


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


a_						:       List[Any]      			=				datasets.logging.get_logger(__name__)


a_						:       List[Any]      			=				"""\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
    author = \"Moosavi, Nafise Sadat  and
      Strube, Michael\",
    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
    month = aug,
    year = \"2016\",
    address = \"Berlin, Germany\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/P16-1060\",
    doi = \"10.18653/v1/P16-1060\",
    pages = \"632--642\",
}

"""

a_						:       Any      			=				"""\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	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.
5	Part-of-Speech
6	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.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""

a_						:       List[str]      			=				"""
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting 'keep_singletons=False', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    'mentions': mentions
    'muc': MUC metric [Vilain et al, 1995]
    'bcub': B-cubed [Bagga and Baldwin, 1998]
    'ceafe': CEAFe [Luo et al., 2005]
    'lea': LEA [Moosavi and Strube, 2016]
    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric('coval')
    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',
    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',
    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',
    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',
    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',
    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""





def __snake_case      (   UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		str      ,       UpperCAmelCase_		:		int=False      ,       UpperCAmelCase_		:		List[str]=False      ,       UpperCAmelCase_		:		int=True      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       UpperCAmelCase_		:		Union[str, Any]="dummy_doc"	):
 lowerCamelCase_  				=						{doc: key_lines}
 lowerCamelCase_  				=						{doc: sys_lines}

 lowerCamelCase_  				=						{}

 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0

 lowerCamelCase_				,lowerCamelCase_  				=						reader.get_doc_mentions(UpperCAmelCase_      ,       key_doc_lines[doc]      ,       UpperCAmelCase_	)
 key_singletons_num += singletons_num

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

 lowerCamelCase_				,lowerCamelCase_  				=						reader.get_doc_mentions(UpperCAmelCase_      ,       sys_doc_lines[doc]      ,       UpperCAmelCase_	)
 sys_singletons_num += singletons_num

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

 if remove_nested:
  lowerCamelCase_				,lowerCamelCase_  				=						reader.remove_nested_coref_mentions(UpperCAmelCase_      ,       UpperCAmelCase_	)
  key_nested_coref_num += nested_mentions
  key_removed_nested_clusters += removed_clusters

  lowerCamelCase_				,lowerCamelCase_  				=						reader.remove_nested_coref_mentions(UpperCAmelCase_      ,       UpperCAmelCase_	)
  sys_nested_coref_num += nested_mentions
  sys_removed_nested_clusters += removed_clusters

 lowerCamelCase_  				=						reader.get_mention_assignments(UpperCAmelCase_      ,       UpperCAmelCase_	)
 lowerCamelCase_  				=						reader.get_mention_assignments(UpperCAmelCase_      ,       UpperCAmelCase_	)

 lowerCamelCase_  				=						(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 __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		List[str]	):
 lowerCamelCase_  				=						get_coref_infos(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)

 lowerCamelCase_  				=						{}
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0

 for name, metric in metrics:
  lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						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:
  lowerCamelCase_  				=						(conll / 3) * 100
  logger.info(F'''CoNLL score: {conll:.2f}'''	)
  output_scores.update({"conll_score": conll}	)

 return output_scores





def __snake_case      (   UpperCAmelCase_		:		List[Any]	):
 lowerCamelCase_  				=						False
 for line in key_lines:
  if not line.startswith("#"	):
   if len(line.split()	) > 6:
    lowerCamelCase_  				=						line.split()[5]
    if not parse_col == "-":
     lowerCamelCase_  				=						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 						snake_case	(					self  ):







   """simple docstring"""
   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 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=True    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=False  ):







   """simple docstring"""
   lowerCamelCase_  				=						[
       ("mentions", evaluator.mentions),
       ("muc", evaluator.muc),
       ("bcub", evaluator.b_cubed),
       ("ceafe", evaluator.ceafe),
       ("lea", evaluator.lea),
   ]

   if min_span:
    lowerCamelCase_  				=						util.check_gold_parse_annotation(UpperCamelCase  )
    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"

   lowerCamelCase_  				=						evaluate(
       key_lines=UpperCamelCase    ,						sys_lines=UpperCamelCase    ,						metrics=UpperCamelCase    ,						NP_only=UpperCamelCase    ,						remove_nested=UpperCamelCase    ,						keep_singletons=UpperCamelCase    ,						min_span=UpperCamelCase    ,						)

   return score







'''simple docstring'''

# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


a_						:       Dict      			=				get_logger(__name__)





class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						"dummy_data"
  _lowerCamelCase						=						"datasets"
  _lowerCamelCase						=						False






  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = False    ,						UpperCamelCase = True    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						0
   lowerCamelCase_  				=						dataset_name
   lowerCamelCase_  				=						cache_dir
   lowerCamelCase_  				=						use_local_dummy_data
   lowerCamelCase_  				=						config
   # download_callbacks take a single url as input
   lowerCamelCase_  				=						download_callbacks or []
   # if False, it doesn't load existing files and it returns the paths of the dummy files relative
   # to the dummy_data zip file root
   lowerCamelCase_  				=						load_existing_dummy_data

   # TODO(PVP, QL) might need to make this more general
   lowerCamelCase_  				=						str(UpperCamelCase  )
   # to be downloaded
   lowerCamelCase_  				=						None
   lowerCamelCase_  				=						None






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self._dummy_file is None:
    lowerCamelCase_  				=						self.download_dummy_data()
   return self._dummy_file






