





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


__UpperCAmelCase       :   Any				       =							datasets.logging.get_logger(__name__)


__UpperCAmelCase       :   Optional[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"

__UpperCAmelCase       :   Optional[Any]				       =							"\\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"

__UpperCAmelCase       :   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__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__=False					,					SCREAMING_SNAKE_CASE__=False					,					SCREAMING_SNAKE_CASE__=True					,					SCREAMING_SNAKE_CASE__=False					,					SCREAMING_SNAKE_CASE__="dummy_doc")   ->    Dict:
  __snake_case:						Any   =						{doc: key_lines}
  __snake_case:						Optional[int]   =						{doc: sys_lines}

  __snake_case:						Any   =						{}

  __snake_case:						int   =						0
  __snake_case:						List[str]   =						0
  __snake_case:						Tuple   =						0
  __snake_case:						Union[str, Any]   =						0
  __snake_case:						Union[str, Any]   =						0
  __snake_case:						Optional[int]   =						0

  __snake_case  ,   __snake_case:						List[Any]   =						reader.get_doc_mentions(SCREAMING_SNAKE_CASE__					,					key_doc_lines[doc]					,					SCREAMING_SNAKE_CASE__)
  key_singletons_num += singletons_num

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

  __snake_case  ,   __snake_case:						Dict   =						reader.get_doc_mentions(SCREAMING_SNAKE_CASE__					,					sys_doc_lines[doc]					,					SCREAMING_SNAKE_CASE__)
  sys_singletons_num += singletons_num

  if NP_only or min_span:
    __snake_case:						List[Any]   =						reader.set_annotated_parse_trees(SCREAMING_SNAKE_CASE__					,					key_doc_lines[doc]					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

  if remove_nested:
    __snake_case  ,   __snake_case:						List[str]   =						reader.remove_nested_coref_mentions(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
    key_nested_coref_num += nested_mentions
    key_removed_nested_clusters += removed_clusters

    __snake_case  ,   __snake_case:						Optional[int]   =						reader.remove_nested_coref_mentions(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
    sys_nested_coref_num += nested_mentions
    sys_removed_nested_clusters += removed_clusters

  __snake_case:						Any   =						reader.get_mention_assignments(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)
  __snake_case:						str   =						reader.get_mention_assignments(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

  __snake_case:						List[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__							(    SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)   ->    Optional[Any]:
  __snake_case:						str   =						get_coref_infos(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

  __snake_case:						List[str]   =						{}
  __snake_case:						str   =						0
  __snake_case:						Optional[int]   =						0

  for name, metric in metrics:
    __snake_case  ,   __snake_case  ,   __snake_case:						Any   =						evaluator.evaluate_documents(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__					,					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:
    __snake_case:						Union[str, Any]   =						(conll / 3) * 100
    logger.info(F'''CoNLL score: {conll:.2f}''')
    output_scores.update({"""conll_score""": conll})

  return output_scores







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    str:
  __snake_case:						List[Any]   =						False
  for line in key_lines:
    if not line.startswith("""#"""):
      if len(line.split()) > 6:
        __snake_case:						Tuple   =						line.split()[5]
        if not parse_col == "-":
          __snake_case:						Union[str, Any]   =						True
          break
        else:
          break
  return has_gold_parse







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





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    List[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 							UpperCAmelCase__ (  self :    str							,				A :    Optional[Any]							,				A :    Tuple							,				A :    Union[str, Any]=True							,				A :    Dict=False							,				A :    Any=False							,				A :    Union[str, Any]=False		):
         __snake_case:						int   =						[
             ("""mentions""", evaluator.mentions),
             ("""muc""", evaluator.muc),
             ("""bcub""", evaluator.b_cubed),
             ("""ceafe""", evaluator.ceafe),
             ("""lea""", evaluator.lea),
         ]

         if min_span:
           __snake_case:						int   =						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"

         __snake_case:						int   =						evaluate(
             key_lines=A							,				sys_lines=A							,				metrics=A							,				NP_only=A							,				remove_nested=A							,				keep_singletons=A							,				min_span=A							,				)

         return score













from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__UpperCAmelCase       :   Any				       =							logging.get_logger(__name__)


__UpperCAmelCase       :   str				       =							Dict[str, Any]
__UpperCAmelCase       :   int				       =							List[Prediction]







