
import os
import warnings
from typing import List, Optional

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


_UpperCAmelCase :			Dict              =	logging.get_logger(__name__)


class     lowercase :



		def __init__(			self    ,				A_    ,				A_	)  -> List[str]:
			"""simple docstring"""
			UpperCamelCase          =					question_encoder
			UpperCamelCase          =					generator
			UpperCamelCase          =					self.question_encoder



		def __UpperCamelCase			(			self    ,				A_	)  -> List[Any]:
			"""simple docstring"""
			if os.path.isfile(A_	):
				raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''	)
			os.makedirs(A_    ,				exist_ok=A_	)
			UpperCamelCase          =					os.path.join(A_    ,				'question_encoder_tokenizer'	)
			UpperCamelCase          =					os.path.join(A_    ,				'generator_tokenizer'	)
			self.question_encoder.save_pretrained(A_	)
			self.generator.save_pretrained(A_	)



		@classmethod
		def __UpperCamelCase			(			cls    ,				A_    ,				**A_	)  -> Any:
			"""simple docstring"""
			from ..auto.tokenization_auto import AutoTokenizer

			UpperCamelCase          =					kwargs.pop('config'    ,				A_	)

			if config is None:
				UpperCamelCase          =					RagConfig.from_pretrained(A_	)

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



		def __call__(			self    ,				*A_    ,				**A_	)  -> Optional[int]:
			"""simple docstring"""
			return self.current_tokenizer(*A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				*A_    ,				**A_	)  -> Tuple:
			"""simple docstring"""
			return self.generator.batch_decode(*A_    ,				**A_	)



		def __UpperCamelCase			(			self    ,				*A_    ,				**A_	)  -> Optional[int]:
			"""simple docstring"""
			return self.generator.decode(*A_    ,				**A_	)



		def __UpperCamelCase			(			self	)  -> Tuple:
			"""simple docstring"""
			UpperCamelCase          =					self.question_encoder



		def __UpperCamelCase			(			self	)  -> Tuple:
			"""simple docstring"""
			UpperCamelCase          =					self.generator



		def __UpperCamelCase			(			self    ,				A_    ,				A_ = None    ,				A_ = None    ,				A_ = None    ,				A_ = "longest"    ,				A_ = None    ,				A_ = True    ,				**A_    ,				)  -> BatchEncoding:
			"""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'    ,				A_    ,				)
			if max_length is None:
				UpperCamelCase          =					self.current_tokenizer.model_max_length
			UpperCamelCase          =					self(
			    A_    ,				add_special_tokens=A_    ,				return_tensors=A_    ,				max_length=A_    ,				padding=A_    ,				truncation=A_    ,				**A_    ,				)
			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=A_    ,				add_special_tokens=A_    ,				return_tensors=A_    ,				padding=A_    ,				max_length=A_    ,				truncation=A_    ,				**A_    ,				)
			UpperCamelCase          =					labels['input_ids']
			return model_inputs


import pprint

import requests

_UpperCAmelCase :			Union[str, Any]              =	"https://zenquotes.io/api"


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/today'			).json()


def 				A	( )       ->  list:
		'''simple docstring'''

		return requests.get(API_ENDPOINT_URL + '/random'			).json()


if __name__ == "__main__":
					_UpperCAmelCase :			str              =	random_quotes()
					pprint.pprint(response)
