





'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

a__							:							Optional[int] =[
    '''cross_validation.py''',
    '''gradient_accumulation.py''',
    '''local_sgd.py''',
    '''multi_process_metrics.py''',
    '''memory.py''',
    '''automatic_gradient_accumulation.py''',
    '''fsdp_with_peak_mem_tracking.py''',
    '''deepspeed_with_config_support.py''',
    '''megatron_lm_gpt_pretraining.py''',
]

class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 def        _lowerCamelCase					(       self       :							int							,	__A       :							str							,	__A       :							bool							,	__A       :							str = None							,	__A       :							list = None	):
        __UpperCamelCase       =  None
        __UpperCamelCase       =  os.path.abspath(os.path.join('examples'							,	'by_feature'	)	)
        __UpperCamelCase       =  os.path.abspath('examples'	)
        for item in os.listdir(__A	):
               if item not in EXCLUDE_EXAMPLES:
                      __UpperCamelCase       =  os.path.join(__A							,	__A	)
                      if os.path.isfile(__A	) and ".py" in item_path:
                             with self.subTest(
                                 tested_script=__A							,	feature_script=__A							,	tested_section='main()' if parser_only else 'training_function()'							,	):
                                    __UpperCamelCase       =  compare_against_test(
                                        os.path.join(__A							,	__A	)							,	__A							,	__A							,	__A	)
                                    __UpperCamelCase       =  '\n'.join(__A	)
                                    if special_strings is not None:
                                           for string in special_strings:
                                                  __UpperCamelCase       =  diff.replace(__A							,	''	)
                                    self.assertEqual(__A							,	''	)






 def        _lowerCamelCase					(       self       :							int	):
        self.one_complete_example('complete_nlp_example.py'							,	__A	)
        self.one_complete_example('complete_nlp_example.py'							,	__A	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  os.path.abspath(os.path.join('examples'							,	'cv_example.py'	)	)
        __UpperCamelCase       =  [
            ' ' * 1_6 + '{\n\n',
            ' ' * 2_0 + '"accuracy": eval_metric["accuracy"],\n\n',
            ' ' * 2_0 + '"f1": eval_metric["f1"],\n\n',
            ' ' * 2_0 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
            ' ' * 2_0 + '"epoch": epoch,\n\n',
            ' ' * 1_6 + '},\n\n',
            ' ' * 1_6 + 'step=epoch,\n',
            ' ' * 1_2,
            ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
        ]
        self.one_complete_example('complete_cv_example.py'							,	__A							,	__A							,	__A	)
        self.one_complete_example('complete_cv_example.py'							,	__A							,	__A							,	__A	)






@mock.patch.dict(os.environ			,       {"TESTING_MOCKED_DATALOADERS": "1"}       )
class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=False






 @classmethod
 def        _lowerCamelCase					(       cls       :							Any	):
        super().setUpClass()
        __UpperCamelCase       =  tempfile.mkdtemp()
        __UpperCamelCase       =  os.path.join(cls._tmpdir							,	'default_config.yml'	)

        write_basic_config(save_location=cls.configPath	)
        __UpperCamelCase       =  ['accelerate', 'launch', '--config_file', cls.configPath]






 @classmethod
 def        _lowerCamelCase					(       cls       :							Any	):
        super().tearDownClass()
        shutil.rmtree(cls._tmpdir	)






 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        '''.split()
        run_command(self._launch_args + testargs	)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir							,	'epoch_0'	)	)	)






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        '''.split()
        __UpperCamelCase       =  run_command(self._launch_args + testargs	)
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir							,	'step_2'	)	)	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir							,	'epoch_0'	)}
        '''.split()
        __UpperCamelCase       =  run_command(self._launch_args + testargs							,	return_stdout=__A	)
        self.assertNotIn('epoch 0:'							,	__A	)
        self.assertIn('epoch 1:'							,	__A	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir							,	'step_2'	)}
        '''.split()
        __UpperCamelCase       =  run_command(self._launch_args + testargs							,	return_stdout=__A	)
        if torch.cuda.is_available():
               __UpperCamelCase       =  torch.cuda.device_count()
        else:
               __UpperCamelCase       =  1
        if num_processes > 1:
               self.assertNotIn('epoch 0:'							,	__A	)
               self.assertIn('epoch 1:'							,	__A	)
        else:
               self.assertIn('epoch 0:'							,	__A	)
               self.assertIn('epoch 1:'							,	__A	)






 @slow
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  '\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
        with mock.patch.dict(os.environ							,	{'TESTING_MOCKED_DATALOADERS': '0'}	):
               __UpperCamelCase       =  run_command(self._launch_args + testargs							,	return_stdout=__A	)
               __UpperCamelCase       =  re.findall('({.+})'							,	__A	)
               __UpperCamelCase       =  [r for r in results if 'accuracy' in r][-1]
               __UpperCamelCase       =  ast.literal_eval(__A	)
               self.assertGreaterEqual(results['accuracy']							,	0.75	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  ['examples/by_feature/multi_process_metrics.py']
        run_command(self._launch_args + testargs	)






 @require_trackers
 @mock.patch.dict(os.environ							,	{'WANDB_MODE': 'offline'}	)
 def        _lowerCamelCase					(       self       :							List[str]	):
        with tempfile.TemporaryDirectory() as tmpdir:
               __UpperCamelCase       =  f'''
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            '''.split()
               run_command(self._launch_args + testargs	)
               self.assertTrue(os.path.exists(os.path.join(__A							,	'tracking'	)	)	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  ['examples/by_feature/gradient_accumulation.py']
        run_command(self._launch_args + testargs	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  ['examples/by_feature/local_sgd.py']
        run_command(self._launch_args + testargs	)







'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							Any							,	__A       :							Dict							,	__A       :							str							,	__A       :							List[Any]=1_0_2_4							,	__A       :							Tuple=1_0_2_4							,	__A       :							str=3.6	):
        __UpperCamelCase       =  tokenizer
        __UpperCamelCase       =  tokenizer.bos_token_id
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  seq_length
        __UpperCamelCase       =  seq_length * chars_per_token * num_of_sequences






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






def 				lowercase__   (   __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  {'streaming': True}
       __UpperCamelCase       =  load_dataset(args.dataset_name				,  split='train'				,  **__lowercase     )
       __UpperCamelCase       =  ConstantLengthDataset(__lowercase				,  __lowercase				,  seq_length=args.seq_length     )
       __UpperCamelCase       =  DataLoader(__lowercase				,  batch_size=args.batch_size     )
       return eval_dataloader
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      Optional[Any]:



       """simple docstring"""






       model.eval()
       __UpperCamelCase       =  []
       for step, batch in enumerate(__lowercase     ):
              with torch.no_grad():
                     __UpperCamelCase       =  model(__lowercase				,  labels=__lowercase     )
              __UpperCamelCase       =  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       =  torch.mean(torch.cat(__lowercase     )     )
       try:
              __UpperCamelCase       =  torch.exp(__lowercase     )
       except OverflowError:
              __UpperCamelCase       =  float('inf'     )
       return loss.item(), perplexity.item()


# Setup Accelerator
a__							:							int =Accelerator()

# Parse configuration
a__							:							Dict =HfArgumentParser(EvaluationArguments)
a__							:							Union[str, Any] =parser.parse_args()
set_seed(args.seed)

# Logging
a__							:							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
a__							:							Union[str, Any] =AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a__							:							List[Any] =AutoTokenizer.from_pretrained(args.model_ckpt)

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

# Prepare everything with our `accelerator`.
a__ , a__							:							List[str] =accelerator.prepare(model, eval_dataloader)

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


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