





'''simple docstring'''


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowerCAmelCase_   :   Dict       =					logging.get_logger(__name__)

lowerCAmelCase_   :   Optional[int]       =					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_   :   List[str]       =					{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_   :   List[Any]       =					{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			Any								=						VOCAB_FILES_NAMES
	lowerCAmelCase__					:			Any								=						PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__					:			Dict								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__					:			Union[str, Any]								=						["input_ids", "attention_mask"]
	lowerCAmelCase__					:			str								=						GPTaTokenizer







	def __init__(				self			:   Optional[int] , lowerCamelCase			:   str=None , lowerCamelCase			:   str=None , lowerCamelCase			:   str=None , lowerCamelCase			:   str="<|endoftext|>" , lowerCamelCase			:   str="<|endoftext|>" , lowerCamelCase			:   Union[str, Any]="<|endoftext|>" , lowerCamelCase			:   Any=False , **lowerCamelCase			:   List[str] , ):




				'''simple docstring'''
				super().__init__(
				    lowerCamelCase , lowerCamelCase , tokenizer_file=lowerCamelCase , unk_token=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , add_prefix_space=lowerCamelCase , **lowerCamelCase , )

				a__     = kwargs.pop("add_bos_token" , lowerCamelCase		)

				a__     = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get("add_prefix_space" , lowerCamelCase		) != add_prefix_space:
							a__     = getattr(lowerCamelCase , pre_tok_state.pop("type"		)		)
							a__     = add_prefix_space
							a__     = pre_tok_class(**lowerCamelCase		)

				a__     = add_prefix_space







	def 						__a   (				self			:   Any , *lowerCamelCase			:   Optional[Any] , **lowerCamelCase			:   Any		):




				'''simple docstring'''
				a__     = kwargs.get("is_split_into_words" , lowerCamelCase		)
				assert self.add_prefix_space or not is_split_into_words, (
				    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
				    "to use it with pretokenized inputs."
				)

				return super()._batch_encode_plus(*lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   Dict , *lowerCamelCase			:   Any , **lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = kwargs.get("is_split_into_words" , lowerCamelCase		)

				assert self.add_prefix_space or not is_split_into_words, (
				    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
				    "to use it with pretokenized inputs."
				)

				return super()._encode_plus(*lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   List[Any] , lowerCamelCase			:   str , lowerCamelCase			:   Optional[str] = None		):




				'''simple docstring'''
				a__     = self._tokenizer.model.save(lowerCamelCase , name=lowerCamelCase		)
				return tuple(lowerCamelCase		)







	def 						__a   (				self			:   Dict , lowerCamelCase			:   "Conversation"		):




				'''simple docstring'''
				a__     = []
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowerCamelCase , add_special_tokens=lowerCamelCase		) + [self.eos_token_id]		)

				if len(lowerCamelCase		) > self.model_max_length:
							a__     = input_ids[-self.model_max_length :]
				return input_ids










'''simple docstring'''


import argparse
import json

from tqdm import tqdm







def    _lowerCamelCase  ()   ->    Any:
			a__     = argparse.ArgumentParser()

			# Required parameters
			parser.add_argument(
			    "--src_path"					,  type=__lowerCamelCase					,  default="biencoder-nq-dev.json"					,  help="Path to raw DPR training data"					,  )
			parser.add_argument(
			    "--evaluation_set"					,  type=__lowerCamelCase					,  help="where to store parsed evaluation_set file"					,  )
			parser.add_argument(
			    "--gold_data_path"					,  type=__lowerCamelCase					,  help="where to store parsed gold_data_path file"					,  )
			a__     = parser.parse_args()

			with open(args.src_path					,  "r"     ) as src_file, open(args.evaluation_set					,  "w"     ) as eval_file, open(
			    args.gold_data_path					,  "w"     ) as gold_file:
						a__     = json.load(__lowerCamelCase     )
						for dpr_record in tqdm(__lowerCamelCase     ):
									a__     = dpr_record["question"]
									a__     = [context["title"] for context in dpr_record["positive_ctxs"]]
									eval_file.write(question + "\n"     )
									gold_file.write("\t".join(__lowerCamelCase     ) + "\n"     )


if __name__ == "__main__":
		main()



