





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_     							= getLogger(__name__)




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Union[str, Any] = 8		,   __UpperCAmelCase:			Optional[int] = 1024		,   __UpperCAmelCase:			Optional[Any]="val"		,   __UpperCAmelCase:			int=None		,   __UpperCAmelCase:			Any=False		,   __UpperCAmelCase:			Any="summarization"		,   __UpperCAmelCase:			Union[str, Any]=None		,   __UpperCAmelCase:			List[Any]=1		,   __UpperCAmelCase:			str = None		,   __UpperCAmelCase:			Optional[Any]=""		,   **__UpperCAmelCase:			str		,   )      ->	Optional[Any]:
    UpperCamelCase__							:				List[str]          =      str(lowercase__      )
    assert local_rank is not None
    torch.distributed.init_process_group(backend='''nccl'''		,   rank=lowercase__      )

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

    UpperCamelCase__							:				List[Any]          =      AutoTokenizer.from_pretrained(lowercase__      )
    logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"      )  # if this is wrong, check config.model_type.

    if max_source_length is None:
        UpperCamelCase__							:				Tuple          =      tokenizer.model_max_length
    if prefix is None:
        UpperCamelCase__							:				Optional[Any]          =      prefix or getattr(model.config		,   '''prefix'''		,   ''''''      ) or ''
    UpperCamelCase__							:				int          =      SeqaSeqDataset(
        lowercase__		,   lowercase__		,   lowercase__		,   max_target_length=1024		,   type_path=lowercase__		,   n_obs=lowercase__		,   prefix=lowercase__		,   **lowercase__		,   )
    # 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(lowercase__		,   distributed=lowercase__		,   add_extra_examples=lowercase__		,   shuffle=lowercase__      )
    UpperCamelCase__							:				List[Any]          =      DataLoader(lowercase__		,   sampler=lowercase__		,   batch_size=lowercase__		,   collate_fn=ds.collate_fn      )
    UpperCamelCase__							:				Optional[Any]          =      []
    for batch in tqdm(lowercase__      ):
        UpperCamelCase__							:				Tuple          =      model.generate(
            input_ids=batch['''input_ids'''].to(model.device      )		,   attention_mask=batch['''attention_mask'''].to(model.device      )		,   num_return_sequences=lowercase__		,   num_beams=lowercase__		,   **lowercase__		,   )
        UpperCamelCase__							:				Tuple          =      tokenizer.batch_decode(lowercase__		,   skip_special_tokens=lowercase__		,   clean_up_tokenization_spaces=lowercase__      )
        UpperCamelCase__							:				str          =      batch['ids']
        if num_return_sequences > 1:
            UpperCamelCase__							:				Any          =      chunks(lowercase__		,   lowercase__      )  # batch size chunks, each of size num_return_seq
        for i, pred in enumerate(lowercase__      ):
            results.append({'''pred''': pred, '''id''': ids[i].item()}      )
    save_json(lowercase__		,   lowercase__      )
    return results, sampler.num_replicas




def 		lowerCAmelCase_    (  )      ->	Any:
    UpperCamelCase__							:				str          =      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=lowercase__		,   help='''like cnn_dm/test.source'''      )
    parser.add_argument(
        '''--model_name'''		,   type=lowercase__		,   help='''like facebook/bart-large-cnn,t5-base, etc.'''		,   default='''sshleifer/distilbart-xsum-12-3'''		,   )
    parser.add_argument('''--save_dir'''		,   type=lowercase__		,   help='''where to save'''		,   default='''tmp_gen'''      )
    parser.add_argument('''--max_source_length'''		,   type=lowercase__		,   default=lowercase__      )
    parser.add_argument(
        '''--type_path'''		,   type=lowercase__		,   default='''test'''		,   help='''which subset to evaluate typically train/val/test'''      )
    parser.add_argument('''--task'''		,   type=lowercase__		,   default='''summarization'''		,   help='''used for task_specific_params + metrics'''      )
    parser.add_argument('''--bs'''		,   type=lowercase__		,   default=8		,   required=lowercase__		,   help='''batch size'''      )
    parser.add_argument(
        '''--local_rank'''		,   type=lowercase__		,   default=-1		,   required=lowercase__		,   help='''should be passed by distributed.launch'''      )

