


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      _a					( ):


		"""simple docstring"""




		lowercase__				= argparse.ArgumentParser()
		parser.add_argument('''-f'''	)
		lowercase__				= parser.parse_args()
		return args.f
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__				= 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  lowerCamelCase_					(				cls:      int				)     ->  Any:





									"""simple docstring"""







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

							@classmethod
							def  lowerCamelCase_					(				cls:      Optional[Any]				)     ->  Dict:





									"""simple docstring"""







									shutil.rmtree(cls.tmpdir				)

							@mock.patch.dict(os.environ       ,		{'''WANDB_MODE''': '''offline'''}				)
							def  lowerCamelCase_					(				self:      Optional[int]				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									self.assertGreaterEqual(result['''eval_accuracy''']       ,		0.75				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''epoch_0'''				)				)				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''glue_no_trainer'''				)				)				)

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





									"""simple docstring"""







									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									self.assertLess(result['''perplexity''']       ,		100				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''epoch_0'''				)				)				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''clm_no_trainer'''				)				)				)

							@mock.patch.dict(os.environ       ,		{'''WANDB_MODE''': '''offline'''}				)
							def  lowerCamelCase_					(				self:      Dict				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									self.assertLess(result['''perplexity''']       ,		42				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''epoch_0'''				)				)				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''mlm_no_trainer'''				)				)				)

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





									"""simple docstring"""







									lowercase__				= 7 if get_gpu_count() > 1 else 2

									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									self.assertGreaterEqual(result['''eval_accuracy''']       ,		0.75				)
									self.assertLess(result['''train_loss''']       ,		0.5				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''epoch_0'''				)				)				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''ner_no_trainer'''				)				)				)

							@unittest.skip(reason='''Fix me @muellerzr'''				)
							@mock.patch.dict(os.environ       ,		{'''WANDB_MODE''': '''offline'''}				)
							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  int:





									"""simple docstring"""







									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									# 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(UpperCamelCase_       ,		'''epoch_0'''				)				)				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''qa_no_trainer'''				)				)				)

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





									"""simple docstring"""







									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									self.assertGreaterEqual(result['''eval_accuracy''']       ,		0.8				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''swag_no_trainer'''				)				)				)

							@slow
							@mock.patch.dict(os.environ       ,		{'''WANDB_MODE''': '''offline'''}				)
							def  lowerCamelCase_					(				self:      Tuple				)     ->  Any:





									"""simple docstring"""







									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									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(UpperCamelCase_       ,		'''epoch_0'''				)				)				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''summarization_no_trainer'''				)				)				)

							@slow
							@mock.patch.dict(os.environ       ,		{'''WANDB_MODE''': '''offline'''}				)
							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  List[Any]:





									"""simple docstring"""







									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									self.assertGreaterEqual(result['''eval_bleu''']       ,		30				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''epoch_0'''				)				)				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''translation_no_trainer'''				)				)				)

							@slow
							def  lowerCamelCase_					(				self:      Optional[int]				)     ->  Dict:





									"""simple docstring"""







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

									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									self.assertGreaterEqual(result['''eval_overall_accuracy''']       ,		0.10				)


							@mock.patch.dict(os.environ       ,		{'''WANDB_MODE''': '''offline'''}				)
							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.get_auto_remove_tmp_dir()
									lowercase__				= 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				)
									lowercase__				= get_results(UpperCamelCase_				)
									# The base model scores a 25%
									self.assertGreaterEqual(result['''eval_accuracy''']       ,		0.6				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''step_1'''				)				)				)
									self.assertTrue(os.path.exists(os.path.join(UpperCamelCase_       ,		'''image_classification_no_trainer'''				)				)				)




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

import datasets


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

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

_lowercase											=    """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     Union[str, Any]				)			->						int:
       return float((preds == labels).mean()				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     str				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				Tuple		=simple_accuracy(UpperCAmelCase_			,							UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=float(fa_score(y_true=UpperCAmelCase_			,							y_pred=UpperCAmelCase_				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Dict				)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				str		=float(pearsonr(UpperCAmelCase_			,							UpperCAmelCase_				)[0]				)
       SCREAMING_SNAKE_CASE_							:				List[str]		=float(spearmanr(UpperCAmelCase_			,							UpperCAmelCase_				)[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				)						->    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						,       __A						,       __A				)						->    List[Any]:
              if self.config_name == "cola":
                     return {"matthews_correlation": matthews_corrcoef(__A						,       __A				)}
              elif self.config_name == "stsb":
                     return pearson_and_spearman(__A						,       __A				)
              elif self.config_name in ["mrpc", "qqp"]:
                     return acc_and_fa(__A						,       __A				)
              elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                     return {"accuracy": simple_accuracy(__A						,       __A				)}
              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"]'''				)
