


"""simple docstring"""




import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


__UpperCamelCase						=      getLogger(__name__)


__UpperCamelCase						=      '''cuda''' if torch.cuda.is_available() else '''cpu'''


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				int = 8 ,			SCREAMING_SNAKE_CASE_		:				str = DEFAULT_DEVICE ,			SCREAMING_SNAKE_CASE_		:				List[Any]=False ,			SCREAMING_SNAKE_CASE_		:				str="summarization" ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			**SCREAMING_SNAKE_CASE_		:				Optional[int] ,			)   ->						Dict:
						SCREAMING_SNAKE_CASE													=					Path(SCREAMING_SNAKE_CASE_  ).open('w' ,			encoding='utf-8'  )
						SCREAMING_SNAKE_CASE													=					str(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )
						if fpaa:
												SCREAMING_SNAKE_CASE													=					model.half()

						SCREAMING_SNAKE_CASE													=					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )
						logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'  )  # if this is wrong, check config.model_type.

						SCREAMING_SNAKE_CASE													=					time.time()
						# update config with task specific params
						use_task_specific_params(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						if prefix is None:
												SCREAMING_SNAKE_CASE													=					prefix or getattr(model.config ,			'prefix' ,			''  ) or ''
						for examples_chunk in tqdm(list(chunks(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )  )  ):
												SCREAMING_SNAKE_CASE													=					[prefix + text for text in examples_chunk]
												SCREAMING_SNAKE_CASE													=					tokenizer(SCREAMING_SNAKE_CASE_ ,			return_tensors='pt' ,			truncation=SCREAMING_SNAKE_CASE_ ,			padding='longest'  ).to(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					model.generate(
												    input_ids=batch.input_ids ,			attention_mask=batch.attention_mask ,			**SCREAMING_SNAKE_CASE_ ,			)
												SCREAMING_SNAKE_CASE													=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ ,			skip_special_tokens=SCREAMING_SNAKE_CASE_ ,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_  )
												for hypothesis in dec:
																		fout.write(hypothesis + '\n'  )
																		fout.flush()
						fout.close()
						SCREAMING_SNAKE_CASE													=					int(time.time() - start_time  )  # seconds
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  )
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,			4  )}


def     lowercase			()   ->						int:
						return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any=True  )   ->						Tuple:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			help='like facebook/bart-large-cnn,t5-base, etc.'  )
						parser.add_argument('input_path' ,			type=SCREAMING_SNAKE_CASE_ ,			help='like cnn_dm/test.source'  )
						parser.add_argument('save_path' ,			type=SCREAMING_SNAKE_CASE_ ,			help='where to save summaries'  )
						parser.add_argument('--reference_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='like cnn_dm/test.target'  )
						parser.add_argument('--score_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			default='metrics.json' ,			help='where to save metrics'  )
						parser.add_argument('--device' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			help='cuda, cuda:1, cpu etc.'  )
						parser.add_argument(
						    '--prefix' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			help='will be added to the begininng of src examples'  )
						parser.add_argument('--task' ,			type=SCREAMING_SNAKE_CASE_ ,			default='summarization' ,			help='used for task_specific_params + metrics'  )
						parser.add_argument('--bs' ,			type=SCREAMING_SNAKE_CASE_ ,			default=8 ,			required=SCREAMING_SNAKE_CASE_ ,			help='batch size'  )
						parser.add_argument(
						    '--n_obs' ,			type=SCREAMING_SNAKE_CASE_ ,			default=-1 ,			required=SCREAMING_SNAKE_CASE_ ,			help='How many observations. Defaults to all.'  )
						parser.add_argument('--fp16' ,			action='store_true'  )
						parser.add_argument('--dump-args' ,			action='store_true' ,			help='print the custom hparams with the results'  )
						parser.add_argument(
						    '--info' ,			nargs='?' ,			type=SCREAMING_SNAKE_CASE_ ,			const=datetime_now() ,			help=(
						        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
						        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
						    ) ,			)
						# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					parser.parse_known_args()
						SCREAMING_SNAKE_CASE													=					parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE_  )
						if parsed_args and verbose:
												print(F'parsed the following generate kwargs: {parsed_args}'  )
						SCREAMING_SNAKE_CASE													=					[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path  ).readlines()]
						if args.n_obs > 0:
												SCREAMING_SNAKE_CASE													=					examples[: args.n_obs]
						Path(args.save_path  ).parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )

						if args.reference_path is None and Path(args.score_path  ).exists():
												warnings.warn(F'score_path {args.score_path} will be overwritten unless you type ctrl-c.'  )

						if args.device == "cpu" and args.fpaa:
												# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
												raise ValueError('Can\'t mix --fp16 and --device cpu'  )

						SCREAMING_SNAKE_CASE													=					generate_summaries_or_translations(
						    SCREAMING_SNAKE_CASE_ ,			args.save_path ,			args.model_name ,			batch_size=args.bs ,			device=args.device ,			fpaa=args.fpaa ,			task=args.task ,			prefix=args.prefix ,			**SCREAMING_SNAKE_CASE_ ,			)

						if args.reference_path is None:
												return {}

						# Compute scores
						SCREAMING_SNAKE_CASE													=					calculate_bleu if 'translation' in args.task else calculate_rouge
						SCREAMING_SNAKE_CASE													=					[x.rstrip() for x in open(args.save_path  ).readlines()]
						SCREAMING_SNAKE_CASE													=					[x.rstrip() for x in open(args.reference_path  ).readlines()][: len(SCREAMING_SNAKE_CASE_  )]
						SCREAMING_SNAKE_CASE													=					score_fn(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						scores.update(SCREAMING_SNAKE_CASE_  )

						if args.dump_args:
												scores.update(SCREAMING_SNAKE_CASE_  )
						if args.info:
												SCREAMING_SNAKE_CASE													=					args.info

						if verbose:
												print(SCREAMING_SNAKE_CASE_  )

						if args.score_path is not None:
												json.dump(SCREAMING_SNAKE_CASE_ ,			open(args.score_path ,			'w'  )  )

						return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)






"""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				)


