

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


A_       : Any         =			logging.getLogger(__name__)



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       A__=-1			):
						# in NER datasets, the last column is usually reserved for NER label
						A__							:    Optional[int]        =		label_idx






					def   __A			( self    ,       A__    ,       A__			):
						if isinstance(A__    ,       A__			):
							A__							:    Optional[Any]        =		mode.value
						A__							:    str        =		os.path.join(A__    ,       F"""{mode}.txt"""			)
						A__							:    Optional[Any]        =		1
						A__							:    Optional[int]        =		[]
						with open(A__    ,       encoding="""utf-8"""			) as f:
							A__							:    Optional[int]        =		[]
							A__							:    str        =		[]
							for line in f:
								if line.startswith("""-DOCSTART-"""			) or line == "" or line == "\n":
									if words:
										examples.append(InputExample(guid=F"""{mode}-{guid_index}"""    ,       words=A__    ,       labels=A__			)			)
										guid_index += 1
										A__							:    str        =		[]
										A__							:    int        =		[]
								else:
									A__							:    Union[str, Any]        =		line.split(""" """			)
									words.append(splits[0]			)
									if len(A__			) > 1:
										labels.append(splits[self.label_idx].replace("""\n"""    ,       """"""			)			)
									else:
										# Examples could have no label for mode = "test"
										labels.append("""O"""			)
							if words:
								examples.append(InputExample(guid=F"""{mode}-{guid_index}"""    ,       words=A__    ,       labels=A__			)			)
						return examples






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						A__							:    Any        =		0
						for line in test_input_reader:
							if line.startswith("""-DOCSTART-"""			) or line == "" or line == "\n":
								writer.write(A__			)
								if not preds_list[example_id]:
									example_id += 1
							elif preds_list[example_id]:
								A__							:    Any        =		line.split()[0] + """ """ + preds_list[example_id].pop(0			) + """\n"""
								writer.write(A__			)
							else:
								logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""    ,       line.split()[0]			)






					def   __A			( self    ,       A__			):
						if path:
							with open(A__    ,       """r"""			) as f:
								A__							:    Dict        =		f.read().splitlines()
							if "O" not in labels:
								A__							:    Union[str, Any]        =		["""O"""] + labels
							return labels
						else:
							return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self			):
						# in CONLL2003 dataset chunk column is second-to-last
						super().__init__(label_idx=-2			)






					def   __A			( self    ,       A__			):
						if path:
							with open(A__    ,       """r"""			) as f:
								A__							:    Any        =		f.read().splitlines()
							if "O" not in labels:
								A__							:    Any        =		["""O"""] + labels
							return labels
						else:
							return [
							    "O",
							    "B-ADVP",
							    "B-INTJ",
							    "B-LST",
							    "B-PRT",
							    "B-NP",
							    "B-SBAR",
							    "B-VP",
							    "B-ADJP",
							    "B-CONJP",
							    "B-PP",
							    "I-ADVP",
							    "I-INTJ",
							    "I-LST",
							    "I-PRT",
							    "I-NP",
							    "I-SBAR",
							    "I-VP",
							    "I-ADJP",
							    "I-CONJP",
							    "I-PP",
							]



class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self    ,       A__    ,       A__			):
						if isinstance(A__    ,       A__			):
							A__							:    Union[str, Any]        =		mode.value
						A__							:    Dict        =		os.path.join(A__    ,       F"""{mode}.txt"""			)
						A__							:    int        =		1
						A__							:    List[Any]        =		[]

						with open(A__    ,       encoding="""utf-8"""			) as f:
							for sentence in parse_incr(A__			):
								A__							:    Tuple        =		[]
								A__							:    Union[str, Any]        =		[]
								for token in sentence:
									words.append(token["""form"""]			)
									labels.append(token["""upos"""]			)
								assert len(A__			) == len(A__			)
								if words:
									examples.append(InputExample(guid=F"""{mode}-{guid_index}"""    ,       words=A__    ,       labels=A__			)			)
									guid_index += 1
						return examples






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						A__							:    Optional[Any]        =		0
						for sentence in parse_incr(A__			):
							A__							:    str        =		preds_list[example_id]
							A__							:    Tuple        =		""""""
							for token in sentence:
								out += F"""{token['form']} ({token['upos']}|{s_p.pop(0			)}) """
							out += "\n"
							writer.write(A__			)
							example_id += 1






					def   __A			( self    ,       A__			):
						if path:
							with open(A__    ,       """r"""			) as f:
								return f.read().splitlines()
						else:
							return [
							    "ADJ",
							    "ADP",
							    "ADV",
							    "AUX",
							    "CCONJ",
							    "DET",
							    "INTJ",
							    "NOUN",
							    "NUM",
							    "PART",
							    "PRON",
							    "PROPN",
							    "PUNCT",
							    "SCONJ",
							    "SYM",
							    "VERB",
							    "X",
							]






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


A_       : Union[str, Any]         =			logging.get_logger(__name__)



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       **A__			):
						warnings.warn(
						    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
						    """ use DonutImageProcessor instead."""    ,       A__    ,       )
						super().__init__(*A__    ,       **A__			)



