

'''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


lowerCAmelCase__								=		logging.get_logger(__name__)







class 	lowercase    :


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		int			=						question_encoder
			_UpperCamelCase   :		int			=						generator
			_UpperCamelCase   :		Optional[int]			=						self.question_encoder


		def 		A__					(				self	,       __snake_case):
			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   :		Optional[Any]			=						os.path.join(UpperCamelCase__	,       'question_encoder_tokenizer')
			_UpperCamelCase   :		List[Any]			=						os.path.join(UpperCamelCase__	,       'generator_tokenizer')
			self.question_encoder.save_pretrained(UpperCamelCase__)
			self.generator.save_pretrained(UpperCamelCase__)


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

			_UpperCamelCase   :		int			=						kwargs.pop('config'	,       UpperCamelCase__)

			if config is None:
				_UpperCamelCase   :		Any			=						RagConfig.from_pretrained(UpperCamelCase__)

			_UpperCamelCase   :		Optional[int]			=						AutoTokenizer.from_pretrained(
			    UpperCamelCase__	,       config=config.question_encoder	,       subfolder='question_encoder_tokenizer')
			_UpperCamelCase   :		List[Any]			=						AutoTokenizer.from_pretrained(
			    UpperCamelCase__	,       config=config.generator	,       subfolder='generator_tokenizer')
			return cls(question_encoder=UpperCamelCase__	,       generator=UpperCamelCase__)


		def __call__(				self	,       *__snake_case	,       **__snake_case):
			return self.current_tokenizer(*UpperCamelCase__	,       **UpperCamelCase__)


		def 		A__					(				self	,       *__snake_case	,       **__snake_case):
			return self.generator.batch_decode(*UpperCamelCase__	,       **UpperCamelCase__)


		def 		A__					(				self	,       *__snake_case	,       **__snake_case):
			return self.generator.decode(*UpperCamelCase__	,       **UpperCamelCase__)


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.question_encoder


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						self.generator






		def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = "longest"	,       __snake_case = None	,       __snake_case = True	,       **__snake_case	,       ):
			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   :		str			=						self.current_tokenizer.model_max_length
			_UpperCamelCase   :		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:
				_UpperCamelCase   :		List[Any]			=						self.current_tokenizer.model_max_length
			_UpperCamelCase   :		Dict			=						self(
			    text_target=UpperCamelCase__	,       add_special_tokens=UpperCamelCase__	,       return_tensors=UpperCamelCase__	,       padding=UpperCamelCase__	,       max_length=UpperCamelCase__	,       truncation=UpperCamelCase__	,       **UpperCamelCase__	,       )
			_UpperCamelCase   :		Any			=						labels['''input_ids''']
			return model_inputs







lowerCAmelCase__								=		range(2, 2_0 + 1)
lowerCAmelCase__								=		[1_0**k for k in range(ks[-1] + 1)]
lowerCAmelCase__								=		{}






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     int	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						sum(a_i[j] for j in range(UpperCAmelCase_							,       len(UpperCAmelCase_	)	)	)
	_UpperCamelCase   :		Optional[int]			=						sum(a_i[j] * base[j] for j in range(min(len(UpperCAmelCase_	)							,       UpperCAmelCase_	)	)	)

	_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						0, 0
	_UpperCamelCase   :		Optional[int]			=						n - i

	_UpperCamelCase   :		Union[str, Any]			=						memo.get(UpperCAmelCase_	)

	if sub_memo is not None:
		_UpperCamelCase   :		str			=						sub_memo.get(UpperCAmelCase_	)

		if jumps is not None and len(UpperCAmelCase_	) > 0:
			# find and make the largest jump without going over
			_UpperCamelCase   :		str			=						-1
			for _k in range(len(UpperCAmelCase_	) - 1							,       -1							,       -1	):
				if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
					_UpperCamelCase   :		Optional[Any]			=						_k
					break

			if max_jump >= 0:
				_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						jumps[max_jump]
				# since the difference between jumps is cached, add c
				_UpperCamelCase   :		Tuple			=						diff + c
				for j in range(min(UpperCAmelCase_							,       len(UpperCAmelCase_	)	)	):
					_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						divmod(UpperCAmelCase_							,       1_0	)
				if new_c > 0:
					add(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

