


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,
)


lowerCAmelCase__    :str       =				getLogger(__name__)

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						int     ,    a__:						List[Any]     ,    a__:						Tuple = 8     ,    a__:						Union[str, Any] = 1_0_2_4     ,    a__:						Optional[Any]="val"     ,    a__:						str=None     ,    a__:						Any=False     ,    a__:						str="summarization"     ,    a__:						str=None     ,    a__:						Dict=1     ,    a__:						str = None     ,    a__:						Any=""     ,    **a__:						List[str]     ,    )    ->						Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase											=     str(A__			)
							assert local_rank is not None
							torch.distributed.init_process_group(backend='nccl'     ,    rank=A__			)

							_UpperCAmelCase											=     Path(A__			)
							_UpperCAmelCase											=     save_dir.joinpath(F'''rank_{local_rank}_output.json'''			)
							torch.cuda.set_device(A__			)
							_UpperCAmelCase											=     AutoModelForSeqaSeqLM.from_pretrained(A__			).cuda()
							if fpaa:
														_UpperCAmelCase											=     model.half()
							# determine if we need to increase num_beams
							use_task_specific_params(A__     ,    A__			)  # update config with task specific params
							_UpperCAmelCase											=     generate_kwargs.pop('num_beams'     ,    model.config.num_beams			)  # AttributeError risk?
							if num_return_sequences > num_beams:
														_UpperCAmelCase											=     num_return_sequences

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

							if max_source_length is None:
														_UpperCAmelCase											=     tokenizer.model_max_length
							if prefix is None:
														_UpperCAmelCase											=     prefix or getattr(model.config     ,    'prefix'     ,    ''			) or ''
							_UpperCAmelCase											=     SeqaSeqDataset(
							    A__     ,    A__     ,    A__     ,    max_target_length=1_0_2_4     ,    type_path=A__     ,    n_obs=A__     ,    prefix=A__     ,    **A__     ,    )
							# 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											=     ds.make_sortish_sampler(A__     ,    distributed=A__     ,    add_extra_examples=A__     ,    shuffle=A__			)
							_UpperCAmelCase											=     DataLoader(A__     ,    sampler=A__     ,    batch_size=A__     ,    collate_fn=ds.collate_fn			)
							_UpperCAmelCase											=     []
							for batch in tqdm(A__			):
														_UpperCAmelCase											=     model.generate(
														    input_ids=batch['input_ids'].to(model.device			)     ,    attention_mask=batch['attention_mask'].to(model.device			)     ,    num_return_sequences=A__     ,    num_beams=A__     ,    **A__     ,    )
														_UpperCAmelCase											=     tokenizer.batch_decode(A__     ,    skip_special_tokens=A__     ,    clean_up_tokenization_spaces=A__			)
														_UpperCAmelCase											=     batch['ids']
														if num_return_sequences > 1:
																					_UpperCAmelCase											=     chunks(A__     ,    A__			)  # batch size chunks, each of size num_return_seq
														for i, pred in enumerate(A__			):
																					results.append({'pred': pred, 'id': ids[i].item()}			)
							save_json(A__     ,    A__			)
							return results, sampler.num_replicas

def 			lowerCAmelCase__					(     )    ->						List[str]:


							'''simple docstring'''





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

							parser.add_argument(
							    '--n_obs'     ,    type=A__     ,    default=A__     ,    required=A__     ,    help='How many observations. Defaults to all.'			)
							parser.add_argument(
							    '--num_return_sequences'     ,    type=A__     ,    default=1     ,    required=A__     ,    help='How many sequences to return'			)
							parser.add_argument(
							    '--sync_timeout'     ,    type=A__     ,    default=6_0_0     ,    required=A__     ,    help='How long should master process wait for other processes to finish.'     ,    )
							parser.add_argument('--src_lang'     ,    type=A__     ,    default=A__     ,    required=A__			)
							parser.add_argument('--tgt_lang'     ,    type=A__     ,    default=A__     ,    required=A__			)
							parser.add_argument(
							    '--prefix'     ,    type=A__     ,    required=A__     ,    default=A__     ,    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											=     time.time()
							_UpperCAmelCase						,			_UpperCAmelCase											=     parser.parse_known_args()
							_UpperCAmelCase											=     parse_numeric_n_bool_cl_kwargs(A__			)
							if generate_kwargs and args.local_rank <= 0:
														print(F'''parsed the following generate kwargs: {generate_kwargs}'''			)
							_UpperCAmelCase											=     Path(args.save_dir + '_tmp'			)
							Path(A__			).mkdir(exist_ok=A__			)  # this handles locking.
							_UpperCAmelCase											=     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											=     {}
							if args.src_lang is not None:
														_UpperCAmelCase											=     args.src_lang
							if args.tgt_lang is not None:
														_UpperCAmelCase											=     args.tgt_lang

							Path(args.save_dir			).mkdir(exist_ok=A__			)
							_UpperCAmelCase						,			_UpperCAmelCase											=     eval_data_dir(
							    args.data_dir     ,    A__     ,    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=A__     ,    **A__     ,    )

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

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

def 			lowerCAmelCase__					(     a__:						Tuple			)    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     []
							for partial_result in partial_results:
														records.extend(A__			)
							_UpperCAmelCase											=     sorted(A__     ,    key=lambda a__			: x["id"]			)
							_UpperCAmelCase											=     [x['pred'] for x in records]
							return preds

def 			lowerCAmelCase__					(     a__:						int     ,    a__:						str     ,    a__:						Tuple			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     time.time()
							logger.info('waiting for all nodes to finish'			)
							_UpperCAmelCase											=     None
							while (time.time() - start_wait) < timeout:
														_UpperCAmelCase											=     list(save_dir.glob('rank_*.json'			)			)
														if len(A__			) < num_replicas:
																					continue
														try:
																					# make sure all json files are fully saved
																					_UpperCAmelCase											=     lmap(A__     ,    A__			)
																					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()





from sklearn.metrics import matthews_corrcoef

import datasets


a__   :					Any 						=      "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

a__   :					Any 						=      "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

a__   :					str 						=      "\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,				_KWARGS_DESCRIPTION						)
class    UpperCAmelCase__(				datasets.Metric						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[Any])						->					int:




         """simple docstring"""

         return datasets.MetricInfo(
             description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
                 {
                     'predictions': datasets.Value('int32'),
                     'references': datasets.Value('int32'),
                 })      ,					reference_urls=[
                 'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
             ]      ,					)



   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any]=None)						->					Any:




         """simple docstring"""

         return {
             "matthews_correlation": float(matthews_corrcoef(lowerCAmelCase      ,					lowerCAmelCase      ,					sample_weight=lowerCAmelCase)),
         }
