






'''simple docstring'''





import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_UpperCamelCase						:						List[Any]  =					getLogger(__name__)
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			str			,       A					:			str			,       A					:			int = 8			,       A					:			int = 1_0_2_4			,       A					:			List[Any]="val"			,       A					:			Tuple=None			,       A					:			str=False			,       A					:			Union[str, Any]="summarization"			,       A					:			Tuple=None			,       A					:			List[Any]=1			,       A					:			Dict = None			,       A					:			Any=""			,       **A					:			Union[str, Any]			,       )    ->   Dict:
			UpperCAmelCase_      :       List[Any]          =       str(lowerCamelCase_		)
			assert local_rank is not None
			torch.distributed.init_process_group(backend='''nccl'''			,       rank=lowerCamelCase_		)

			UpperCAmelCase_      :       List[Any]          =       Path(lowerCamelCase_		)
			UpperCAmelCase_      :       str          =       save_dir.joinpath(F"rank_{local_rank}_output.json"		)
			torch.cuda.set_device(lowerCamelCase_		)
			UpperCAmelCase_      :       List[Any]          =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_		).cuda()
			if fpaa:
						UpperCAmelCase_      :       List[str]          =       model.half()
			# determine if we need to increase num_beams
			use_task_specific_params(lowerCamelCase_			,       lowerCamelCase_		)  # update config with task specific params
			UpperCAmelCase_      :       List[str]          =       generate_kwargs.pop('''num_beams'''			,       model.config.num_beams		)  # AttributeError risk?
			if num_return_sequences > num_beams:
						UpperCAmelCase_      :       Optional[int]          =       num_return_sequences

			UpperCAmelCase_      :       Any          =       AutoTokenizer.from_pretrained(lowerCamelCase_		)
			logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

			if max_source_length is None:
						UpperCAmelCase_      :       Optional[int]          =       tokenizer.model_max_length
			if prefix is None:
						UpperCAmelCase_      :       Optional[int]          =       prefix or getattr(model.config			,       '''prefix'''			,       ''''''		) or ''
			UpperCAmelCase_      :       List[Any]          =       SeqaSeqDataset(
			    lowerCamelCase_			,       lowerCamelCase_			,       lowerCamelCase_			,       max_target_length=1_0_2_4			,       type_path=lowerCamelCase_			,       n_obs=lowerCamelCase_			,       prefix=lowerCamelCase_			,       **lowerCamelCase_			,       )
			# I set shuffle=True for a more accurate progress bar.
			# If all the longest samples are first, the prog bar estimate is too high at the beginning.
			UpperCAmelCase_      :       List[Any]          =       ds.make_sortish_sampler(lowerCamelCase_			,       distributed=lowerCamelCase_			,       add_extra_examples=lowerCamelCase_			,       shuffle=lowerCamelCase_		)
			UpperCAmelCase_      :       int          =       DataLoader(lowerCamelCase_			,       sampler=lowerCamelCase_			,       batch_size=lowerCamelCase_			,       collate_fn=ds.collate_fn		)
			UpperCAmelCase_      :       Union[str, Any]          =       []
			for batch in tqdm(lowerCamelCase_		):
						UpperCAmelCase_      :       Any          =       model.generate(
						    input_ids=batch['''input_ids'''].to(model.device		)			,       attention_mask=batch['''attention_mask'''].to(model.device		)			,       num_return_sequences=lowerCamelCase_			,       num_beams=lowerCamelCase_			,       **lowerCamelCase_			,       )
						UpperCAmelCase_      :       List[Any]          =       tokenizer.batch_decode(lowerCamelCase_			,       skip_special_tokens=lowerCamelCase_			,       clean_up_tokenization_spaces=lowerCamelCase_		)
						UpperCAmelCase_      :       Optional[int]          =       batch['ids']
						if num_return_sequences > 1:
									UpperCAmelCase_      :       List[Any]          =       chunks(lowerCamelCase_			,       lowerCamelCase_		)  # batch size chunks, each of size num_return_seq
						for i, pred in enumerate(lowerCamelCase_		):
									results.append({'''pred''': pred, '''id''': ids[i].item()}		)
			save_json(lowerCamelCase_			,       lowerCamelCase_		)
			return results, sampler.num_replicas
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Any          =       argparse.ArgumentParser(
			    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''		)
			parser.add_argument('''--data_dir'''			,       type=lowerCamelCase_			,       help='''like cnn_dm/test.source'''		)
			parser.add_argument(
			    '''--model_name'''			,       type=lowerCamelCase_			,       help='''like facebook/bart-large-cnn,t5-base, etc.'''			,       default='''sshleifer/distilbart-xsum-12-3'''			,       )
			parser.add_argument('''--save_dir'''			,       type=lowerCamelCase_			,       help='''where to save'''			,       default='''tmp_gen'''		)
			parser.add_argument('''--max_source_length'''			,       type=lowerCamelCase_			,       default=lowerCamelCase_		)
			parser.add_argument(
			    '''--type_path'''			,       type=lowerCamelCase_			,       default='''test'''			,       help='''which subset to evaluate typically train/val/test'''		)
			parser.add_argument('''--task'''			,       type=lowerCamelCase_			,       default='''summarization'''			,       help='''used for task_specific_params + metrics'''		)
			parser.add_argument('''--bs'''			,       type=lowerCamelCase_			,       default=8			,       required=lowerCamelCase_			,       help='''batch size'''		)
			parser.add_argument(
			    '''--local_rank'''			,       type=lowerCamelCase_			,       default=-1			,       required=lowerCamelCase_			,       help='''should be passed by distributed.launch'''		)

