


import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_=True			,					lowercase_="pt"			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       int  						=  {'''add_prefix_space''': True} if isinstance(lowercase_			,					lowercase_			) and not line.startswith(''' '''			) else {}
						__UpperCAmelCase :       List[Any]  						=  padding_side
						return tokenizer(
						    [line]			,					max_length=lowercase_			,					padding='''max_length''' if pad_to_max_length else None			,					truncation=lowercase_			,					return_tensors=lowercase_			,					add_special_tokens=lowercase_			,					**lowercase_			,					)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_=None			,					)		->    Tuple:




						'''simple docstring'''







						__UpperCAmelCase :       Union[str, Any]  						=  input_ids.ne(lowercase_			).any(dim=0			)
						if attention_mask is None:
												return input_ids[:, keep_column_mask]
						else:
												return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__="train"							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=""							,    ):
							super().__init__()
							__UpperCAmelCase :       int  						=  Path(lowercase__).joinpath(type_path + '''.source''')
							__UpperCAmelCase :       Dict  						=  Path(lowercase__).joinpath(type_path + '''.target''')
							__UpperCAmelCase :       int  						=  self.get_char_lens(self.src_file)
							__UpperCAmelCase :       Optional[Any]  						=  max_source_length
							__UpperCAmelCase :       str  						=  max_target_length
							assert min(self.src_lens) > 0, F"found empty line in {self.src_file}"
							__UpperCAmelCase :       int  						=  tokenizer
							__UpperCAmelCase :       str  						=  prefix
							if n_obs is not None:
													__UpperCAmelCase :       Any  						=  self.src_lens[:n_obs]
							__UpperCAmelCase :       List[Any]  						=  src_lang
							__UpperCAmelCase :       Dict  						=  tgt_lang


	def __len__(						self):
							return len(self.src_lens)


	def __getitem__(						self							,    lowercase__):
							__UpperCAmelCase :       Union[str, Any]  						=  index + 1  # linecache starts at 1
							__UpperCAmelCase :       List[str]  						=  self.prefix + linecache.getline(str(self.src_file)							,    lowercase__).rstrip('''\n''')
							__UpperCAmelCase :       Optional[int]  						=  linecache.getline(str(self.tgt_file)							,    lowercase__).rstrip('''\n''')
							assert source_line, F"empty source line for index {index}"
							assert tgt_line, F"empty tgt line for index {index}"

							# Need to add eos token manually for T5
							if isinstance(self.tokenizer							,    lowercase__):
													source_line += self.tokenizer.eos_token
													tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							__UpperCAmelCase :       Dict  						=  (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer							,    lowercase__) else self.tokenizer
							)
							__UpperCAmelCase :       Tuple  						=  self.tokenizer.generator if isinstance(self.tokenizer							,    lowercase__) else self.tokenizer

							__UpperCAmelCase :       int  						=  encode_line(lowercase__							,    lowercase__							,    self.max_source_length							,    '''right''')
							__UpperCAmelCase :       Optional[int]  						=  encode_line(lowercase__							,    lowercase__							,    self.max_target_length							,    '''right''')

							__UpperCAmelCase :       Tuple  						=  source_inputs['''input_ids'''].squeeze()
							__UpperCAmelCase :       Optional[Any]  						=  target_inputs['''input_ids'''].squeeze()
							__UpperCAmelCase :       List[Any]  						=  source_inputs['''attention_mask'''].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}


	@staticmethod
	def    A(						lowercase__):
							return [len(lowercase__) for x in Path(lowercase__).open().readlines()]


	def    A(						self							,    lowercase__):
							__UpperCAmelCase :       Optional[int]  						=  torch.stack([x['''input_ids'''] for x in batch])
							__UpperCAmelCase :       str  						=  torch.stack([x['''attention_mask'''] for x in batch])
							__UpperCAmelCase :       Dict  						=  torch.stack([x['''decoder_input_ids'''] for x in batch])
							__UpperCAmelCase :       Optional[Any]  						=  (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer							,    lowercase__)
							    else self.tokenizer.pad_token_id
							)
							__UpperCAmelCase :       Dict  						=  (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer							,    lowercase__)
							    else self.tokenizer.pad_token_id
							)
							__UpperCAmelCase :       str  						=  trim_batch(lowercase__							,    lowercase__)
							__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  trim_batch(lowercase__							,    lowercase__							,    attention_mask=lowercase__)
							__UpperCAmelCase :       Dict  						=  {
							    '''input_ids''': source_ids,
							    '''attention_mask''': source_mask,
							    '''decoder_input_ids''': y,
							}
							return batch






lowerCAmelCase							=						getLogger(__name__)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    List[str]:




						'''simple docstring'''







						return list(itertools.chain.from_iterable(lowercase_			)			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    None:




						'''simple docstring'''







						__UpperCAmelCase :       Any  						=  get_git_info()
						save_json(lowercase_			,					os.path.join(lowercase_			,					'''git_log.json'''			)			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_=4			,					**lowercase_			)		->    Dict:




						'''simple docstring'''







						with open(lowercase_			,					'''w'''			) as f:
												json.dump(lowercase_			,					lowercase_			,					indent=lowercase_			,					**lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    Any:




						'''simple docstring'''







						with open(lowercase_			) as f:
												return json.load(lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					)		->    int:




						'''simple docstring'''







