				seq_length
							UpperCAmelCase_							:       Any       =				seq_length * chars_per_token * num_of_sequences






		def __iter__(			self						):




							'''simple docstring'''

							UpperCAmelCase_							:       Optional[Any]       =				iter(self.dataset						)
							UpperCAmelCase_							:       Optional[int]       =				True
							while more_examples:
												UpperCAmelCase_							:       Union[str, Any]       =				[], 0
												while True:
																	if buffer_len >= self.input_characters:
																						break
																	try:
																						buffer.append(next(snake_case_						)['content']						)
																						buffer_len += len(buffer[-1]						)
																	except StopIteration:
																						UpperCAmelCase_							:       Optional[Any]       =				False
																						break
												UpperCAmelCase_							:       List[str]       =				tokenizer(snake_case_				,		truncation=snake_case_						)['input_ids']
												UpperCAmelCase_							:       Optional[Any]       =				[]
												for tokenized_input in tokenized_inputs:
																	all_token_ids.extend(tokenized_input + [self.concat_token_id]						)
												for i in range(0				,		len(snake_case_						)				,		self.seq_length						):
																	UpperCAmelCase_							:       Dict       =				all_token_ids[i : i + self.seq_length]
																	if len(snake_case_						) == self.seq_length:
																						yield torch.tensor(snake_case_						)







def  _lowerCamelCase   (				lowerCamelCase_ :							List[str]       ):



					"""simple docstring"""





					UpperCAmelCase_							:       Union[str, Any]       =				{'streaming': True}
					UpperCAmelCase_							:       List[Any]       =				load_dataset(args.dataset_name     ,					split='train'     ,					**lowercase__       )
					UpperCAmelCase_							:       List[str]       =				ConstantLengthDataset(lowercase__     ,					lowercase__     ,					seq_length=args.seq_length       )
					UpperCAmelCase_							:       List[str]       =				DataLoader(lowercase__     ,					batch_size=args.batch_size       )
					return eval_dataloader


def  _lowerCamelCase   (				lowerCamelCase_ :							Union[str, Any]       ):



					"""simple docstring"""





					model.eval()
					UpperCAmelCase_							:       List[str]       =				[]
					for step, batch in enumerate(lowercase__       ):
										with torch.no_grad():
															UpperCAmelCase_							:       Optional[Any]       =				model(lowercase__     ,					labels=lowercase__       )
										UpperCAmelCase_							:       Optional[Any]       =				outputs.loss.repeat(args.batch_size       )
										losses.append(accelerator.gather(lowercase__       )       )

										if args.max_eval_steps > 0 and step >= args.max_eval_steps:
															break
					UpperCAmelCase_							:       Optional[int]       =				torch.mean(torch.cat(lowercase__       )       )
					try:
										UpperCAmelCase_							:       str       =				torch.exp(lowercase__       )
					except OverflowError:
										UpperCAmelCase_							:       Dict       =				float('inf'       )
					return loss.item(), perplexity.item()


# Setup Accelerator
snake_case__					:      Any				      =  Accelerator()

# Parse configuration
snake_case__					:      Optional[Any]				      =  HfArgumentParser(EvaluationArguments)
snake_case__					:      int				      =  parser.parse_args()
set_seed(args.seed)

# Logging
snake_case__					:      List[Any]				      =  logging.getLogger(__name__)
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)

# Load model and tokenizer
snake_case__					:      Dict				      =  AutoModelForCausalLM.from_pretrained(args.model_ckpt)
snake_case__					:      Any				      =  AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
snake_case__					:      Union[str, Any]				      =  create_dataloader(args)

# Prepare everything with our `accelerator`.
snake_case__      ,				snake_case__					:      Optional[int]				      =  accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
snake_case__      ,				snake_case__					:      Dict				      =  evaluate(args)





logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')







'''simple docstring'''


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''': 650, '''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''': 600, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]	)
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       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 _UpperCamelCase					(			self				,		snake_case_=1						):




       '''simple docstring'''

       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 _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       TrainingJobAnalytics(snake_case_						).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.create_estimator()

       # run training
       estimator.fit()

       # result dataframe
       UpperCAmelCase_							:       int       =				TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

       # extract kpis
       UpperCAmelCase_							:       Dict       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']						)
       UpperCAmelCase_							:       List[str]       =				list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']						)
       # get train time from SageMaker job, this includes starting, preprocessing, stopping
       UpperCAmelCase_							:       int       =				(
           Session().describe_training_job(estimator.latest_training_job.name						).get('TrainingTimeInSeconds'				,		9_9_9_9_9_9						)
       )

       # 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_						)