			parser.add_argument(
			    '''--n_obs'''			,       type=lowerCamelCase_			,       default=lowerCamelCase_			,       required=lowerCamelCase_			,       help='''How many observations. Defaults to all.'''		)
			parser.add_argument(
			    '''--num_return_sequences'''			,       type=lowerCamelCase_			,       default=1			,       required=lowerCamelCase_			,       help='''How many sequences to return'''		)
			parser.add_argument(
			    '''--sync_timeout'''			,       type=lowerCamelCase_			,       default=6_0_0			,       required=lowerCamelCase_			,       help='''How long should master process wait for other processes to finish.'''			,       )
			parser.add_argument('''--src_lang'''			,       type=lowerCamelCase_			,       default=lowerCamelCase_			,       required=lowerCamelCase_		)
			parser.add_argument('''--tgt_lang'''			,       type=lowerCamelCase_			,       default=lowerCamelCase_			,       required=lowerCamelCase_		)
			parser.add_argument(
			    '''--prefix'''			,       type=lowerCamelCase_			,       required=lowerCamelCase_			,       default=lowerCamelCase_			,       help='''will be added to the begininng of src examples'''		)
			parser.add_argument('''--fp16'''			,       action='''store_true'''		)
			parser.add_argument('''--debug'''			,       action='''store_true'''		)
			UpperCAmelCase_      :       List[Any]          =       time.time()
			UpperCAmelCase_      :       str          =       parser.parse_known_args()
			UpperCAmelCase_      :       List[str]          =       parse_numeric_n_bool_cl_kwargs(lowerCamelCase_		)
			if generate_kwargs and args.local_rank <= 0:
						print(F"parsed the following generate kwargs: {generate_kwargs}"		)
			UpperCAmelCase_      :       Tuple          =       Path(args.save_dir + '''_tmp'''		)
			Path(lowerCamelCase_		).mkdir(exist_ok=lowerCamelCase_		)  # this handles locking.
			UpperCAmelCase_      :       Dict          =       list(json_save_dir.glob('''rank_*.json'''		)		)
			if intermediate_files:
						raise ValueError(F"Found files at {json_save_dir} please move or remove them."		)
						# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
			UpperCAmelCase_      :       Any          =       {}
			if args.src_lang is not None:
						UpperCAmelCase_      :       Dict          =       args.src_lang
			if args.tgt_lang is not None:
						UpperCAmelCase_      :       Tuple          =       args.tgt_lang

			Path(args.save_dir		).mkdir(exist_ok=lowerCamelCase_		)
			UpperCAmelCase_      :       str          =       eval_data_dir(
			    args.data_dir			,       lowerCamelCase_			,       args.model_name			,       type_path=args.type_path			,       bs=args.bs			,       fpaa=args.fpaa			,       task=args.task			,       local_rank=args.local_rank			,       n_obs=args.n_obs			,       max_source_length=args.max_source_length			,       num_return_sequences=args.num_return_sequences			,       prefix=args.prefix			,       dataset_kwargs=lowerCamelCase_			,       **lowerCamelCase_			,       )

			if args.local_rank <= 0:
						UpperCAmelCase_      :       Tuple          =       Path(args.save_dir		)
						save_dir.mkdir(exist_ok=lowerCamelCase_		)
						UpperCAmelCase_      :       int          =       gather_results_from_each_node(lowerCamelCase_			,       lowerCamelCase_			,       args.sync_timeout		)
						UpperCAmelCase_      :       int          =       combine_partial_results(lowerCamelCase_		)
						if args.num_return_sequences > 1:
									UpperCAmelCase_      :       List[Any]          =       save_dir.joinpath('''pseudolabel_results.json'''		)
									print(F"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"		)
									save_json(lowerCamelCase_			,       lowerCamelCase_		)
									return
						UpperCAmelCase_      :       Any          =       Path(args.data_dir		).joinpath(args.type_path + '''.target'''		)
						with open(lowerCamelCase_		) as f:
									UpperCAmelCase_      :       Union[str, Any]          =       [x.rstrip() for x in f.readlines()][: len(lowerCamelCase_		)]

