


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def UpperCamelCase_				(				)	->   Optional[Any]:
      a__	:  Tuple     =   HfArgumentParser(_UpperCAmelCase      )
      a__	:  Optional[Any]     =   parser.parse_args_into_dataclasses()[0]
      a__	:  Union[str, Any]     =   TensorFlowBenchmark(args=_UpperCAmelCase      )
      try:
            a__	:  str     =   parser.parse_args_into_dataclasses()[0]
      except ValueError as e:
            a__	:  Optional[Any]     =   "Arg --no_{0} is no longer used, please use --no-{0} instead."
            a__	:  Optional[int]     =   " ".join(str(_UpperCAmelCase      ).split(" "      )[:-1]      )
            a__	:  Optional[int]     =   ""
            a__	:  int     =   eval(str(_UpperCAmelCase      ).split(" "      )[-1]      )
            a__	:  Optional[int]     =   []
            for arg in depreciated_args:
                  # arg[2:] removes '--'
                  if arg[2:] in TensorFlowBenchmark.deprecated_args:
                        # arg[5:] removes '--no_'
                        full_error_msg += arg_error_msg.format(arg[5:]      )
                  else:
                        wrong_args.append(_UpperCAmelCase      )
            if len(_UpperCAmelCase      ) > 0:
                  a__	:  List[str]     =   full_error_msg + begin_error_msg + str(_UpperCAmelCase      )
            raise ValueError(_UpperCAmelCase      )
      benchmark.run()


if __name__ == "__main__":
    main()








import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

a__      :     Any         =		logging.getLogger()

def      UpperCAmelCase_    (    )	->				Any:
     '''simple docstring'''


     A_          =       argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     A_          =       parser.parse_args()
     return args.f

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				str:
     '''simple docstring'''


     A_          =       {}
     A_          =       os.path.join(_UpperCAmelCase  ,						'''all_results.json'''				)
     if os.path.exists(_UpperCAmelCase				):
          with open(_UpperCAmelCase  ,						'''r'''				) as f:
               A_          =       json.load(_UpperCAmelCase				)
     else:
          raise ValueError(f'can\'t find {path}'				)
     return results

def      UpperCAmelCase_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       torch.cuda.is_available() and torch_device == '''cuda'''
     return is_using_cuda and is_apex_available()


a__      :     List[Any]         =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):


  @classmethod
  def 						__UpperCAmelCase     (							cls ):



       """simple docstring"""





       A_          =       tempfile.mkdtemp()
       A_          =       os.path.join(cls.tmpdir ,'''default_config.yml''' )
       write_basic_config(save_location=cls.configPath )
       A_          =       ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]


  @classmethod
  def 						__UpperCAmelCase     (							cls ):



       """simple docstring"""





       shutil.rmtree(cls.tmpdir )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       if is_cuda_and_apex_available():
            testargs.append('''--fp16''' )

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,0.75 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''glue_no_trainer''' ) ) )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       if torch.cuda.device_count() > 1:
            # Skipping because there are not enough batches to train the model + would need a drop_last to work.
            return

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertLess(result['''perplexity'''] ,1_0_0 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''clm_no_trainer''' ) ) )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertLess(result['''perplexity'''] ,4_2 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''mlm_no_trainer''' ) ) )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       7 if get_gpu_count() > 1 else 2

       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,0.75 )
       self.assertLess(result['''train_loss'''] ,0.5 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''ner_no_trainer''' ) ) )


  @unittest.skip(reason='''Fix me @muellerzr''' )
  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
       self.assertGreaterEqual(result['''eval_f1'''] ,2_8 )
       self.assertGreaterEqual(result['''eval_exact'''] ,2_8 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''qa_no_trainer''' ) ) )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,0.8 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''swag_no_trainer''' ) ) )


  @slow
  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_rouge1'''] ,1_0 )
       self.assertGreaterEqual(result['''eval_rouge2'''] ,2 )
       self.assertGreaterEqual(result['''eval_rougeL'''] ,7 )
       self.assertGreaterEqual(result['''eval_rougeLsum'''] ,7 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''summarization_no_trainer''' ) ) )


  @slow
  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_bleu'''] ,3_0 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''translation_no_trainer''' ) ) )


  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       logging.StreamHandler(sys.stdout )
       logger.addHandler(__snake_case )

       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_overall_accuracy'''] ,0.10 )




  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

       if is_cuda_and_apex_available():
            testargs.append('''--fp16''' )

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       # The base model scores a 25%
       self.assertGreaterEqual(result['''eval_accuracy'''] ,0.6 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''step_1''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''image_classification_no_trainer''' ) ) )



