"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_UpperCamelCase								=		[
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

_UpperCamelCase								=		{
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
_UpperCamelCase								=		{F'funnel-transformer/{name}': 5_12 for name in _model_names}
_UpperCamelCase								=		{F'funnel-transformer/{name}': {"do_lower_case": True} for name in _model_names}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						List[Any]					     =				VOCAB_FILES_NAMES
			_UpperCamelCase  :						List[str]					     =				PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase  :						List[str]					     =				PRETRAINED_INIT_CONFIGURATION
			_UpperCamelCase  :						List[str]					     =				FunnelTokenizer
			_UpperCamelCase  :						Optional[Any]					     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCamelCase  :						int					     =				2





			def __init__(			self   ,      snake_case_=None   ,      snake_case_=None   ,      snake_case_=True   ,      snake_case_="<unk>"   ,      snake_case_="<sep>"   ,      snake_case_="<pad>"   ,      snake_case_="<cls>"   ,      snake_case_="<mask>"   ,      snake_case_="<s>"   ,      snake_case_="</s>"   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=None   ,      snake_case_="##"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    snake_case_   ,      tokenizer_file=snake_case_   ,      do_lower_case=snake_case_   ,      unk_token=snake_case_   ,      sep_token=snake_case_   ,      pad_token=snake_case_   ,      cls_token=snake_case_   ,      mask_token=snake_case_   ,      bos_token=snake_case_   ,      eos_token=snake_case_   ,      clean_text=snake_case_   ,      tokenize_chinese_chars=snake_case_   ,      strip_accents=snake_case_   ,      wordpieces_prefix=snake_case_   ,      **snake_case_   ,      )

										A__       :     Union[str, Any]								=			json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
										if (
										    normalizer_state.get("""lowercase"""   ,      snake_case_							) != do_lower_case
										    or normalizer_state.get("""strip_accents"""   ,      snake_case_							) != strip_accents
										    or normalizer_state.get("""handle_chinese_chars"""   ,      snake_case_							) != tokenize_chinese_chars
										):
																	A__       :     Optional[Any]								=			getattr(snake_case_   ,      normalizer_state.pop("""type"""							)							)
																	A__       :     Optional[Any]								=			do_lower_case
																	A__       :     Union[str, Any]								=			strip_accents
																	A__       :     Any								=			tokenize_chinese_chars
																	A__       :     str								=			normalizer_class(**snake_case_							)

										A__       :     Optional[Any]								=			do_lower_case





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=None							):







										'''simple docstring'''


										A__       :     List[Any]								=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

										if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

										return output





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			[self.sep_token_id]
										A__       :     Union[str, Any]								=			[self.cls_token_id]
										if token_ids_a is None:
																	return len(cls							) * [self.cls_token_type_id] + len(token_ids_a + sep							) * [0]
										return len(cls							) * [self.cls_token_type_id] + len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     Optional[int]								=			self._tokenizer.model.save(snake_case_   ,      name=snake_case_							)
										return tuple(snake_case_							)






"""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()