						# Calculate metrics, save metrics,  and save _generations.txt
						UpperCAmelCase_      :       Dict          =       'translation' in args.task
						UpperCAmelCase_      :       Optional[Any]          =       calculate_bleu if calc_bleu else calculate_rouge
						UpperCAmelCase_      :       Optional[Any]          =       'bleu' if calc_bleu else 'rouge'
						UpperCAmelCase_      :       Dict          =       score_fn(lowerCamelCase_			,       lowerCamelCase_		)
						UpperCAmelCase_      :       List[str]          =       len(lowerCamelCase_		)
						UpperCAmelCase_      :       Optional[Any]          =       time.time() - start_time
						UpperCAmelCase_      :       List[Any]          =       round(runtime / metrics['''n_obs''']			,       4		)
						UpperCAmelCase_      :       Union[str, Any]          =       num_replicas
						# TODO(@stas00): add whatever metadata to metrics
						UpperCAmelCase_      :       List[str]          =       save_dir.joinpath(F"{args.type_path}_{metric_name}.json"		)
						save_json(lowerCamelCase_			,       lowerCamelCase_			,       indent=lowerCamelCase_		)
						print(lowerCamelCase_		)
						write_txt_file(lowerCamelCase_			,       save_dir.joinpath(F"{args.type_path}_generations.txt"		)		)
						if args.debug:
									write_txt_file(lowerCamelCase_			,       save_dir.joinpath(F"{args.type_path}.target"		)		)
						else:
									shutil.rmtree(lowerCamelCase_		)
def __UpperCAmelCase   (					A					:			int		)    ->   List:
			UpperCAmelCase_      :       Any          =       []
			for partial_result in partial_results:
						records.extend(lowerCamelCase_		)
			UpperCAmelCase_      :       Optional[int]          =       sorted(lowerCamelCase_			,       key=lambda A		: x["id"]		)
			UpperCAmelCase_      :       List[str]          =       [x['pred'] for x in records]
			return preds
def __UpperCAmelCase   (					A					:			Dict			,       A					:			Union[str, Any]			,       A					:			str		)    ->   List[Dict[str, List]]:
			UpperCAmelCase_      :       Union[str, Any]          =       time.time()
			logger.info('''waiting for all nodes to finish'''		)
			UpperCAmelCase_      :       Optional[int]          =       None
			while (time.time() - start_wait) < timeout:
						UpperCAmelCase_      :       str          =       list(save_dir.glob('''rank_*.json'''		)		)
						if len(lowerCamelCase_		) < num_replicas:
									continue
						try:
									# make sure all json files are fully saved
									UpperCAmelCase_      :       Tuple          =       lmap(lowerCamelCase_			,       lowerCamelCase_		)
									return json_data
						except JSONDecodeError:
									continue
			else:
						raise TimeoutError('''Rank 0 gave up on waiting for other processes'''		)
			# Unreachable


if __name__ == "__main__":
						# Usage for MT:
						run_generate()








import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


UpperCamelCase__ :   List[Any]						=							pytest.mark.integration





@pytest.mark.parametrize('path'  ,							['paws', 'csv']  )
def        __UpperCAmelCase						(							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  )       ->  Any:







			"""simple docstring"""





			inspect_dataset(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      path + '.py'
			assert script_name in os.listdir(lowerCamelCase_  )
			assert "__pycache__" not in os.listdir(lowerCamelCase_  )





@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning'  )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'  )
@pytest.mark.parametrize('path'  ,							['accuracy']  )
def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  Optional[Any]:







			"""simple docstring"""





			inspect_metric(lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      path + '.py'
			assert script_name in os.listdir(lowerCamelCase_  )
			assert "__pycache__" not in os.listdir(lowerCamelCase_  )





@pytest.mark.parametrize(
    'path, config_name, expected_splits'  ,							[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      get_dataset_config_info(lowerCamelCase_  ,							config_name=lowerCamelCase_  )
			assert info.config_name == config_name
			assert list(info.splits.keys()  ) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'  ,							[
        ('paws', None, ValueError),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     int  )       ->  List[Any]:







			"""simple docstring"""





			with pytest.raises(lowerCamelCase_  ):
						get_dataset_config_info(lowerCamelCase_  ,							config_name=lowerCamelCase_  )





@pytest.mark.parametrize(
    'path, expected'  ,							[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     List[Any]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_config_names(lowerCamelCase_  )
			assert expected in config_names





@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'  ,							[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     Union[str, Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      get_dataset_infos(lowerCamelCase_  )
			assert list(infos.keys()  ) == expected_configs
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      expected_configs[0]
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : Any					  =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()  ) == expected_splits_in_first_config





@pytest.mark.parametrize(
    'path, expected_config, expected_splits'  ,							[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     str  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      get_dataset_infos(lowerCamelCase_  )
			assert expected_config in infos
			SCREAMING_SNAKE_CASE_   : Tuple					  =      infos[expected_config]
			assert info.config_name == expected_config
			assert list(info.splits.keys()  ) == expected_splits





@pytest.mark.parametrize(
    'path, config_name, expected_exception'  ,							[
        ('paws', None, ValueError),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





			with pytest.raises(lowerCamelCase_  ):
						get_dataset_split_names(lowerCamelCase_  ,							config_name=lowerCamelCase_  )