		else:
			_UpperCamelCase   :		Union[str, Any]			=						[]
	else:
		_UpperCamelCase   :		List[Any]			=						{c: []}
		_UpperCamelCase   :		Optional[int]			=						sub_memo

	if dn >= max_dn or c + diff >= base[k]:
		return diff, dn

	if k > ks[0]:
		while True:
			# keep doing smaller jumps
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						next_term(UpperCAmelCase_							,       k - 1							,       i + dn							,       UpperCAmelCase_	)
			diff += _diff
			dn += terms_jumped

			if dn >= max_dn or c + diff >= base[k]:
				break
	else:
		# would be too small a jump, just compute sequential terms instead
		_UpperCamelCase	,		_UpperCamelCase   :		Any			=						compute(UpperCAmelCase_							,       UpperCAmelCase_							,       i + dn							,       UpperCAmelCase_	)
		diff += _diff
		dn += terms_jumped

	_UpperCamelCase   :		List[str]			=						sub_memo[c]

	# keep jumps sorted by # of terms skipped
	_UpperCamelCase   :		Union[str, Any]			=						0
	while j < len(UpperCAmelCase_	):
		if jumps[j][1] > dn:
			break
		j += 1

	# cache the jump for this value digitsum(b) and c
	sub_memo[c].insert(UpperCAmelCase_							,       (diff, dn, k)	)
	return (diff, dn)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     Any	)						->				Dict:




	'''simple docstring'''




	if i >= n:
		return 0, i
	if k > len(UpperCAmelCase_	):
		a_i.extend([0 for _ in range(k - len(UpperCAmelCase_	)	)]	)

	# note: a_i -> b * 10^k + c
	# ds_b -> digitsum(b)
	# ds_c -> digitsum(c)
	_UpperCamelCase   :		Any			=						i
	_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Any			=						0, 0, 0
	for j in range(len(UpperCAmelCase_	)	):
		if j >= k:
			ds_b += a_i[j]
		else:
			ds_c += a_i[j]

	while i < n:
		i += 1
		_UpperCamelCase   :		Union[str, Any]			=						ds_c + ds_b
		diff += addend
		_UpperCamelCase   :		Union[str, Any]			=						0
		for j in range(UpperCAmelCase_	):
			_UpperCamelCase   :		Union[str, Any]			=						a_i[j] + addend
			_UpperCamelCase	,		_UpperCamelCase   :		Any			=						divmod(UpperCAmelCase_							,       1_0	)

			ds_c += a_i[j]

		if addend > 0:
			break

	if addend > 0:
		add(UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)
	return diff, i - start_i






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Union[str, Any]							,       UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Any	)						->				Dict:




	'''simple docstring'''




	for j in range(UpperCAmelCase_							,       len(UpperCAmelCase_	)	):
		_UpperCamelCase   :		List[str]			=						digits[j] + addend
		if s >= 1_0:
			_UpperCamelCase	,		_UpperCamelCase   :		Any			=						divmod(UpperCAmelCase_							,       1_0	)
			_UpperCamelCase   :		Union[str, Any]			=						addend // 1_0 + quotient
		else:
			_UpperCamelCase   :		Dict			=						s
			_UpperCamelCase   :		Optional[Any]			=						addend // 1_0

		if addend == 0:
			break

	while addend > 0:
		_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						divmod(UpperCAmelCase_							,       1_0	)
		digits.append(UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int = 1_0**1_5	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						[1]
	_UpperCamelCase   :		Optional[int]			=						1
	_UpperCamelCase   :		int			=						0
	while True:
		_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						next_term(UpperCAmelCase_							,       2_0							,       i + dn							,       UpperCAmelCase_	)
		dn += terms_jumped
		if dn == n - i:
			break

	_UpperCamelCase   :		str			=						0
	for j in range(len(UpperCAmelCase_	)	):
		a_n += digits[j] * 1_0**j
	return a_n


if __name__ == "__main__":
							print(f'{solution() = }')




