




import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			question_encoder
       SCREAMING_SNAKE_CASE_							=			generator
       SCREAMING_SNAKE_CASE_							=			self.question_encoder



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		List[str]						):
       if os.path.isfile(_lowerCAmelCase						):
             raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"						)
       os.makedirs(_lowerCAmelCase				,				exist_ok=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			os.path.join(_lowerCAmelCase				,				'question_encoder_tokenizer'						)
       SCREAMING_SNAKE_CASE_							=			os.path.join(_lowerCAmelCase				,				'generator_tokenizer'						)
       self.question_encoder.save_pretrained(_lowerCAmelCase						)
       self.generator.save_pretrained(_lowerCAmelCase						)



 @classmethod
 def       lowerCAmelCase_  (							cls  :		Union[str, Any]				,				_lowerCAmelCase  :		Tuple				,				**_lowerCAmelCase  :		Optional[int]						):
       # dynamically import AutoTokenizer
       from ..auto.tokenization_auto import AutoTokenizer

       SCREAMING_SNAKE_CASE_							=			kwargs.pop('config'				,				_lowerCAmelCase						)

       if config is None:
             SCREAMING_SNAKE_CASE_							=			RagConfig.from_pretrained(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			AutoTokenizer.from_pretrained(
           _lowerCAmelCase				,				config=config.question_encoder				,				subfolder='question_encoder_tokenizer'						)
       SCREAMING_SNAKE_CASE_							=			AutoTokenizer.from_pretrained(
           _lowerCAmelCase				,				config=config.generator				,				subfolder='generator_tokenizer'						)
       return cls(question_encoder=_lowerCAmelCase				,				generator=_lowerCAmelCase						)



 def __call__(							self  :		int				,				*_lowerCAmelCase  :		List[Any]				,				**_lowerCAmelCase  :		Union[str, Any]						):
       return self.current_tokenizer(*_lowerCAmelCase				,				**_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Any				,				*_lowerCAmelCase  :		str				,				**_lowerCAmelCase  :		Dict						):
       return self.generator.batch_decode(*_lowerCAmelCase				,				**_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str				,				*_lowerCAmelCase  :		List[str]				,				**_lowerCAmelCase  :		Optional[int]						):
       return self.generator.decode(*_lowerCAmelCase				,				**_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.question_encoder



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.generator




 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Optional[List[str]] = None				,				_lowerCAmelCase  :		Optional[int] = None				,				_lowerCAmelCase  :		Optional[int] = None				,				_lowerCAmelCase  :		str = "longest"				,				_lowerCAmelCase  :		str = None				,				_lowerCAmelCase  :		bool = True				,				**_lowerCAmelCase  :		Tuple				,				):
       warnings.warn(
           '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
           'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
           'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
           'details'				,				_lowerCAmelCase				,				)
       if max_length is None:
             SCREAMING_SNAKE_CASE_							=			self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE_							=			self(
           _lowerCAmelCase				,				add_special_tokens=_lowerCAmelCase				,				return_tensors=_lowerCAmelCase				,				max_length=_lowerCAmelCase				,				padding=_lowerCAmelCase				,				truncation=_lowerCAmelCase				,				**_lowerCAmelCase				,				)
       if tgt_texts is None:
             return model_inputs
       # Process tgt_texts
       if max_target_length is None:
             SCREAMING_SNAKE_CASE_							=			self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE_							=			self(
           text_target=_lowerCAmelCase				,				add_special_tokens=_lowerCAmelCase				,				return_tensors=_lowerCAmelCase				,				padding=_lowerCAmelCase				,				max_length=_lowerCAmelCase				,				truncation=_lowerCAmelCase				,				**_lowerCAmelCase				,				)
       SCREAMING_SNAKE_CASE_							=			labels['input_ids']
       return model_inputs





import re

from filelock import FileLock


try:
   import nltk

   lowerCamelCase__ :   str					=							True
except (ImportError, ModuleNotFoundError):
   lowerCamelCase__ :   Union[str, Any]					=							False

if NLTK_AVAILABLE:
   with FileLock('.lock') as lock:
      nltk.download('punkt', quiet=True)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> str:
      re.sub('<n>'			,			''			,			__UpperCAmelCase							)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(__UpperCAmelCase							)							)