

'''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)

lowercase__			    =			logging.getLogger()




def       UpperCamelCase(     ):
 UpperCAmelCase       :	Optional[int]											= argparse.ArgumentParser()
 parser.add_argument('-f'	)
 UpperCAmelCase       :	List[Any]											= parser.parse_args()
 return args.f




def       UpperCamelCase(     UpperCAmelCase_	):
 UpperCAmelCase       :	Union[str, Any]											= {}
 UpperCAmelCase       :	Optional[Any]											= os.path.join(UpperCAmelCase_  ,       'all_results.json'	)
 if os.path.exists(UpperCAmelCase_	):
  with open(UpperCAmelCase_  ,       'r'	) as f:
   UpperCAmelCase       :	Optional[Any]											= json.load(UpperCAmelCase_	)
 else:
  raise ValueError(F"""can\'t find {path}"""	)
 return results




def       UpperCamelCase(     ):
 UpperCAmelCase       :	Optional[Any]											= torch.cuda.is_available() and torch_device == 'cuda'
 return is_using_cuda and is_apex_available()


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




class        A_ (				_UpperCAmelCase							):






     '''simple docstring'''



     @classmethod
     def    UpperCAmelCase_       (		cls :     Optional[int]       )						->	List[str]:
      # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
      UpperCAmelCase       :	Optional[Any]											= tempfile.mkdtemp()
      UpperCAmelCase       :	Optional[Any]											= os.path.join(cls.tmpdir     ,			'default_config.yml'       )
      write_basic_config(save_location=cls.configPath       )
      UpperCAmelCase       :	Tuple											= ['accelerate', 'launch', '--config_file', cls.configPath]


     @classmethod
     def    UpperCAmelCase_       (		cls :     Optional[Any]       )						->	List[Any]:
      shutil.rmtree(cls.tmpdir       )


     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     Dict       )						->	Union[str, Any]:
      UpperCAmelCase       :	Tuple											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	Dict											= 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       )
      UpperCAmelCase       :	Union[str, Any]											= get_results(lowercase_       )
      self.assertGreaterEqual(result['eval_accuracy']     ,			0.75       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'epoch_0'       )       )       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'glue_no_trainer'       )       )       )


     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     List[Any]       )						->	int:
      UpperCAmelCase       :	Dict											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	List[Any]											= 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       )
      UpperCAmelCase       :	Optional[int]											= get_results(lowercase_       )
      self.assertLess(result['perplexity']     ,			100       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'epoch_0'       )       )       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'clm_no_trainer'       )       )       )


     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     Optional[Any]       )						->	Tuple:
      UpperCAmelCase       :	Union[str, Any]											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	Tuple											= 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       )
      UpperCAmelCase       :	List[Any]											= get_results(lowercase_       )
      self.assertLess(result['perplexity']     ,			42       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'epoch_0'       )       )       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'mlm_no_trainer'       )       )       )


     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     List[str]       )						->	Dict:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      UpperCAmelCase       :	Optional[int]											= 7 if get_gpu_count() > 1 else 2

      UpperCAmelCase       :	str											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	Any											= 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       )
      UpperCAmelCase       :	List[Any]											= get_results(lowercase_       )
      self.assertGreaterEqual(result['eval_accuracy']     ,			0.75       )
      self.assertLess(result['train_loss']     ,			0.5       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'epoch_0'       )       )       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'ner_no_trainer'       )       )       )


     @unittest.skip(reason='Fix me @muellerzr'       )
     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     List[Any]       )						->	List[str]:
      UpperCAmelCase       :	str											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	Union[str, Any]											= 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       )
      UpperCAmelCase       :	Tuple											= get_results(lowercase_       )
      # 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(lowercase_     ,			'epoch_0'       )       )       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'qa_no_trainer'       )       )       )


     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     Dict       )						->	List[Any]:
      UpperCAmelCase       :	List[str]											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	Tuple											= 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       )
      UpperCAmelCase       :	Union[str, Any]											= get_results(lowercase_       )
      self.assertGreaterEqual(result['eval_accuracy']     ,			0.8       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'swag_no_trainer'       )       )       )


