


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

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


_snake_case	:						Dict         =							logging.getLogger(__name__)

class 							a						(__SCREAMING_SNAKE_CASE ):




							"""simple docstring"""


							def __init__(				self			:    List[Any]       ,      lowerCamelCase			:    Optional[Any]=-1							)      ->       Any:
														__snake_case						:     List[str]									=  label_idx

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    str							)      ->       Dict:







														if isinstance(_A       ,      _A							):
																					__snake_case						:     Tuple									=  mode.value
														__snake_case						:     int									=  os.path.join(_A       ,      F'{mode}.txt'							)
														__snake_case						:     List[Any]									=  1
														__snake_case						:     str									=  []
														with open(_A       ,      encoding="utf-8"							) as f:
																					__snake_case						:     Any									=  []
																					__snake_case						:     Dict									=  []
																					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
																																										__snake_case						:     int									=  []
																																										__snake_case						:     Union[str, Any]									=  []
																												else:
																																			__snake_case						:     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        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    List[Any]							)      ->       Optional[int]:
														__snake_case						:     List[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]:
																												__snake_case						:     str									=  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        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    List[Any]							)      ->       List[str]:







														if path:
																					with open(_A       ,      "r"							) as f:
																												__snake_case						:     Union[str, Any]									=  f.read().splitlines()
																					if "O" not in labels:
																												__snake_case						:     Dict									=  ["""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						(__SCREAMING_SNAKE_CASE ):




							"""simple docstring"""


							def __init__(				self			:    List[Any]							)      ->       Optional[int]:
														super().__init__(label_idx=-2							)






							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Any							)      ->       str:







														if path:
																					with open(_A       ,      "r"							) as f:
																												__snake_case						:     str									=  f.read().splitlines()
																					if "O" not in labels:
																												__snake_case						:     List[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						(__SCREAMING_SNAKE_CASE ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Union[str, Any]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    List[str]							)      ->       Dict:







														if isinstance(_A       ,      _A							):
																					__snake_case						:     Optional[Any]									=  mode.value
														__snake_case						:     Optional[Any]									=  os.path.join(_A       ,      F'{mode}.txt'							)
														__snake_case						:     Dict									=  1
														__snake_case						:     List[str]									=  []

														with open(_A       ,      encoding="utf-8"							) as f:
																					for sentence in parse_incr(_A							):
																												__snake_case						:     List[Any]									=  []
																												__snake_case						:     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        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    List[Any]							)      ->       List[str]:
														__snake_case						:     List[str]									=  0
														for sentence in parse_incr(_A							):
																					__snake_case						:     int									=  preds_list[example_id]
																					__snake_case						:     Tuple									=  """"""
																					for token in sentence:
																												out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0							)}) '
																					out += "\n"
																					writer.write(_A							)
																					example_id += 1






							def        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    List[str]							)      ->       int:







														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",
																					]








"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__magic_name__       :					Dict						=			logging.getLogger(__name__)






@dataclass
class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=0.0					,     metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to SortishSamler or not."""}	)
			__lowerCAmelCase     :				bool										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}	)
			__lowerCAmelCase     :				bool										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """whether to use adafactor"""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[float]										=  field(
			    default=__SCREAMING_SNAKE_CASE					,     metadata={"""help""": """Attention dropout probability. Goes into model.config."""}	)
			__lowerCAmelCase     :				Optional[str]										=  field(
			    default="""linear"""					,     metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()	)}'''}					,     )



