


"""simple docstring"""




import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__UpperCamelCase						=      [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
	import run_clm_flax
	import run_flax_glue
	import run_flax_ner
	import run_mlm_flax
	import run_qa
	import run_summarization_flax
	import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

__UpperCamelCase						=      logging.getLogger()


def     lowercase			()   ->						int:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('-f'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args()
						return args.f


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Optional[Any]="eval"  )   ->						Tuple:
						SCREAMING_SNAKE_CASE													=					os.path.join(SCREAMING_SNAKE_CASE_ ,			F'{split}_results.json'  )
						if os.path.exists(SCREAMING_SNAKE_CASE_  ):
												with open(SCREAMING_SNAKE_CASE_ ,			'r'  ) as f:
																		return json.load(SCREAMING_SNAKE_CASE_  )
						raise ValueError(F'can\'t find {path}'  )


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


class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE													=					F'\n            run_glue.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            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

								with patch.object(lowerCAmelCase__						,       'argv'						,       lowerCAmelCase__				):
														run_flax_glue.main()
														SCREAMING_SNAKE_CASE													=					get_results(lowerCAmelCase__				)
														self.assertGreaterEqual(result['eval_accuracy']						,       0.75				)







		@slow
		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE													=					F'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

								with patch.object(lowerCAmelCase__						,       'argv'						,       lowerCAmelCase__				):
														run_clm_flax.main()
														SCREAMING_SNAKE_CASE													=					get_results(lowerCAmelCase__				)
														self.assertLess(result['eval_perplexity']						,       100				)







		@slow
		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE													=					F'\n            run_summarization.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            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

								with patch.object(lowerCAmelCase__						,       'argv'						,       lowerCAmelCase__				):
														run_summarization_flax.main()
														SCREAMING_SNAKE_CASE													=					get_results(lowerCAmelCase__						,       split='test'				)
														self.assertGreaterEqual(result['test_rouge1']						,       10				)
														self.assertGreaterEqual(result['test_rouge2']						,       2				)
														self.assertGreaterEqual(result['test_rougeL']						,       7				)
														self.assertGreaterEqual(result['test_rougeLsum']						,       7				)







		@slow
		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE													=					F'\n            run_mlm.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            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

								with patch.object(lowerCAmelCase__						,       'argv'						,       lowerCAmelCase__				):
														run_mlm_flax.main()
														SCREAMING_SNAKE_CASE													=					get_results(lowerCAmelCase__				)
														self.assertLess(result['eval_perplexity']						,       42				)







		@slow
		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE													=					F'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

								with patch.object(lowerCAmelCase__						,       'argv'						,       lowerCAmelCase__				):
														run_ta_mlm_flax.main()
														SCREAMING_SNAKE_CASE													=					get_results(lowerCAmelCase__				)
														self.assertGreaterEqual(result['eval_accuracy']						,       0.42				)







		@slow
		def 							__A						(	self				)     ->							Dict:
								# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
								SCREAMING_SNAKE_CASE													=					7 if get_gpu_count() > 1 else 2

								SCREAMING_SNAKE_CASE													=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE													=					F'\n            run_flax_ner.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            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

								with patch.object(lowerCAmelCase__						,       'argv'						,       lowerCAmelCase__				):
														run_flax_ner.main()
														SCREAMING_SNAKE_CASE													=					get_results(lowerCAmelCase__				)
														self.assertGreaterEqual(result['eval_accuracy']						,       0.75				)
														self.assertGreaterEqual(result['eval_f1']						,       0.3				)







		@slow
		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE													=					F'\n            run_qa.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            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

								with patch.object(lowerCAmelCase__						,       'argv'						,       lowerCAmelCase__				):
														run_qa.main()
														SCREAMING_SNAKE_CASE													=					get_results(lowerCAmelCase__				)
														self.assertGreaterEqual(result['eval_f1']						,       30				)
														self.assertGreaterEqual(result['eval_exact']						,       30				)






"""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)

__UpperCamelCase						=      [
    '''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 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







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







		def 							__A						(	self				)     ->							Optional[int]:
								self.one_complete_example('complete_nlp_example.py'						,       lowerCAmelCase__				)
								self.one_complete_example('complete_nlp_example.py'						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					os.path.abspath(os.path.join('examples'						,       'cv_example.py'				)				)
								SCREAMING_SNAKE_CASE													=					[
								    ' ' * 16 + '{\n\n',
								    ' ' * 20 + '"accuracy": eval_metric["accuracy"],\n\n',
								    ' ' * 20 + '"f1": eval_metric["f1"],\n\n',
								    ' ' * 20 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
								    ' ' * 20 + '"epoch": epoch,\n\n',
								    ' ' * 16 + '},\n\n',
								    ' ' * 16 + 'step=epoch,\n',
								    ' ' * 12,
								    ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
								]
								self.one_complete_example('complete_cv_example.py'						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)
								self.one_complete_example('complete_cv_example.py'						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)





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




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[str]								=     False







		@classmethod
		def 							__A						(	cls				)     ->							List[str]:
								super().setUpClass()
								SCREAMING_SNAKE_CASE													=					tempfile.mkdtemp()
								SCREAMING_SNAKE_CASE													=					os.path.join(cls._tmpdir						,       'default_config.yml'				)

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







		@classmethod
		def 							__A						(	cls				)     ->							Dict:
								super().tearDownClass()
								shutil.rmtree(cls._tmpdir				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
								run_command(self._launch_args + testargs				)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,       'epoch_0'				)				)				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs				)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,       'step_2'				)				)				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,       "epoch_0"				)}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
								self.assertNotIn('epoch 0:'						,       lowerCAmelCase__				)
								self.assertIn('epoch 1:'						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					F'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir						,       "step_2"				)}\n        '.split()
								SCREAMING_SNAKE_CASE													=					run_command(self._launch_args + testargs						,       return_stdout=lowerCAmelCase__				)
								if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE													=					torch.cuda.device_count()
								else:
														SCREAMING_SNAKE_CASE													=					1
								if num_processes > 1:
														self.assertNotIn('epoch 0:'						,       lowerCAmelCase__				)
														self.assertIn('epoch 1:'						,       lowerCAmelCase__				)
								else:
														self.assertIn('epoch 0:'						,       lowerCAmelCase__				)
														self.assertIn('epoch 1:'						,       lowerCAmelCase__				)







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







		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/multi_process_metrics.py']
								run_command(self._launch_args + testargs				)







		@require_trackers
		@mock.patch.dict(os.environ						,       {'WANDB_MODE': 'offline'}				)
		def 							__A						(	self				)     ->							Union[str, Any]:
								with tempfile.TemporaryDirectory() as tmpdir:
														SCREAMING_SNAKE_CASE													=					F'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
														run_command(self._launch_args + testargs				)
														self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__						,       'tracking'				)				)				)







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/gradient_accumulation.py']
								run_command(self._launch_args + testargs				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					['examples/by_feature/local_sgd.py']
								run_command(self._launch_args + testargs				)


