




import copy

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


__A						:		Dict          =						logging.get_logger(__name__)

__A						:		int          =						{}

class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= "fsmt_decoder"




					def __init__(   self:		Optional[Any],     _lowercase:		Optional[Any]=0,     _lowercase:		Dict=0):


									'''simple docstring'''


									super().__init__()
									__lowerCAmelCase					=  vocab_size
									__lowerCAmelCase					=  bos_token_id





class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= "fsmt"
					__UpperCamelCase     	= {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(   self:		str,     _lowercase:		Optional[Any]=["en", "de"],     _lowercase:		List[str]=42024,     _lowercase:		List[str]=42024,     _lowercase:		Optional[int]="relu",     _lowercase:		Union[str, Any]=1024,     _lowercase:		Dict=200,     _lowercase:		Dict=1024,     _lowercase:		List[str]=4096,     _lowercase:		str=12,     _lowercase:		Optional[Any]=16,     _lowercase:		List[str]=0.0,     _lowercase:		List[Any]=4096,     _lowercase:		Dict=12,     _lowercase:		Optional[int]=16,     _lowercase:		Optional[int]=0.0,     _lowercase:		Union[str, Any]=0.0,     _lowercase:		Dict=0.1,     _lowercase:		List[Any]=0.0,     _lowercase:		Optional[Any]=0.02,     _lowercase:		Optional[int]=2,     _lowercase:		int=True,     _lowercase:		List[str]=True,     _lowercase:		Any=False,     _lowercase:		Union[str, Any]=5,     _lowercase:		Tuple=1.0,     _lowercase:		List[str]=False,     _lowercase:		Tuple=True,     _lowercase:		Union[str, Any]=1,     _lowercase:		Union[str, Any]=0,     _lowercase:		Optional[int]=2,     _lowercase:		Dict=2,     **_lowercase:		Optional[Any],     ):


									'''simple docstring'''


									__lowerCAmelCase					=  langs
									__lowerCAmelCase					=  src_vocab_size
									__lowerCAmelCase					=  tgt_vocab_size
									__lowerCAmelCase					=  d_model  # encoder_embed_dim and decoder_embed_dim

									__lowerCAmelCase					=  encoder_ffn_dim
									__lowerCAmelCase					=  __lowerCAmelCase					=  encoder_layers
									__lowerCAmelCase					=  encoder_attention_heads
									__lowerCAmelCase					=  encoder_layerdrop
									__lowerCAmelCase					=  decoder_layerdrop
									__lowerCAmelCase					=  decoder_ffn_dim
									__lowerCAmelCase					=  decoder_layers
									__lowerCAmelCase					=  decoder_attention_heads
									__lowerCAmelCase					=  max_position_embeddings
									__lowerCAmelCase					=  init_std  # Normal(0, this parameter)
									__lowerCAmelCase					=  activation_function

									__lowerCAmelCase					=  DecoderConfig(vocab_size=_lowercase,     bos_token_id=_lowercase)
									if "decoder" in common_kwargs:
													del common_kwargs["decoder"]

									__lowerCAmelCase					=  scale_embedding  # scale factor will be sqrt(d_model) if True

									# 3 Types of Dropout
									__lowerCAmelCase					=  attention_dropout
									__lowerCAmelCase					=  activation_dropout
									__lowerCAmelCase					=  dropout

									__lowerCAmelCase					=  use_cache
									super().__init__(
									    pad_token_id=_lowercase,     bos_token_id=_lowercase,     eos_token_id=_lowercase,     decoder_start_token_id=_lowercase,     is_encoder_decoder=_lowercase,     tie_word_embeddings=_lowercase,     forced_eos_token_id=_lowercase,     max_length=_lowercase,     num_beams=_lowercase,     length_penalty=_lowercase,     early_stopping=_lowercase,     **_lowercase,     )







					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  copy.deepcopy(self.__dict__)
									__lowerCAmelCase					=  self.decoder.to_dict()
									__lowerCAmelCase					=  self.__class__.model_type
									return output










import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__UpperCAmelCase    	=  logging.getLogger(__name__)


class 						lowerCamelCase__			:


						def __init__(			self    :	Optional[Any]      ):
													a__:							Tuple									=False


