






'''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,
)


A:      Dict  				=				getLogger(__name__)






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





						"""simple docstring"""
						lowercase_      :   List[str]        =			str(a							)
						assert local_rank is not None
						torch.distributed.init_process_group(backend='nccl'						,			rank=a							)

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

						lowercase_      :   Any        =			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:
												lowercase_      :   Optional[int]        =			tokenizer.model_max_length
						if prefix is None:
												lowercase_      :   int        =			prefix or getattr(model.config						,			'prefix'						,			''							) or ''''''
						lowercase_      :   Union[str, Any]        =			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.
						lowercase_      :   Tuple        =			ds.make_sortish_sampler(a						,			distributed=a						,			add_extra_examples=a						,			shuffle=a							)
						lowercase_      :   List[Any]        =			DataLoader(a						,			sampler=a						,			batch_size=a						,			collate_fn=ds.collate_fn							)
						lowercase_      :   Any        =			[]
						for batch in tqdm(a							):
												lowercase_      :   str        =			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						,			)
												lowercase_      :   Tuple        =			tokenizer.batch_decode(a						,			skip_special_tokens=a						,			clean_up_tokenization_spaces=a							)
												lowercase_      :   Tuple        =			batch['''ids''']
												if num_return_sequences > 1:
																		lowercase_      :   str        =			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 	_UpperCAmelCase    (  )			->			Dict:





						"""simple docstring"""
						lowercase_      :   Dict        =			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'							)
						lowercase_      :   Any        =			time.time()
						lowercase_      :   List[Any]        =			parser.parse_known_args()
						lowercase_      :   Union[str, Any]        =			parse_numeric_n_bool_cl_kwargs(a							)
						if generate_kwargs and args.local_rank <= 0:
												print(f"parsed the following generate kwargs: {generate_kwargs}"							)
						lowercase_      :   str        =			Path(args.save_dir + '_tmp'							)
						Path(a							).mkdir(exist_ok=a							)  # this handles locking.
						lowercase_      :   List[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.
						lowercase_      :   Optional[Any]        =			{}
						if args.src_lang is not None:
												lowercase_      :   Union[str, Any]        =			args.src_lang
						if args.tgt_lang is not None:
												lowercase_      :   Optional[int]        =			args.tgt_lang

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

												# Calculate metrics, save metrics,  and save _generations.txt
												lowercase_      :   Any        =			'''translation''' in args.task
												lowercase_      :   List[str]        =			calculate_bleu if calc_bleu else calculate_rouge
												lowercase_      :   Union[str, Any]        =			'''bleu''' if calc_bleu else '''rouge'''
												lowercase_      :   Dict        =			score_fn(a						,			a							)
												lowercase_      :   int        =			len(a							)
												lowercase_      :   Union[str, Any]        =			time.time() - start_time
												lowercase_      :   List[Any]        =			round(runtime / metrics['n_obs']						,			4							)
												lowercase_      :   Union[str, Any]        =			num_replicas
												# TODO(@stas00): add whatever metadata to metrics
												lowercase_      :   Tuple        =			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 	_UpperCAmelCase    (  a		:	Dict							)			->			str:





						"""simple docstring"""
						lowercase_      :   int        =			[]
						for partial_result in partial_results:
												records.extend(a							)
						lowercase_      :   List[str]        =			sorted(a						,			key=lambda a							: x["id"]							)
						lowercase_      :   Optional[Any]        =			[x['''pred'''] for x in records]
						return preds






def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	Tuple						,			a		:	List[Any]							)			->			Union[str, Any]:





						"""simple docstring"""
						lowercase_      :   Optional[int]        =			time.time()
						logger.info('waiting for all nodes to finish'							)
						lowercase_      :   str        =			None
						while (time.time() - start_wait) < timeout:
												lowercase_      :   int        =			list(save_dir.glob('rank_*.json'							)							)
												if len(a							) < num_replicas:
																		continue
												try:
																		# make sure all json files are fully saved
																		lowercase_      :   List[Any]        =			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()