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self.config is not None:
    # structure is dummy / config_name / version_name
    return os.path.join("dummy"    ,						self.config.name    ,						self.version_name  )
   # structure is dummy / version_name
   return os.path.join("dummy"    ,						self.version_name  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return os.path.join(self.dummy_data_folder    ,						"dummy_data.zip"  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						(
       self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
   )

   lowerCamelCase_  				=						cached_path(
       UpperCamelCase    ,						cache_dir=self.cache_dir    ,						extract_compressed_file=UpperCamelCase    ,						force_extract=UpperCamelCase  )

   return os.path.join(UpperCamelCase    ,						self.dummy_file_name  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return os.path.join(self.datasets_scripts_dir    ,						self.dataset_name    ,						self.dummy_zip_file  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self._bucket_url is None:
    lowerCamelCase_  				=						hf_github_url(self.dataset_name    ,						self.dummy_zip_file.replace(os.sep    ,						"/"  )  )
   return self._bucket_url






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   # return full path if its a dir
   if os.path.isdir(self.dummy_file  ):
    return self.dummy_file
   # else cut off path to file -> example `xsum`.
   return "/".join(self.dummy_file.replace(os.sep    ,						"/"  ).split("/"  )[:-1]  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   if self.load_existing_dummy_data:
    # dummy data is downloaded and tested
    lowerCamelCase_  				=						self.dummy_file
   else:
    # dummy data cannot be downloaded and only the path to dummy file is returned
    lowerCamelCase_  				=						self.dummy_file_name

   # special case when data_url is a dict
   if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
    return self.create_dummy_data_dict(UpperCamelCase    ,						UpperCamelCase  )
   elif isinstance(UpperCamelCase    ,						(list, tuple)  ):
    return self.create_dummy_data_list(UpperCamelCase    ,						UpperCamelCase  )
   else:
    return self.create_dummy_data_single(UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   return self.download_and_extract(UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return self.download_and_extract(UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return path






  def 						snake_case	(					self  ):







   """simple docstring"""
   return {}






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						{}
   for key, single_urls in data_url.items():
    for download_callback in self.download_callbacks:
     if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
      for single_url in single_urls:
       download_callback(UpperCamelCase  )
     else:
      lowerCamelCase_  				=						single_urls
      download_callback(UpperCamelCase  )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
    if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
     lowerCamelCase_  				=						[os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(Path(UpperCamelCase  ).name  )  ) for x in single_urls]
    else:
     lowerCamelCase_  				=						single_urls
     lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(Path(UpperCamelCase  ).name  )  )
    lowerCamelCase_  				=						value

   # make sure that values are unique
   if all(isinstance(UpperCamelCase    ,						UpperCamelCase  ) for i in dummy_data_dict.values()  ) and len(set(dummy_data_dict.values()  )  ) < len(
       dummy_data_dict.values()  ):
    # append key to value to make its name unique
    lowerCamelCase_  				=						{key: value + key for key, value in dummy_data_dict.items()}

   return dummy_data_dict






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
   lowerCamelCase_  				=						all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"    ,						UpperCamelCase  )  ) for url in data_url  )
   lowerCamelCase_  				=						all(
       url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"  ) for url in data_url  )
   if data_url and (is_tf_records or is_pubmed_records):
    lowerCamelCase_  				=						[data_url[0]] * len(UpperCamelCase  )
   for single_url in data_url:
    for download_callback in self.download_callbacks:
     download_callback(UpperCamelCase  )
    # we force the name of each key to be the last file / folder name of the url path
    # if the url has arguments, we need to encode them with urllib.parse.quote_plus
    lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(single_url.split("/"  )[-1]  )  )
    dummy_data_list.append(UpperCamelCase  )
   return dummy_data_list






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   for download_callback in self.download_callbacks:
    download_callback(UpperCamelCase  )
   # we force the name of each key to be the last file / folder name of the url path
   # if the url has arguments, we need to encode them with urllib.parse.quote_plus
   lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(data_url.split("/"  )[-1]  )  )
   if os.path.exists(UpperCamelCase  ) or not self.load_existing_dummy_data:
    return value
   else:
    # Backward compatibility, maybe deprecate at one point.
    # For many datasets with single url calls to dl_manager.download_and_extract,
    # the dummy_data.zip file is actually the zipped downloaded file
    # while now we expected the dummy_data.zip file to be a directory containing
    # the downloaded file.
    return path_to_dummy_data






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   def _iter_archive_members(UpperCamelCase  ):
    # this preserves the order of the members inside the ZIP archive
    lowerCamelCase_  				=						Path(self.dummy_file  ).parent
    lowerCamelCase_  				=						path.relative_to(UpperCamelCase  )
    with ZipFile(self.local_path_to_dummy_data  ) as zip_file:
     lowerCamelCase_  				=						zip_file.namelist()
    for member in members:
     if member.startswith(relative_path.as_posix()  ):
      yield dummy_parent_path.joinpath(UpperCamelCase  )

   lowerCamelCase_  				=						Path(UpperCamelCase  )
   lowerCamelCase_  				=						_iter_archive_members(UpperCamelCase  ) if self.use_local_dummy_data else path.rglob("*"  )
   for file_path in file_paths:
    if file_path.is_file() and not file_path.name.startswith((".", "__")  ):
     yield file_path.relative_to(UpperCamelCase  ).as_posix(), file_path.open("rb"  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   if not isinstance(UpperCamelCase    ,						UpperCamelCase  ):
    lowerCamelCase_  				=						[paths]
   for path in paths:
    if os.path.isfile(UpperCamelCase  ):
     if os.path.basename(UpperCamelCase  ).startswith((".", "__")  ):
      return
     yield path
    else:
     for dirpath, dirnames, filenames in os.walk(UpperCamelCase  ):
      if os.path.basename(UpperCamelCase  ).startswith((".", "__")  ):
       continue
      dirnames.sort()
      for filename in sorted(UpperCamelCase  ):
       if filename.startswith((".", "__")  ):
        continue
       yield os.path.join(UpperCamelCase    ,						UpperCamelCase  )