    parser.add_argument(
        '''--n_obs'''		,   type=lowercase__		,   default=lowercase__		,   required=lowercase__		,   help='''How many observations. Defaults to all.'''      )
    parser.add_argument(
        '''--num_return_sequences'''		,   type=lowercase__		,   default=1		,   required=lowercase__		,   help='''How many sequences to return'''      )
    parser.add_argument(
        '''--sync_timeout'''		,   type=lowercase__		,   default=600		,   required=lowercase__		,   help='''How long should master process wait for other processes to finish.'''		,   )
    parser.add_argument('''--src_lang'''		,   type=lowercase__		,   default=lowercase__		,   required=lowercase__      )
    parser.add_argument('''--tgt_lang'''		,   type=lowercase__		,   default=lowercase__		,   required=lowercase__      )
    parser.add_argument(
        '''--prefix'''		,   type=lowercase__		,   required=lowercase__		,   default=lowercase__		,   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__							:				str          =      time.time()
    UpperCamelCase__							:				List[Any]          =      parser.parse_known_args()
    UpperCamelCase__							:				Any          =      parse_numeric_n_bool_cl_kwargs(lowercase__      )
    if generate_kwargs and args.local_rank <= 0:
        print(f"parsed the following generate kwargs: {generate_kwargs}"      )
    UpperCamelCase__							:				List[Any]          =      Path(args.save_dir + '''_tmp'''      )
    Path(lowercase__      ).mkdir(exist_ok=lowercase__      )  # this handles locking.
    UpperCamelCase__							:				Any          =      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__							:				Optional[Any]          =      {}
    if args.src_lang is not None:
        UpperCamelCase__							:				Tuple          =      args.src_lang
    if args.tgt_lang is not None:
        UpperCamelCase__							:				Tuple          =      args.tgt_lang

    Path(args.save_dir      ).mkdir(exist_ok=lowercase__      )
    UpperCamelCase__							:				Optional[Any]          =      eval_data_dir(
        args.data_dir		,   lowercase__		,   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=lowercase__		,   **lowercase__		,   )

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

        # Calculate metrics, save metrics,  and save _generations.txt
        UpperCamelCase__							:				str          =      'translation' in args.task
        UpperCamelCase__							:				Tuple          =      calculate_bleu if calc_bleu else calculate_rouge
        UpperCamelCase__							:				List[str]          =      'bleu' if calc_bleu else 'rouge'
        UpperCamelCase__							:				Dict          =      score_fn(lowercase__		,   lowercase__      )
        UpperCamelCase__							:				Any          =      len(lowercase__      )
        UpperCamelCase__							:				Dict          =      time.time() - start_time
        UpperCamelCase__							:				Optional[int]          =      round(runtime / metrics['''n_obs''']		,   4      )
        UpperCamelCase__							:				Optional[Any]          =      num_replicas
        # TODO(@stas00): add whatever metadata to metrics
        UpperCamelCase__							:				Union[str, Any]          =      save_dir.joinpath(f"{args.type_path}_{metric_name}.json"      )
        save_json(lowercase__		,   lowercase__		,   indent=lowercase__      )
        print(lowercase__      )
        write_txt_file(lowercase__		,   save_dir.joinpath(f"{args.type_path}_generations.txt"      )      )
        if args.debug:
            write_txt_file(lowercase__		,   save_dir.joinpath(f"{args.type_path}.target"      )      )
        else:
            shutil.rmtree(lowercase__      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[str]      )      ->	int:
    UpperCamelCase__							:				str          =      []
    for partial_result in partial_results:
        records.extend(lowercase__      )
    UpperCamelCase__							:				int          =      sorted(lowercase__		,   key=lambda __UpperCAmelCase      : x["id"]      )
    UpperCamelCase__							:				List[Any]          =      [x['pred'] for x in records]
    return preds




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Any		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			Optional[int]      )      ->	str:
    # WAIT FOR lots of .json files
    UpperCamelCase__							:				str          =      time.time()
    logger.info('''waiting for all nodes to finish'''      )
    UpperCamelCase__							:				Dict          =      None
    while (time.time() - start_wait) < timeout:
        UpperCamelCase__							:				Union[str, Any]          =      list(save_dir.glob('''rank_*.json'''      )      )
        if len(lowercase__      ) < num_replicas:
            continue
        try:
            # make sure all json files are fully saved
            UpperCamelCase__							:				str          =      lmap(lowercase__		,   lowercase__      )
            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()







"""simple docstring"""






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

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

lowercase__     						=						logging.getLogger()







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


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






@require_torch_tpu
class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def 					A_    (  self  ):
							import xla_spawn

							_lowerCamelCase     :			List[Any]    =    self.get_auto_remove_tmp_dir()
							_lowerCamelCase     :			List[Any]    =    F'''
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

							with patch.object(lowercase  ,      'argv'  ,      lowercase  ):
											_lowerCamelCase     :			Dict    =    time()
											xla_spawn.main()
											_lowerCamelCase     :			Any    =    time()

											_lowerCamelCase     :			Optional[int]    =    get_results(lowercase  )
											self.assertGreaterEqual(result['eval_accuracy']  ,      0.75  )

											# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
											self.assertLess(end - start  ,      500  )






			def 					A_    (  self  ):
							import xla_spawn

							_lowerCamelCase     :			Tuple    =    '\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
							with patch.object(lowercase  ,      'argv'  ,      lowercase  ):
											xla_spawn.main()