'''simple docstring'''

from typing import Dict, Optional

import numpy as np

import datasets


A:      int  				=				"\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

A:      List[str]  				=				"\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

A:      Union[str, Any]  				=				"\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"






def 	_UpperCAmelCase    (  a		:	str						,			a		:	Union[str, Any]						,			a		:	Dict						,			a		:	bool						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			str:





						"""simple docstring"""
						if label_map is not None:
												for old_id, new_id in label_map.items():
																		lowercase_      :   Union[str, Any]        =			new_id

    # turn into Numpy arrays
						lowercase_      :   List[Any]        =			np.array(a							)
						lowercase_      :   Optional[Any]        =			np.array(a							)

						if reduce_labels:
												lowercase_      :   Any        =			2_5_5
												lowercase_      :   Dict        =			label - 1
												lowercase_      :   List[Any]        =			2_5_5

						lowercase_      :   Any        =			label != ignore_index
						lowercase_      :   List[Any]        =			np.not_equal(a						,			a							)
						lowercase_      :   Optional[int]        =			pred_label[mask]
						lowercase_      :   Union[str, Any]        =			np.array(a							)[mask]

						lowercase_      :   Optional[int]        =			pred_label[pred_label == label]

						lowercase_      :   Optional[int]        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]
						lowercase_      :   Optional[int]        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]
						lowercase_      :   Dict        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]

						lowercase_      :   Optional[Any]        =			area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label






def 	_UpperCAmelCase    (  a		:	int						,			a		:	Optional[Any]						,			a		:	Optional[int]						,			a		:	bool						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			Dict:





						"""simple docstring"""
						lowercase_      :   Dict        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   List[str]        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   List[Any]        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   str        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						for result, gt_seg_map in zip(a						,			a							):
												lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   Tuple        =			intersect_and_union(
												    a						,			a						,			a						,			a						,			a						,			a							)
												total_area_intersect += area_intersect
												total_area_union += area_union
												total_area_pred_label += area_pred_label
												total_area_label += area_label
						return total_area_intersect, total_area_union, total_area_pred_label, total_area_label






def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	List[str]						,			a		:	Optional[Any]						,			a		:	bool						,			a		:	Optional[int] = None						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			Optional[int]:





						"""simple docstring"""
						lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   Optional[Any]        =			total_intersect_and_union(
						    a						,			a						,			a						,			a						,			a						,			a							)

						# compute metrics
						lowercase_      :   str        =			{}

						lowercase_      :   str        =			total_area_intersect.sum() / total_area_label.sum()
						lowercase_      :   Optional[Any]        =			total_area_intersect / total_area_union
						lowercase_      :   List[Any]        =			total_area_intersect / total_area_label

						lowercase_      :   Any        =			np.nanmean(a							)
						lowercase_      :   Optional[Any]        =			np.nanmean(a							)
						lowercase_      :   int        =			all_acc
						lowercase_      :   Union[str, Any]        =			iou
						lowercase_      :   Optional[Any]        =			acc

						if nan_to_num is not None:
												lowercase_      :   Optional[int]        =			{metric: np.nan_to_num(a						,			nan=a							) for metric, metric_value in metrics.items()}

						return metrics






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







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  int:
							return datasets.MetricInfo(
							    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
							        # 1st Seq - height dim, 2nd - width dim
							        {
							            'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'       )       )       ),
							            'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'       )       )       ),
							        }       )  ,      reference_urls=[
							        'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
							    ]  ,      )







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = False  ,      )						->  Tuple:
							lowercase_      :   Optional[int]        =			mean_iou(
							    results=_lowercase  ,      gt_seg_maps=_lowercase  ,      num_labels=_lowercase  ,      ignore_index=_lowercase  ,      nan_to_num=_lowercase  ,      label_map=_lowercase  ,      reduce_labels=_lowercase  ,      )
							return iou_result
