

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class  __UpperCAmelCase					:



   def __init__(							self							:					Union[str, Any],		__A							:					List[Any],		__A							:					Optional[Any]=sys.maxsize						):
     UpperCAmelCase	:							Union[str, Any]						=     '''bilinear'''
     UpperCAmelCase	:							int						=     max_size
     UpperCAmelCase	:							Tuple						=     short_edge_length





   def __call__(							self							:					Tuple,		__A							:					Dict						):
     UpperCAmelCase	:							int						=     []
     for img in imgs:
       UpperCAmelCase			,						UpperCAmelCase	:							Optional[Any]						=     img.shape[:2]
       # later: provide list and randomly choose index for resize
       UpperCAmelCase	:							List[Any]						=     np.random.randint(self.short_edge_length[0],		self.short_edge_length[1] + 1						)
       if size == 0:
         return img
       UpperCAmelCase	:							int						=     size * 1.0 / min(__a,		__a						)
       if h < w:
         UpperCAmelCase			,						UpperCAmelCase	:							Dict						=     size, scale * w
       else:
         UpperCAmelCase			,						UpperCAmelCase	:							str						=     scale * h, size
       if max(__a,		__a						) > self.max_size:
         UpperCAmelCase	:							Any						=     self.max_size * 1.0 / max(__a,		__a						)
         UpperCAmelCase	:							Tuple						=     newh * scale
         UpperCAmelCase	:							Optional[Any]						=     neww * scale
       UpperCAmelCase	:							Optional[int]						=     int(neww + 0.5						)
       UpperCAmelCase	:							List[Any]						=     int(newh + 0.5						)

       if img.dtype == np.uinta:
         UpperCAmelCase	:							Union[str, Any]						=     Image.fromarray(__a						)
         UpperCAmelCase	:							List[str]						=     pil_image.resize((neww, newh),		PILImageResampling.BILINEAR						)
         UpperCAmelCase	:							Tuple						=     np.asarray(__a						)
       else:
         UpperCAmelCase	:							Union[str, Any]						=     img.permute(2,		0,		1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
         UpperCAmelCase	:							int						=     nn.functional.interpolate(
             __a,		(newh, neww),		mode=self.interp_method,		align_corners=__a						).squeeze(0						)
       img_augs.append(__a						)

     return img_augs






class  __UpperCAmelCase					:



   def __init__(							self							:					int,		__A							:					List[str]						):
     UpperCAmelCase	:							List[Any]						=     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],		cfg.INPUT.MAX_SIZE_TEST						)
     UpperCAmelCase	:							Tuple						=     cfg.INPUT.FORMAT
     UpperCAmelCase	:							str						=     cfg.SIZE_DIVISIBILITY
     UpperCAmelCase	:							List[Any]						=     cfg.PAD_VALUE
     UpperCAmelCase	:							Dict						=     cfg.INPUT.MAX_SIZE_TEST
     UpperCAmelCase	:							str						=     cfg.MODEL.DEVICE
     UpperCAmelCase	:							int						=     torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),		1,		1						)
     UpperCAmelCase	:							List[Any]						=     torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),		1,		1						)
     UpperCAmelCase	:							Union[str, Any]						=     lambda __A						: (x - self.pixel_mean) / self.pixel_std



   def 	__magic_name__   (							self							:					Dict,		__A							:					Optional[Any]						):
     UpperCAmelCase	:							Optional[int]						=     tuple(max(__a						) for s in zip(*[img.shape for img in images]						)						)
     UpperCAmelCase	:							List[Any]						=     [im.shape[-2:] for im in images]
     UpperCAmelCase	:							Any						=     [
         nn.functional.pad(
             __a,		[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],		value=self.pad_value,		)
         for size, im in zip(__a,		__a						)
     ]

     return torch.stack(__a						), torch.tensor(__a						)





   def __call__(							self							:					Any,		__A							:					Any,		__A							:					Tuple=False						):
     with torch.no_grad():
       if not isinstance(__a,		__a						):
         UpperCAmelCase	:							Optional[Any]						=     [images]
       if single_image:
         assert len(__a						) == 1
       for i in range(len(__a						)						):
         if isinstance(images[i],		torch.Tensor						):
           images.insert(__a,		images.pop(__a						).to(self.device						).float()						)
         elif not isinstance(images[i],		torch.Tensor						):
           images.insert(
               __a,		torch.as_tensor(img_tensorize(images.pop(__a						),		input_format=self.input_format						)						)
               .to(self.device						)
               .float(),		)
            # resize smallest edge
       UpperCAmelCase	:							Union[str, Any]						=     torch.tensor([im.shape[:2] for im in images]						)
       UpperCAmelCase	:							int						=     self.aug(__a						)
       # transpose images and convert to torch tensors
       # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
       # now normalize before pad to avoid useless arithmetic
       UpperCAmelCase	:							Optional[int]						=     [self.normalizer(__a						) for x in images]
       # now pad them to do the following operations
       UpperCAmelCase			,						UpperCAmelCase	:							Dict						=     self.pad(__a						)
       # Normalize

