






import os
import warnings
from typing import List, Optional

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


_a      	=  logging.get_logger(__name__)





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			question_encoder
							_UpperCAmelCase									=			generator
							_UpperCAmelCase									=			self.question_encoder

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							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    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'question_encoder_tokenizer'    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'generator_tokenizer'    )
							self.question_encoder.save_pretrained(UpperCAmelCase    )
							self.generator.save_pretrained(UpperCAmelCase    )

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							from ..auto.tokenization_auto import AutoTokenizer

							_UpperCAmelCase									=			kwargs.pop('config'       ,    UpperCAmelCase    )

							if config is None:
													_UpperCAmelCase									=			RagConfig.from_pretrained(UpperCAmelCase    )

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

	def __call__(							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.current_tokenizer(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.generator.batch_decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.generator.decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.question_encoder

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.generator

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = "longest"       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							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:
													_UpperCAmelCase									=			self.current_tokenizer.model_max_length
							_UpperCAmelCase									=			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:
													_UpperCAmelCase									=			self.current_tokenizer.model_max_length
							_UpperCAmelCase									=			self(
							    text_target=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
							_UpperCAmelCase									=			labels['input_ids']
							return model_inputs

from math import ceil
def       A	(    _lowercase = 1_001						):
						SCREAMING_SNAKE_CASE							:    Any							=							1

						for i in range(1		,  int(ceil(n / 2.0						)						)						):
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							2 * i + 1
												SCREAMING_SNAKE_CASE							:    int							=							2 * i
												SCREAMING_SNAKE_CASE							:    List[str]							=							total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															__UpperCamelCase		:       Dict									=     int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')





