



'''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	:		int				=   logging.get_logger(__name__)






class 					UpperCAmelCase__	:



					def __init__(    self			,				UpperCamelCase			,				UpperCamelCase     )     ->			Optional[Any]:
						__lowerCAmelCase												=  question_encoder
						__lowerCAmelCase												=  generator
						__lowerCAmelCase												=  self.question_encoder



					def        UpperCAmelCase_ (    self			,				UpperCamelCase     )     ->			Union[str, Any]:
						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__     )
						__lowerCAmelCase												=  os.path.join(UpperCAmelCase__			,				"question_encoder_tokenizer"     )
						__lowerCAmelCase												=  os.path.join(UpperCAmelCase__			,				"generator_tokenizer"     )
						self.question_encoder.save_pretrained(UpperCAmelCase__     )
						self.generator.save_pretrained(UpperCAmelCase__     )



					@classmethod
					def        UpperCAmelCase_ (    cls			,				UpperCamelCase			,				**UpperCamelCase     )     ->			Dict:
						from ..auto.tokenization_auto import AutoTokenizer

						__lowerCAmelCase												=  kwargs.pop("config"			,				UpperCAmelCase__     )

						if config is None:
							__lowerCAmelCase												=  RagConfig.from_pretrained(UpperCAmelCase__     )

						__lowerCAmelCase												=  AutoTokenizer.from_pretrained(
						    UpperCAmelCase__			,				config=config.question_encoder			,				subfolder="question_encoder_tokenizer"     )
						__lowerCAmelCase												=  AutoTokenizer.from_pretrained(
						    UpperCAmelCase__			,				config=config.generator			,				subfolder="generator_tokenizer"     )
						return cls(question_encoder=UpperCAmelCase__			,				generator=UpperCAmelCase__     )



					def __call__(    self			,				*UpperCamelCase			,				**UpperCamelCase     )     ->			int:
						return self.current_tokenizer(*UpperCAmelCase__			,				**UpperCAmelCase__     )



					def        UpperCAmelCase_ (    self			,				*UpperCamelCase			,				**UpperCamelCase     )     ->			List[Any]:
						return self.generator.batch_decode(*UpperCAmelCase__			,				**UpperCAmelCase__     )



					def        UpperCAmelCase_ (    self			,				*UpperCamelCase			,				**UpperCamelCase     )     ->			Dict:
						return self.generator.decode(*UpperCAmelCase__			,				**UpperCAmelCase__     )



					def        UpperCAmelCase_ (    self     )     ->			Optional[Any]:
						__lowerCAmelCase												=  self.question_encoder



					def        UpperCAmelCase_ (    self     )     ->			List[str]:
						__lowerCAmelCase												=  self.generator



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = None			,				UpperCamelCase = "longest"			,				UpperCamelCase = None			,				UpperCamelCase = True			,				**UpperCamelCase			,				)     ->			Any:
						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:
							__lowerCAmelCase												=  self.current_tokenizer.model_max_length
						__lowerCAmelCase												=  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:
							__lowerCAmelCase												=  self.current_tokenizer.model_max_length
						__lowerCAmelCase												=  self(
						    text_target=UpperCAmelCase__			,				add_special_tokens=UpperCAmelCase__			,				return_tensors=UpperCAmelCase__			,				padding=UpperCAmelCase__			,				max_length=UpperCAmelCase__			,				truncation=UpperCAmelCase__			,				**UpperCAmelCase__			,				)
						__lowerCAmelCase												=  labels['''input_ids''']
						return model_inputs




'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase	:		Optional[Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Optional[int]				=   {
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Optional[Any]        =							"""dpr"""



					def __init__(    self			,				UpperCamelCase=3_0522			,				UpperCamelCase=768			,				UpperCamelCase=12			,				UpperCamelCase=12			,				UpperCamelCase=3072			,				UpperCamelCase="gelu"			,				UpperCamelCase=0.1			,				UpperCamelCase=0.1			,				UpperCamelCase=512			,				UpperCamelCase=2			,				UpperCamelCase=0.02			,				UpperCamelCase=1E-12			,				UpperCamelCase=0			,				UpperCamelCase="absolute"			,				UpperCamelCase = 0			,				**UpperCamelCase			,				)     ->			Tuple:
						super().__init__(pad_token_id=UpperCamelCase			,				**UpperCamelCase     )

						__lowerCAmelCase												=  vocab_size
						__lowerCAmelCase												=  hidden_size
						__lowerCAmelCase												=  num_hidden_layers
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  hidden_act
						__lowerCAmelCase												=  intermediate_size
						__lowerCAmelCase												=  hidden_dropout_prob
						__lowerCAmelCase												=  attention_probs_dropout_prob
						__lowerCAmelCase												=  max_position_embeddings
						__lowerCAmelCase												=  type_vocab_size
						__lowerCAmelCase												=  initializer_range
						__lowerCAmelCase												=  layer_norm_eps
						__lowerCAmelCase												=  projection_dim
						__lowerCAmelCase												=  position_embedding_type