




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_            =			logging.get_logger(__name__)






class 			_lowerCAmelCase  :

      def __init__( self   ,					__UpperCAmelCase   ,					__UpperCAmelCase		):
        lowerCAmelCase__					:	Any									=  question_encoder
        lowerCAmelCase__					:	Dict									=  generator
        lowerCAmelCase__					:	Optional[int]									=  self.question_encoder

      def 						__magic_name__( self   ,					__UpperCAmelCase		):
        if os.path.isfile(__UpperCAmelCase		):
          raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
        os.makedirs(__UpperCAmelCase   ,					exist_ok=__UpperCAmelCase		)
        lowerCAmelCase__					:	Dict									=  os.path.join(__UpperCAmelCase   ,					'''question_encoder_tokenizer'''		)
        lowerCAmelCase__					:	Dict									=  os.path.join(__UpperCAmelCase   ,					'''generator_tokenizer'''		)
        self.question_encoder.save_pretrained(__UpperCAmelCase		)
        self.generator.save_pretrained(__UpperCAmelCase		)

      @classmethod
      def 						__magic_name__( cls   ,					__UpperCAmelCase   ,					**__UpperCAmelCase		):
        # dynamically import AutoTokenizer
        from ..auto.tokenization_auto import AutoTokenizer

        lowerCAmelCase__					:	Optional[Any]									=  kwargs.pop('''config'''   ,					__UpperCAmelCase		)

        if config is None:
          lowerCAmelCase__					:	List[str]									=  RagConfig.from_pretrained(__UpperCAmelCase		)

        lowerCAmelCase__					:	int									=  AutoTokenizer.from_pretrained(
            __UpperCAmelCase   ,					config=config.question_encoder   ,					subfolder='''question_encoder_tokenizer'''		)
        lowerCAmelCase__					:	Union[str, Any]									=  AutoTokenizer.from_pretrained(
            __UpperCAmelCase   ,					config=config.generator   ,					subfolder='''generator_tokenizer'''		)
        return cls(question_encoder=__UpperCAmelCase   ,					generator=__UpperCAmelCase		)

      def __call__( self   ,					*__UpperCAmelCase   ,					**__UpperCAmelCase		):
        return self.current_tokenizer(*__UpperCAmelCase   ,					**__UpperCAmelCase		)

      def 						__magic_name__( self   ,					*__UpperCAmelCase   ,					**__UpperCAmelCase		):
        return self.generator.batch_decode(*__UpperCAmelCase   ,					**__UpperCAmelCase		)

      def 						__magic_name__( self   ,					*__UpperCAmelCase   ,					**__UpperCAmelCase		):
        return self.generator.decode(*__UpperCAmelCase   ,					**__UpperCAmelCase		)

      def 						__magic_name__( self		):
        lowerCAmelCase__					:	Dict									=  self.question_encoder

      def 						__magic_name__( self		):
        lowerCAmelCase__					:	str									=  self.generator







      def 						__magic_name__( self   ,					__UpperCAmelCase   ,					__UpperCAmelCase = None   ,					__UpperCAmelCase = None   ,					__UpperCAmelCase = None   ,					__UpperCAmelCase = "longest"   ,					__UpperCAmelCase = None   ,					__UpperCAmelCase = True   ,					**__UpperCAmelCase   ,					):
        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'''   ,					__UpperCAmelCase   ,					)
        if max_length is None:
          lowerCAmelCase__					:	List[Any]									=  self.current_tokenizer.model_max_length
        lowerCAmelCase__					:	Union[str, Any]									=  self(
            __UpperCAmelCase   ,					add_special_tokens=__UpperCAmelCase   ,					return_tensors=__UpperCAmelCase   ,					max_length=__UpperCAmelCase   ,					padding=__UpperCAmelCase   ,					truncation=__UpperCAmelCase   ,					**__UpperCAmelCase   ,					)
        if tgt_texts is None:
          return model_inputs
        # Process tgt_texts
        if max_target_length is None:
          lowerCAmelCase__					:	List[str]									=  self.current_tokenizer.model_max_length
        lowerCAmelCase__					:	int									=  self(
            text_target=__UpperCAmelCase   ,					add_special_tokens=__UpperCAmelCase   ,					return_tensors=__UpperCAmelCase   ,					padding=__UpperCAmelCase   ,					max_length=__UpperCAmelCase   ,					truncation=__UpperCAmelCase   ,					**__UpperCAmelCase   ,					)
        lowerCAmelCase__					:	Dict									=  labels['''input_ids''']
        return model_inputs









lowercase    				=   {
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase    				=   {value: key for key, value in MORSE_CODE_DICT.items()}





def 		__UpperCAmelCase     (	a_):
							return " ".join(MORSE_CODE_DICT[char] for char in message.upper())





def 		__UpperCAmelCase     (	a_):
							return "".join(REVERSE_DICT[char] for char in message.split())





def 		__UpperCAmelCase     (	):
							snake_case_           =				'Morse code here!'
							print(a_)
							snake_case_           =				encrypt(a_)
							print(a_)
							snake_case_           =				decrypt(a_)
							print(a_)


if __name__ == "__main__":
	main()





