



import os
import warnings
from typing import List, Optional

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


_lowercase     							=						logging.get_logger(__name__)
class 				__A					:





			def __init__(self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		question_encoder
						lowerCamelCase__ :      Dict								=		generator
						lowerCamelCase__ :      Any								=		self.question_encoder





			def        _snake_case    (self							,    __magic_name__				):
						if os.path.isfile(__magic_name__				):
									raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"				)
						os.makedirs(__magic_name__							,    exist_ok=__magic_name__				)
						lowerCamelCase__ :      Any								=		os.path.join(__magic_name__							,    """question_encoder_tokenizer"""				)
						lowerCamelCase__ :      List[Any]								=		os.path.join(__magic_name__							,    """generator_tokenizer"""				)
						self.question_encoder.save_pretrained(__magic_name__				)
						self.generator.save_pretrained(__magic_name__				)





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

						lowerCamelCase__ :      Optional[int]								=		kwargs.pop("""config"""							,    __magic_name__				)

						if config is None:
									lowerCamelCase__ :      str								=		RagConfig.from_pretrained(__magic_name__				)

						lowerCamelCase__ :      Optional[int]								=		AutoTokenizer.from_pretrained(
						    __magic_name__							,    config=config.question_encoder							,    subfolder="""question_encoder_tokenizer"""				)
						lowerCamelCase__ :      Tuple								=		AutoTokenizer.from_pretrained(
						    __magic_name__							,    config=config.generator							,    subfolder="""generator_tokenizer"""				)
						return cls(question_encoder=__magic_name__							,    generator=__magic_name__				)





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





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





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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		self.question_encoder





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.generator





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = "longest"							,    __magic_name__ = None							,    __magic_name__ = True							,    **__magic_name__							,    ):
						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"""							,    __magic_name__							,    )
						if max_length is None:
									lowerCamelCase__ :      Union[str, Any]								=		self.current_tokenizer.model_max_length
						lowerCamelCase__ :      Union[str, Any]								=		self(
						    __magic_name__							,    add_special_tokens=__magic_name__							,    return_tensors=__magic_name__							,    max_length=__magic_name__							,    padding=__magic_name__							,    truncation=__magic_name__							,    **__magic_name__							,    )
						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__ :      List[Any]								=		self(
						    text_target=__magic_name__							,    add_special_tokens=__magic_name__							,    return_tensors=__magic_name__							,    padding=__magic_name__							,    max_length=__magic_name__							,    truncation=__magic_name__							,    **__magic_name__							,    )
						lowerCamelCase__ :      List[Any]								=		labels["""input_ids"""]
						return model_inputs








import itertools
import string
from collections.abc import Generator, Iterable






def      _A				(UpperCamelCase		:      Iterable[str]      ,			UpperCamelCase		:      int )  ->Generator[tuple[str, ...], None, None]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		iter(UpperCamelCase )
			while True:
						lowerCamelCase__ :      Union[str, Any]								=		tuple(itertools.islice(UpperCamelCase      ,			UpperCamelCase ) )
						if not chunk:
									return
						yield chunk






def      _A				(UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		"""""".join([c.upper() for c in dirty if c in string.ascii_letters] )
			lowerCamelCase__ :      Any								=		""""""

			if len(UpperCamelCase ) < 2:
						return dirty

			for i in range(len(UpperCamelCase ) - 1 ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(UpperCamelCase ) & 1:
						clean += "X"

			return clean






def      _A				(UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCamelCase__ :      List[Any]								=		[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(UpperCamelCase )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(UpperCamelCase )

			return table






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      int								=		prepare_input(UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      Any								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      int								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



