
"""simple docstring"""





import os
import warnings
from typing import List, Optional

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


__magic_name__			=     logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_					:




		"""simple docstring"""





		def __init__(	self				,    lowerCAmelCase__				,    lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE				=					question_encoder
				__SCREAMING_SNAKE_CASE				=					generator
				__SCREAMING_SNAKE_CASE				=					self.question_encoder




		def       snake_case_	(	self				,    lowerCAmelCase__):
				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       snake_case_	(	cls				,    lowerCAmelCase__				,    **lowerCAmelCase__):
				# 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				,    *lowerCAmelCase__				,    **lowerCAmelCase__):
				return self.current_tokenizer(*lowerCAmelCase_				,    **lowerCAmelCase_)




		def       snake_case_	(	self				,    *lowerCAmelCase__				,    **lowerCAmelCase__):
				return self.generator.batch_decode(*lowerCAmelCase_				,    **lowerCAmelCase_)




		def       snake_case_	(	self				,    *lowerCAmelCase__				,    **lowerCAmelCase__):
				return self.generator.decode(*lowerCAmelCase_				,    **lowerCAmelCase_)




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					self.question_encoder




		def       snake_case_	(	self):
				__SCREAMING_SNAKE_CASE				=					self.generator




		def       snake_case_	(	self				,    lowerCAmelCase__				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = "longest"				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = True				,    **lowerCAmelCase__				,    ):
				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












"""simple docstring"""
from __future__ import annotations

from collections.abc import Generator





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		dict[int, int]       		=    {}
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    2
				while True:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    factor_map.pop(lowerCamelCase,						lowerCamelCase       )
								if factor:
												_SCREAMING_SNAKE_CASE   :		str       		=    factor + prime
												while x in factor_map:
																x += factor
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    factor
								else:
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    prime
												yield prime
								prime += 1





def 				lowercase__		(					lowerCamelCase = 1E10       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    sieve()

				_SCREAMING_SNAKE_CASE   :		Dict       		=    1
				while True:
								_SCREAMING_SNAKE_CASE   :		int       		=    next(lowerCamelCase       )
								if (2 * prime * n) > limit:
												return n
								# Ignore the next prime as the reminder will be 2.
								next(lowerCamelCase       )
								n += 2


if __name__ == "__main__":
			print(solution())

