




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

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


UpperCamelCase								=	logging.getLogger(__name__)





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

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


    def   a (			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	Union[Split, str]  ) ->			List[InputExample]:
         if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ):
              lowerCAmelCase__            =					mode.value
         lowerCAmelCase__            =					os.path.join(SCREAMING_SNAKE_CASE__  ,      f'{mode}.txt'  )
         lowerCAmelCase__            =					1
         lowerCAmelCase__            =					[]
         with open(SCREAMING_SNAKE_CASE__  ,      encoding="utf-8"  ) as f:
              lowerCAmelCase__            =					[]
              lowerCAmelCase__            =					[]
              for line in f:
                   if line.startswith("-DOCSTART-"  ) or line == "" or line == "\n":
                        if words:
                             examples.append(InputExample(guid=f'{mode}-{guid_index}'  ,      words=SCREAMING_SNAKE_CASE__  ,      labels=SCREAMING_SNAKE_CASE__  )  )
                             guid_index += 1
                             lowerCAmelCase__            =					[]
                             lowerCAmelCase__            =					[]
                   else:
                        lowerCAmelCase__            =					line.split(" "  )
                        words.append(splits[0]  )
                        if len(SCREAMING_SNAKE_CASE__  ) > 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=SCREAMING_SNAKE_CASE__  ,      labels=SCREAMING_SNAKE_CASE__  )  )
         return examples


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


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			List[str]:
         if path:
              with open(SCREAMING_SNAKE_CASE__  ,      "r"  ) as f:
                   lowerCAmelCase__            =					f.read().splitlines()
              if "O" not in labels:
                   lowerCAmelCase__            =					["O"] + labels
              return labels
         else:
              return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

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


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			List[str]:
         if path:
              with open(SCREAMING_SNAKE_CASE__  ,      "r"  ) as f:
                   lowerCAmelCase__            =					f.read().splitlines()
              if "O" not in labels:
                   lowerCAmelCase__            =					["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 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Union[Split, str]  ) ->			List[InputExample]:
         if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ):
              lowerCAmelCase__            =					mode.value
         lowerCAmelCase__            =					os.path.join(SCREAMING_SNAKE_CASE__  ,      f'{mode}.txt'  )
         lowerCAmelCase__            =					1
         lowerCAmelCase__            =					[]

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


    def   a (			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	TextIO  ,      SCREAMING_SNAKE_CASE__	:	TextIO  ,      SCREAMING_SNAKE_CASE__	:	List  ) ->			Optional[Any]:
         lowerCAmelCase__            =					0
         for sentence in parse_incr(SCREAMING_SNAKE_CASE__  ):
              lowerCAmelCase__            =					preds_list[example_id]
              lowerCAmelCase__            =					""
              for token in sentence:
                   out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0  )}) '
              out += "\n"
              writer.write(SCREAMING_SNAKE_CASE__  )
              example_id += 1


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			List[str]:
         if path:
              with open(SCREAMING_SNAKE_CASE__  ,      "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 argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1024							):
							A__				,							A__       :     Any								=			[], []

							A__       :     List[Any]								=			list(zip(lowerCAmelCase			,  lowerCAmelCase							)							)
							A__				,							A__       :     Optional[int]								=			sorted_examples[0]

							def is_too_big(lowerCAmelCase							):
														return tok(lowerCAmelCase			,  return_tensors="""pt"""							).input_ids.shape[1] > max_tokens

							for src, tgt in tqdm(sorted_examples[1:]							):
														A__       :     List[Any]								=			new_src + """ """ + src
														A__       :     List[str]								=			new_tgt + """ """ + tgt
														if is_too_big(lowerCAmelCase							) or is_too_big(lowerCAmelCase							):  # cant fit, finalize example
																					finished_src.append(lowerCAmelCase							)
																					finished_tgt.append(lowerCAmelCase							)
																					A__				,							A__       :     str								=			src, tgt
														else:  # can fit, keep adding
																					A__				,							A__       :     Union[str, Any]								=			cand_src, cand_tgt

    # cleanup
							if new_src:
														assert new_tgt
														finished_src.append(lowerCAmelCase							)
														finished_tgt.append(lowerCAmelCase							)
							return finished_src, finished_tgt

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			Path(lowerCAmelCase							)
							save_path.mkdir(exist_ok=lowerCAmelCase							)
							for split in ["train"]:
														A__				,							A__       :     List[Any]								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														A__       :     Tuple								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__       :     Union[str, Any]								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__				,							A__       :     Tuple								=			pack_examples(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
														print(F'''packed {split} split from {len(lowerCAmelCase							)} examples -> {len(lowerCAmelCase							)}.'''							)
														Path(save_path / F'''{split}.source'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
														Path(save_path / F'''{split}.target'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
							for split in ["val", "test"]:
														A__				,							A__       :     Dict								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.source'''							)
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.target'''							)

def  _A(		):
							A__       :     str								=			argparse.ArgumentParser()
							parser.add_argument("""--tok_name"""			,  type=lowerCAmelCase			,  help="""like facebook/bart-large-cnn,t5-base, etc."""							)
							parser.add_argument("""--max_seq_len"""			,  type=lowerCAmelCase			,  default=128							)
							parser.add_argument("""--data_dir"""			,  type=lowerCAmelCase							)
							parser.add_argument("""--save_path"""			,  type=lowerCAmelCase							)
							A__       :     str								=			parser.parse_args()
							A__       :     Optional[Any]								=			AutoTokenizer.from_pretrained(args.tok_name							)
							return pack_data_dir(lowerCAmelCase			,  Path(args.data_dir							)			,  args.max_seq_len			,  args.save_path							)


if __name__ == "__main__":
				packer_cli()





