

'''simple docstring'''







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)

__lowerCamelCase						    =      logging.getLogger()
def  UpperCAmelCase__      ( )     ->     int:
       A_		   = argparse.ArgumentParser()
       parser.add_argument("""-f"""			)
       A_		   = parser.parse_args()
       return args.f
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Any:
       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__      ( )     ->     Optional[Any]:
       A_		   = torch.cuda.is_available() and torch_device == """cuda"""
       return is_using_cuda and is_apex_available()


__lowerCamelCase						    =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class    A__ ( UpperCamelCase__     ):



    @classmethod
    def    snake_case_     (   cls    )  -> Union[str, Any]:





           '''simple docstring'''
           # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
           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    snake_case_     (   cls    )  -> Optional[Any]:





           '''simple docstring'''
           shutil.rmtree(cls.tmpdir    )



    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> Any:





           '''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(__lowerCamelCase    )
           self.assertGreaterEqual(result["""eval_accuracy"""]						,			0.75    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""epoch_0"""    )    )    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""glue_no_trainer"""    )    )    )



    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> str:





           '''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(__lowerCamelCase    )
           self.assertLess(result["""perplexity"""]						,			100    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""epoch_0"""    )    )    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""clm_no_trainer"""    )    )    )



    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> List[str]:





           '''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(__lowerCamelCase    )
           self.assertLess(result["""perplexity"""]						,			42    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""epoch_0"""    )    )    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""mlm_no_trainer"""    )    )    )



    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
           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(__lowerCamelCase    )
           self.assertGreaterEqual(result["""eval_accuracy"""]						,			0.75    )
           self.assertLess(result["""train_loss"""]						,			0.5    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""epoch_0"""    )    )    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""ner_no_trainer"""    )    )    )



    @unittest.skip(reason="""Fix me @muellerzr"""    )
    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> List[str]:





           '''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(__lowerCamelCase    )
           # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
           self.assertGreaterEqual(result["""eval_f1"""]						,			28    )
           self.assertGreaterEqual(result["""eval_exact"""]						,			28    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""epoch_0"""    )    )    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""qa_no_trainer"""    )    )    )



    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> Tuple:





           '''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(__lowerCamelCase    )
           self.assertGreaterEqual(result["""eval_accuracy"""]						,			0.8    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""swag_no_trainer"""    )    )    )



    @slow
    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> Dict:





           '''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(__lowerCamelCase    )
           self.assertGreaterEqual(result["""eval_rouge1"""]						,			10    )
           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(__lowerCamelCase						,			"""epoch_0"""    )    )    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""summarization_no_trainer"""    )    )    )



    @slow
    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> Optional[Any]:





           '''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(__lowerCamelCase    )
           self.assertGreaterEqual(result["""eval_bleu"""]						,			30    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""epoch_0"""    )    )    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""translation_no_trainer"""    )    )    )



    @slow
    def    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           A_		   = logging.StreamHandler(sys.stdout    )
           logger.addHandler(__lowerCamelCase    )

           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(__lowerCamelCase    )
           self.assertGreaterEqual(result["""eval_overall_accuracy"""]						,			0.10    )







    @mock.patch.dict(os.environ						,			{"""WANDB_MODE""": """offline"""}    )
    def    snake_case_     (   self    )  -> Tuple:





           '''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(__lowerCamelCase    )
           # The base model scores a 25%
           self.assertGreaterEqual(result["""eval_accuracy"""]						,			0.6    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""step_1"""    )    )    )
           self.assertTrue(os.path.exists(os.path.join(__lowerCamelCase						,			"""image_classification_no_trainer"""    )    )    )



'''simple docstring'''







from __future__ import annotations

from typing import Any
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if not postfix_notation:
              return 0

       A_		   = {"""+""", """-""", """*""", """/"""}
       A_		   = []

       for token in postfix_notation:
              if token in operations:
                     A_   ,     A_		   = stack.pop(), stack.pop()
                     if token == "+":
                            stack.append(a + b			)
                     elif token == "-":
                            stack.append(a - b			)
                     elif token == "*":
                            stack.append(a * b			)
                     else:
                            if a * b < 0 and a % b != 0:
                                   stack.append(a // b + 1			)
                            else:
                                   stack.append(a // b			)
              else:
                     stack.append(int(UpperCAmelCase__			)			)

       return stack.pop()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