						__UpperCAmelCase :       Any  						=  git.Repo(search_parent_directories=lowercase_			)
						__UpperCAmelCase :       Optional[Any]  						=  {
						    '''repo_id''': str(lowercase_			),
						    '''repo_sha''': str(repo.head.object.hexsha			),
						    '''repo_branch''': str(repo.active_branch			),
						    '''hostname''': str(socket.gethostname()			),
						}
						return repo_infos




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    List:




						'''simple docstring'''







						return list(map(lowercase_			,					lowercase_			)			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Any:




						'''simple docstring'''







						with open(lowercase_			,					'''wb'''			) as f:
												return pickle.dump(lowercase_			,					lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    Union[str, Any]:




						'''simple docstring'''







						def remove_articles(lowercase_			):
												return re.sub(r'''\b(a|an|the)\b'''			,					''' '''			,					lowercase_			)

						def white_space_fix(lowercase_			):
												return " ".join(text.split()			)

						def remove_punc(lowercase_			):
												__UpperCAmelCase :       Optional[Any]  						=  set(string.punctuation			)
												return "".join(ch for ch in text if ch not in exclude			)

						def lower(lowercase_			):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(lowercase_			)			)			)			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Dict:




						'''simple docstring'''







						__UpperCAmelCase :       List[str]  						=  normalize_answer(lowercase_			).split()
						__UpperCAmelCase :       int  						=  normalize_answer(lowercase_			).split()
						__UpperCAmelCase :       Optional[Any]  						=  Counter(lowercase_			) & Counter(lowercase_			)
						__UpperCAmelCase :       int  						=  sum(common.values()			)
						if num_same == 0:
												return 0
						__UpperCAmelCase :       Optional[int]  						=  1.0 * num_same / len(lowercase_			)
						__UpperCAmelCase :       List[Any]  						=  1.0 * num_same / len(lowercase_			)
						__UpperCAmelCase :       Optional[Any]  						=  (2 * precision * recall) / (precision + recall)
						return fa




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Any:




						'''simple docstring'''







						return normalize_answer(lowercase_			) == normalize_answer(lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Dict:




						'''simple docstring'''







						assert len(lowercase_			) == len(lowercase_			)
						__UpperCAmelCase :       Any  						=  0
						for hypo, pred in zip(lowercase_			,					lowercase_			):
												em += exact_match_score(lowercase_			,					lowercase_			)
						if len(lowercase_			) > 0:
												em /= len(lowercase_			)
						return {"em": em}




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			)		->    Tuple:




						'''simple docstring'''







						return model_prefix.startswith('''rag'''			)





def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			)		->    Any:




						'''simple docstring'''







						__UpperCAmelCase :       int  						=  {p: p for p in extra_params}
						# T5 models don't have `dropout` param, they have `dropout_rate` instead
						__UpperCAmelCase :       Optional[int]  						=  '''dropout_rate'''
						for p in extra_params:
												if getattr(lowercase_			,					lowercase_			,					lowercase_			):
																		if not hasattr(lowercase_			,					lowercase_			) and not hasattr(lowercase_			,					equivalent_param[p]			):
																								logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_			)			)
																								delattr(lowercase_			,					lowercase_			)
																								continue
																		__UpperCAmelCase :       List[Any]  						=  p if hasattr(lowercase_			,					lowercase_			) else equivalent_param[p]
																		setattr(lowercase_			,					lowercase_			,					getattr(lowercase_			,					lowercase_			)			)
																		delattr(lowercase_			,					lowercase_			)
						return hparams, config





from typing import Dict, Optional

import numpy as np

import datasets


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

lowerCAmelCase							=						"""
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'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.        ])}
"""

lowerCAmelCase							=						"""\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = False			,					)		->    Optional[Any]:




						'''simple docstring'''







						if label_map is not None:
												for old_id, new_id in label_map.items():
																		__UpperCAmelCase :       List[str]  						=  new_id

    # turn into Numpy arrays
						__UpperCAmelCase :       Tuple  						=  np.array(lowercase_			)
						__UpperCAmelCase :       str  						=  np.array(lowercase_			)

						if reduce_labels:
												__UpperCAmelCase :       List[Any]  						=  255
												__UpperCAmelCase :       str  						=  label - 1
												__UpperCAmelCase :       Dict  						=  255

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

						__UpperCAmelCase :       Optional[Any]  						=  pred_label[pred_label == label]

						__UpperCAmelCase :       Optional[Any]  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]
						__UpperCAmelCase :       Any  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]
						__UpperCAmelCase :       List[str]  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]

						__UpperCAmelCase :       List[Any]  						=  area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = False			,					)		->    Any:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       List[Any]  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       str  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       str  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						for result, gt_seg_map in zip(lowercase_			,					lowercase_			):
												__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  intersect_and_union(
												    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)
												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 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = None			,					lowercase_ = False			,					)		->    str:




						'''simple docstring'''







						__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  total_intersect_and_union(
						    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)

						# compute metrics
						__UpperCAmelCase :       Any  						=  {}

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

						__UpperCAmelCase :       Optional[int]  						=  np.nanmean(lowercase_			)
						__UpperCAmelCase :       int  						=  np.nanmean(lowercase_			)
						__UpperCAmelCase :       List[str]  						=  all_acc
						__UpperCAmelCase :       Any  						=  iou
						__UpperCAmelCase :       str  						=  acc

						if nan_to_num is not None:
												__UpperCAmelCase :       Any  						=  {metric: np.nan_to_num(lowercase_			,					nan=lowercase_			) for metric, metric_value in metrics.items()}

						return metrics





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


	def    A(						self):
							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    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = False							,    ):
							__UpperCAmelCase :       str  						=  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

