import logging
import random

import ray

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


lowerCAmelCase__  	=      logging.getLogger(__name__)
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ):
		_lowercase   =False


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if not self.initialized:
			_lowercase   =RagRetriever(
			    lowerCAmelCase_       ,  question_encoder_tokenizer=lowerCAmelCase_       ,  generator_tokenizer=lowerCAmelCase_       ,  index=lowerCAmelCase_       ,  init_retrieval=lowerCAmelCase_       ,  )
			_lowercase   =True


	def __lowerCAmelCase      (   self       ):
		self.retriever.index.init_index()


	def __lowerCAmelCase      (   self       ):
		# delete the old self.retriever object before assigning the new index
		del self.retriever
		_lowercase   =False


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   =self.retriever._main_retrieve(lowerCAmelCase_       ,  lowerCAmelCase_       )
		_lowercase   =self.retriever.index.get_doc_dicts(lowerCAmelCase_       )
		return doc_ids, retrieved_doc_embeds, doc_dicts

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ):
		if index is not None and index.is_initialized() and len(lowerCAmelCase_       ) > 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__(
		    lowerCAmelCase_       ,  question_encoder_tokenizer=lowerCAmelCase_       ,  generator_tokenizer=lowerCAmelCase_       ,  index=lowerCAmelCase_       ,  init_retrieval=lowerCAmelCase_       ,  )

		_lowercase   =retrieval_workers
		_lowercase   =question_encoder_tokenizer
		_lowercase   =generator_tokenizer
		if len(self.retrieval_workers       ) > 0:
			ray.get(
			    [
			        worker.create_rag_retriever.remote(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			        for worker in self.retrieval_workers
			    ]       )


	def __lowerCAmelCase      (   self       ):
		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       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if len(self.retrieval_workers       ) > 0:
			# Select a random retrieval actor.
			_lowercase   =self.retrieval_workers[random.randint(0       ,  len(self.retrieval_workers       ) - 1       )]
			_lowercase   ,  _lowercase   ,  _lowercase   =ray.get(
			    random_worker.retrieve.remote(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
		else:
			_lowercase   ,  _lowercase   =self._main_retrieve(lowerCAmelCase_       ,  lowerCAmelCase_       )
			_lowercase   =self.index.get_doc_dicts(lowerCAmelCase_       )
		return retrieved_doc_embeds, doc_ids, doc_dicts


	@classmethod
	def __lowerCAmelCase      (   cls       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ,  **lowerCAmelCase_       ):
		return super(lowerCAmelCase_       ,  cls       ).get_tokenizers(lowerCAmelCase_       ,  lowerCAmelCase_       ,  **lowerCAmelCase_       )


	@classmethod
	def __lowerCAmelCase      (   cls       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ,  **lowerCAmelCase_       ):
		_lowercase   =kwargs.pop("config"       ,  lowerCAmelCase_       ) or RagConfig.from_pretrained(lowerCAmelCase_       ,  **lowerCAmelCase_       )
		_lowercase   =RagTokenizer.from_pretrained(lowerCAmelCase_       ,  config=lowerCAmelCase_       )
		_lowercase   =rag_tokenizer.question_encoder
		_lowercase   =rag_tokenizer.generator

		if indexed_dataset is not None:
			_lowercase   ="custom"
			_lowercase   =CustomHFIndex(config.retrieval_vector_size       ,  lowerCAmelCase_       )
		else:
			_lowercase   =cls._build_index(lowerCAmelCase_       )

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


	def __lowerCAmelCase      (   self       ):
		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
		_lowercase   =self._build_index(self.config       )

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





import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

require_version("tokenizers>=0.13.3")

if is_sentencepiece_available():
						from .tokenization_llama import LlamaTokenizer
else:
						lowerCAmelCase__  	=      None

lowerCAmelCase__  	=      logging.get_logger(__name__)
lowerCAmelCase__  	=      {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__	,					lowerCAmelCase__  	=      "[INST]", "[/INST]"
lowerCAmelCase__	,					lowerCAmelCase__  	=      "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
lowerCAmelCase__  	=      "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE              =					LlamaTokenizer
	__SCREAMING_SNAKE_CASE              =					'left'
	__SCREAMING_SNAKE_CASE              =					['input_ids', 'attention_mask']


	def __init__(   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_="<unk>"       ,  lowerCAmelCase_="<s>"       ,  lowerCAmelCase_="</s>"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=False       ,  **lowerCAmelCase_       ,  ):
		super().__init__(
		    vocab_file=lowerCAmelCase_       ,  tokenizer_file=lowerCAmelCase_       ,  clean_up_tokenization_spaces=lowerCAmelCase_       ,  unk_token=lowerCAmelCase_       ,  bos_token=lowerCAmelCase_       ,  eos_token=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )
		_lowercase   =add_bos_token
		_lowercase   =add_eos_token
		self.update_post_processor()

		_lowercase   =vocab_file
		_lowercase   =False if not self.vocab_file else True


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.bos_token
		_lowercase   =self.bos_token_id

		_lowercase   =self.eos_token
		_lowercase   =self.eos_token_id

		_lowercase   =F'''{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}'''
		_lowercase   =F'''{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}'''

		_lowercase   =[]
		if self.add_bos_token:
			special_tokens.append((bos, bos_token_id)       )
		if self.add_eos_token:
			special_tokens.append((eos, eos_token_id)       )
		_lowercase   =processors.TemplateProcessing(
		    single=lowerCAmelCase_       ,  pair=lowerCAmelCase_       ,  special_tokens=lowerCAmelCase_       )


	@property
	def __lowerCAmelCase      (   self       ):
		return self._add_eos_token


	@property
	def __lowerCAmelCase      (   self       ):
		return self._add_bos_token


	@add_eos_token.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =value
		self.update_post_processor()


	@add_bos_token.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =value
		self.update_post_processor()


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
			    "tokenizer."       )

		if not os.path.isdir(lowerCAmelCase_       ):
			logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
			return
		_lowercase   =os.path.join(
		    lowerCAmelCase_       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

		if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCAmelCase_       ):
			copyfile(self.vocab_file       ,  lowerCAmelCase_       )

		return (out_vocab_file,)


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =list(conversation.iter_texts()       )
		if not all([is_user for is_user, msg in dialogue[::2]]       ) or not all(
		    [not is_user for is_user, msg in dialogue[1::2]]       ):
			raise ValueError(
			    "The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"       )

		_lowercase   =[]
		if len(conversation.past_user_inputs       ) > 0:
			if not conversation.past_user_inputs[0].startswith(lowerCAmelCase_       ) or E_SYS not in conversation.past_user_inputs[0]:
				_lowercase   =(
				    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
				)
		elif not dialogue[0][1].startswith(lowerCAmelCase_       ) or E_SYS not in dialogue[0][1]:
			_lowercase   =(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

		dialog_tokens += sum(
		    [
		        [self.bos_token_id]
		        + self.encode(
		            F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''       ,  add_special_tokens=lowerCAmelCase_       )
		        + [self.eos_token_id]
		        for prompt, answer in zip(dialogue[::2]       ,  dialogue[1::2]       )
		    ]       ,  []       ,  )
		if not (dialogue[-1][0]):
			raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}'''       )
		dialog_tokens += [self.bos_token_id] + self.encode(
		    F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''       ,  add_special_tokens=lowerCAmelCase_       )
		return dialog_tokens