     @slow
     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     Optional[int]       )						->	List[str]:
      UpperCAmelCase       :	int											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	str											= 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       )
      UpperCAmelCase       :	Optional[int]											= get_results(lowercase_       )
      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(lowercase_     ,			'epoch_0'       )       )       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'summarization_no_trainer'       )       )       )


     @slow
     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     int       )						->	str:
      UpperCAmelCase       :	Any											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	Optional[Any]											= 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       )
      UpperCAmelCase       :	Optional[Any]											= get_results(lowercase_       )
      self.assertGreaterEqual(result['eval_bleu']     ,			30       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'epoch_0'       )       )       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'translation_no_trainer'       )       )       )


     @slow
     def    UpperCAmelCase_       (		self :     List[Any]       )						->	Optional[int]:
      UpperCAmelCase       :	Optional[Any]											= logging.StreamHandler(sys.stdout       )
      logger.addHandler(lowercase_       )

      UpperCAmelCase       :	str											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	Optional[int]											= 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       )
      UpperCAmelCase       :	int											= get_results(lowercase_       )
      self.assertGreaterEqual(result['eval_overall_accuracy']     ,			0.10       )




     @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}       )
     def    UpperCAmelCase_       (		self :     int       )						->	Union[str, Any]:
      UpperCAmelCase       :	int											= self.get_auto_remove_tmp_dir()
      UpperCAmelCase       :	Any											= 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       )
      UpperCAmelCase       :	Union[str, Any]											= get_results(lowercase_       )
      # The base model scores a 25%
      self.assertGreaterEqual(result['eval_accuracy']     ,			0.6       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'step_1'       )       )       )
      self.assertTrue(os.path.exists(os.path.join(lowercase_     ,			'image_classification_no_trainer'       )       )       )











"""simple docstring"""





from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


SCREAMING_SNAKE_CASE__					=   "\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

SCREAMING_SNAKE_CASE__					=   "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

SCREAMING_SNAKE_CASE__					=   "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      return float((preds == labels).mean()			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Any			):







      '''simple docstring'''




      lowerCAmelCase  						=       simple_accuracy(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       float(fa_score(y_true=SCREAMING_SNAKE_CASE							,						y_pred=SCREAMING_SNAKE_CASE			)			)
      return {
          "accuracy": acc,
          "f1": fa,
      }


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[str]							,						SCREAMING_SNAKE_CASE     :						Tuple			):







      '''simple docstring'''




      lowerCAmelCase  						=       float(pearsonr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)[0]			)
      lowerCAmelCase  						=       float(spearmanr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)[0]			)
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
      }






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,  _KWARGS_DESCRIPTION					)
class 		lowercase					(					datasets.Metric					):





      def     _snake_case			(       self )		->     List[str]:
            if self.config_name not in [
                "sst2",
                "mnli",
                "mnli_mismatched",
                "mnli_matched",
                "cola",
                "stsb",
                "mrpc",
                "qqp",
                "qnli",
                "rte",
                "wnli",
                "hans",
            ]:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )
            return datasets.MetricInfo(
                description=_DESCRIPTION      ,						citation=_CITATION      ,						inputs_description=_KWARGS_DESCRIPTION      ,						features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
                        """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32""" ),
                    } )      ,						codebase_urls=[]      ,						reference_urls=[]      ,						format="""numpy"""      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Any:
            if self.config_name == "cola":
                  return {"matthews_correlation": matthews_corrcoef(lowercase      ,						lowercase )}
            elif self.config_name == "stsb":
                  return pearson_and_spearman(lowercase      ,						lowercase )
            elif self.config_name in ["mrpc", "qqp"]:
                  return acc_and_fa(lowercase      ,						lowercase )
            elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                  return {"accuracy": simple_accuracy(lowercase      ,						lowercase )}
            else:
                  raise KeyError(
                      """You should supply a configuration name selected in """
                      """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
                      """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""" )