       if self.size_divisibility > 0:
         raise NotImplementedError()
       # pad
       UpperCAmelCase	:							str						=     torch.true_divide(__a,		__a						)
       if single_image:
         return images[0], sizes[0], scales_yx[0]
       else:
         return images, sizes, scales_yx






def       a__					(		UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      Optional[int]      )  ->						Any:
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes






def       a__					(		UpperCAmelCase							:      Optional[int]    ,			UpperCAmelCase							:      List[Any]      )  ->						Union[str, Any]:
  assert torch.isfinite(__snake_case      ).all(), "Box tensor contains infinite or NaN!"
  UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=     box_size
  tensor[:, 0].clamp_(min=0    ,			max=__snake_case      )
  tensor[:, 1].clamp_(min=0    ,			max=__snake_case      )
  tensor[:, 2].clamp_(min=0    ,			max=__snake_case      )
  tensor[:, 3].clamp_(min=0    ,			max=__snake_case      )








import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


_lowerCamelCase    :       int        =							datasets.logging.get_logger(__name__)


_lowerCamelCase    :       Any        =							"\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n    author = \"Moosavi, Nafise Sadat  and\n      Strube, Michael\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1060\",\n    doi = \"10.18653/v1/P16-1060\",\n    pages = \"632--642\",\n}\n\n"

_lowerCamelCase    :       Tuple        =							"\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"

_lowerCamelCase    :       List[str]        =							"\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting 'keep_singletons=False', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    'mentions': mentions\n    'muc': MUC metric [Vilain et al, 1995]\n    'bcub': B-cubed [Bagga and Baldwin, 1998]\n    'ceafe': CEAFe [Luo et al., 2005]\n    'lea': LEA [Moosavi and Strube, 2016]\n    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric('coval')\n    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',\n    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',\n    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',\n    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',\n    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',\n    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"

def       a__					(		UpperCAmelCase							:      int    ,			UpperCAmelCase							:      str    ,			UpperCAmelCase							:      Union[str, Any]=False    ,			UpperCAmelCase							:      Optional[Any]=False    ,			UpperCAmelCase							:      Optional[Any]=True    ,			UpperCAmelCase							:      List[str]=False    ,			UpperCAmelCase							:      List[str]="dummy_doc"      )  ->						Any:
  UpperCAmelCase	:							Dict						=			{doc: key_lines}
  UpperCAmelCase	:							Any						=			{doc: sys_lines}

  UpperCAmelCase	:							int						=			{}

  UpperCAmelCase	:							List[Any]						=			0
  UpperCAmelCase	:							Any						=			0
  UpperCAmelCase	:							Optional[int]						=			0
  UpperCAmelCase	:							Optional[Any]						=			0
  UpperCAmelCase	:							int						=			0
  UpperCAmelCase	:							List[str]						=			0

  UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			reader.get_doc_mentions(UpperCAmelCase    ,			key_doc_lines[doc]    ,			UpperCAmelCase      )
  key_singletons_num += singletons_num

  if NP_only or min_span:
    UpperCAmelCase	:							int						=			reader.set_annotated_parse_trees(UpperCAmelCase    ,			key_doc_lines[doc]    ,			UpperCAmelCase    ,			UpperCAmelCase      )

  UpperCAmelCase			,						UpperCAmelCase	:							str						=			reader.get_doc_mentions(UpperCAmelCase    ,			sys_doc_lines[doc]    ,			UpperCAmelCase      )
  sys_singletons_num += singletons_num

  if NP_only or min_span:
    UpperCAmelCase	:							Tuple						=			reader.set_annotated_parse_trees(UpperCAmelCase    ,			key_doc_lines[doc]    ,			UpperCAmelCase    ,			UpperCAmelCase      )

  if remove_nested:
    UpperCAmelCase			,						UpperCAmelCase	:							Tuple						=			reader.remove_nested_coref_mentions(UpperCAmelCase    ,			UpperCAmelCase      )
    key_nested_coref_num += nested_mentions
    key_removed_nested_clusters += removed_clusters

    UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			reader.remove_nested_coref_mentions(UpperCAmelCase    ,			UpperCAmelCase      )
    sys_nested_coref_num += nested_mentions
    sys_removed_nested_clusters += removed_clusters

  UpperCAmelCase	:							Union[str, Any]						=			reader.get_mention_assignments(UpperCAmelCase    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Optional[int]						=			reader.get_mention_assignments(UpperCAmelCase    ,			UpperCAmelCase      )

  UpperCAmelCase	:							str						=			(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

  if remove_nested:
    logger.info(
        '''Number of removed nested coreferring mentions in the key '''
        f'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}'''      )
    logger.info(
        '''Number of resulting singleton clusters in the key '''
        f'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}'''      )

  if not keep_singletons:
    logger.info(
        f'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
        '''files, respectively'''      )

  return doc_coref_infos

def       a__					(		UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      Optional[Any]    ,			UpperCAmelCase							:      Dict    ,			UpperCAmelCase							:      List[str]    ,			UpperCAmelCase							:      Tuple    ,			UpperCAmelCase							:      int    ,			UpperCAmelCase							:      Optional[Any]      )  ->						Union[str, Any]:
  UpperCAmelCase	:							Optional[int]						=			get_coref_infos(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase      )

  UpperCAmelCase	:							Dict						=			{}
  UpperCAmelCase	:							Dict						=			0
  UpperCAmelCase	:							Tuple						=			0

  for name, metric in metrics:
    UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			evaluator.evaluate_documents(UpperCAmelCase    ,			UpperCAmelCase    ,			beta=1      )
    if name in ["muc", "bcub", "ceafe"]:
      conll += fa
      conll_subparts_num += 1
    output_scores.update({f'''{name}/recall''': recall, f'''{name}/precision''': precision, f'''{name}/f1''': fa}      )

    logger.info(
        name.ljust(10      )    ,			f'''Recall: {recall * 100:.2f}'''    ,			f''' Precision: {precision * 100:.2f}'''    ,			f''' F1: {fa * 100:.2f}'''    ,			)

  if conll_subparts_num == 3:
    UpperCAmelCase	:							Optional[int]						=			(conll / 3) * 100
    logger.info(f'''CoNLL score: {conll:.2f}'''      )
    output_scores.update({'''conll_score''': conll}      )

  return output_scores

def       a__					(		UpperCAmelCase							:      Optional[int]      )  ->						List[str]:
  UpperCAmelCase	:							int						=			False
  for line in key_lines:
    if not line.startswith('''#'''      ):
      if len(line.split()      ) > 6:
        UpperCAmelCase	:							List[str]						=			line.split()[5]
        if not parse_col == "-":
          UpperCAmelCase	:							Optional[int]						=			True
          break
        else:
          break
  return has_gold_parse






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



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     return datasets.MetricInfo(
         description=_DESCRIPTION,		citation=_CITATION,		inputs_description=_KWARGS_DESCRIPTION,		features=datasets.Features(
             {
                 '''predictions''': datasets.Sequence(datasets.Value('''string'''						)						),
                 '''references''': datasets.Sequence(datasets.Value('''string'''						)						),
             }						),		codebase_urls=['''https://github.com/ns-moosavi/coval'''],		reference_urls=[
             '''https://github.com/ns-moosavi/coval''',
             '''https://www.aclweb.org/anthology/P16-1060''',
             '''http://www.conll.cemantix.org/2012/data.html''',
         ],		)





   def 	__magic_name__   (							self							:					str,		__A							:					Any,		__A							:					Dict,		__A							:					List[str]=True,		__A							:					Optional[Any]=False,		__A							:					List[Any]=False,		__A							:					Tuple=False						):
     UpperCAmelCase	:							Optional[Any]						=			[
         ('''mentions''', evaluator.mentions),
         ('''muc''', evaluator.muc),
         ('''bcub''', evaluator.b_cubed),
         ('''ceafe''', evaluator.ceafe),
         ('''lea''', evaluator.lea),
     ]

     if min_span:
       UpperCAmelCase	:							List[Any]						=			util.check_gold_parse_annotation(__A						)
       if not has_gold_parse:
         raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.'''						)
         # util.parse_key_file(key_file)
         # key_file = key_file + ".parsed"

     UpperCAmelCase	:							Union[str, Any]						=			evaluate(
         key_lines=__A,		sys_lines=__A,		metrics=__A,		NP_only=__A,		remove_nested=__A,		keep_singletons=__A,		min_span=__A,		)

     return score