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Any   ,							_a    :	Union[str, Any]   ,							_a    :	List[str]   ,							_a    :	Optional[int]      ):
													if not self.initialized:
																				a__:							Any									=RagRetriever(
																				    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)
																				a__:							str									=True


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.retriever.index.init_index()


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													# delete the old self.retriever object before assigning the new index
													del self.retriever
													a__:							Dict									=False







						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	List[Any]   ,							_a    :	Any      ):
													a__				,      a__:							List[str]									=self.retriever._main_retrieve(_a   ,							_a      )
													a__:							List[str]									=self.retriever.index.get_doc_dicts(_a      )
													return doc_ids, retrieved_doc_embeds, doc_dicts


class 						lowerCamelCase__			(		_a ):


						def __init__(			self    :	Optional[int]   ,							_a    :	List[Any]   ,							_a    :	Any   ,							_a    :	Optional[Any]   ,							_a    :	Union[str, Any]   ,							_a    :	str=None      ):
													if index is not None and index.is_initialized() and len(_a      ) > 0:
																				raise ValueError(
																				    "When using Ray for distributed fine-tuning, "
																				    "you'll need to provide the paths instead, "
																				    "as the dataset and the index are loaded "
																				    "separately. More info in examples/rag/use_own_knowledge_dataset.py "      )

													super().__init__(
													    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)

													a__:							Optional[Any]									=retrieval_workers
													a__:							str									=question_encoder_tokenizer
													a__:							Optional[int]									=generator_tokenizer
													if len(self.retrieval_workers      ) > 0:
																				ray.get(
																				    [
																				        worker.create_rag_retriever.remote(_a   ,							_a   ,							_a   ,							_a      )
																				        for worker in self.retrieval_workers
																				    ]      )


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													logger.info("initializing retrieval"      )

													if len(self.retrieval_workers      ) > 0:
																				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
													else:
																				# Non-distributed training. Load index into this same process.
																				self.index.init_index()


						def 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	Union[str, Any]   ,							_a    :	Union[str, Any]      ):
													if len(self.retrieval_workers      ) > 0:
																				# Select a random retrieval actor.
																				a__:							Tuple									=self.retrieval_workers[random.randint(0   ,							len(self.retrieval_workers      ) - 1      )]
																				a__				,      a__				,      a__:							Union[str, Any]									=ray.get(
																				    random_worker.retrieve.remote(_a   ,							_a      )      )
													else:
																				a__				,      a__:							List[Any]									=self._main_retrieve(_a   ,							_a      )
																				a__:							List[str]									=self.index.get_doc_dicts(_a      )
													return retrieved_doc_embeds, doc_ids, doc_dicts


						@classmethod
						def 	_lowerCamelCase      (			cls    :	str   ,							_a    :	Union[str, Any]   ,							_a    :	Any=None   ,							**_a    :	Dict      ):
													return super(_a   ,							cls      ).get_tokenizers(_a   ,							_a   ,							**_a      )


						@classmethod
						def 	_lowerCamelCase      (			cls    :	Dict   ,							_a    :	Optional[int]   ,							_a    :	Tuple   ,							_a    :	int=None   ,							**_a    :	List[str]      ):
													a__:							Union[str, Any]									=kwargs.pop("config"   ,							_a      ) or RagConfig.from_pretrained(_a   ,							**_a      )
													a__:							List[Any]									=RagTokenizer.from_pretrained(_a   ,							config=_a      )
													a__:							str									=rag_tokenizer.question_encoder
													a__:							Any									=rag_tokenizer.generator

													if indexed_dataset is not None:
																				a__:							Dict									="custom"
																				a__:							Dict									=CustomHFIndex(config.retrieval_vector_size   ,							_a      )
													else:
																				a__:							List[str]									=cls._build_index(_a      )

													return cls(
													    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							retrieval_workers=_a   ,							index=_a   ,							)







						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													logger.info("re-loading the new dataset with embeddings"      )
													# access from the training loop

													ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )

													# build the index object again
													a__:							str									=self._build_index(self.config      )

													ray.get(
													    [
													        worker.create_rag_retriever.remote(
													            self.config   ,							self.question_encoder_tokenizer   ,							self.generator_tokenizer   ,							_a      )
													        for worker in self.retrieval_workers
													    ]      )