@add_end_docstrings(__lowerCamelCase			)
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    int							,				*A :    Optional[int]							,				**A :    Optional[int]		):
         super().__init__(*A							,				**A		)

         if self.framework == "tf":
           raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''		)

         requires_backends(self							,				"""vision"""		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				**A :    Tuple		):
         __snake_case:						List[str]   =						{}
         if "threshold" in kwargs:
           __snake_case:						Optional[Any]   =						kwargs["""threshold"""]
         return {}, {}, postprocess_kwargs




       def __call__(  self :    int							,				*A :    Optional[Any]							,				**A :    Tuple		):
         return super().__call__(*A							,				**A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    str		):
         __snake_case:						Optional[Any]   =						load_image(A		)
         __snake_case:						Dict   =						torch.IntTensor([[image.height, image.width]]		)
         __snake_case:						str   =						self.image_processor(images=[image]							,				return_tensors="""pt"""		)
         if self.tokenizer is not None:
           __snake_case:						Optional[Any]   =						self.tokenizer(text=inputs["""words"""]							,				boxes=inputs["""boxes"""]							,				return_tensors="""pt"""		)
         __snake_case:						Any   =						target_size
         return inputs




       def 							UpperCAmelCase__ (  self :    Optional[int]							,				A :    Dict		):
         __snake_case:						int   =						model_inputs.pop("""target_size"""		)
         __snake_case:						int   =						self.model(**A		)
         __snake_case:						Any   =						outputs.__class__({"""target_size""": target_size, **outputs}		)
         if self.tokenizer is not None:
           __snake_case:						Optional[int]   =						model_inputs["""bbox"""]
         return model_outputs




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    Optional[int]							,				A :    Union[str, Any]=0.9		):
         __snake_case:						Optional[Any]   =						model_outputs["""target_size"""]
         if self.tokenizer is not None:
           # This is a LayoutLMForTokenClassification variant.
           # The OCR got the boxes and the model classified the words.
           __snake_case  ,   __snake_case:						Union[str, Any]   =						target_size[0].tolist()

           def unnormalize(A :    Tuple		):
             return self._get_bounding_box(
                 torch.Tensor(
                     [
                         (width * bbox[0] / 1_000),
                         (height * bbox[1] / 1_000),
                         (width * bbox[2] / 1_000),
                         (height * bbox[3] / 1_000),
                     ]		)		)

           __snake_case  ,   __snake_case:						Optional[int]   =						model_outputs["""logits"""].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
           __snake_case:						List[Any]   =						[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
           __snake_case:						int   =						[unnormalize(A		) for bbox in model_outputs["""bbox"""].squeeze(0		)]
           __snake_case:						int   =						["""score""", """label""", """box"""]
           __snake_case:						List[Any]   =						[dict(zip(A							,				A		)		) for vals in zip(scores.tolist()							,				A							,				A		) if vals[0] > threshold]
         else:
           # This is a regular ForObjectDetectionModel
           __snake_case:						Tuple   =						self.image_processor.post_process_object_detection(A							,				A							,				A		)
           __snake_case:						Optional[Any]   =						raw_annotations[0]
           __snake_case:						int   =						raw_annotation["""scores"""]
           __snake_case:						int   =						raw_annotation["""labels"""]
           __snake_case:						Optional[Any]   =						raw_annotation["""boxes"""]

           __snake_case:						Union[str, Any]   =						scores.tolist()
           __snake_case:						List[str]   =						[self.model.config.idalabel[label.item()] for label in labels]
           __snake_case:						List[str]   =						[self._get_bounding_box(A		) for box in boxes]

           # {"scores": [...], ...} --> [{"score":x, ...}, ...]
           __snake_case:						List[Any]   =						["""score""", """label""", """box"""]
           __snake_case:						Dict   =						[
               dict(zip(A							,				A		)		)
               for vals in zip(raw_annotation["""scores"""]							,				raw_annotation["""labels"""]							,				raw_annotation["""boxes"""]		)
           ]

         return annotation






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    "torch.Tensor"		):
         if self.framework != "pt":
           raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""		)
         __snake_case  ,   __snake_case  ,   __snake_case  ,   __snake_case:						Union[str, Any]   =						box.int().tolist()
         __snake_case:						Optional[Any]   =						{
             """xmin""": xmin,
             """ymin""": ymin,
             """xmax""": xmax,
             """ymax""": ymax,
         }
         return bbox






