




import logging
import random

import ray

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


__SCREAMING_SNAKE_CASE     : str      =logging.getLogger(__name__)






class       A      :
     """simple docstring"""

     def __init__(     self	: Any      )					->							Union[str, Any]:
           lowerCamelCase__   	=					False




     def 			__snake_case (     self	: int						,	A__	: Optional[int]						,	A__	: str						,	A__	: int						,	A__	: List[Any]      )					->							Tuple:
           if not self.initialized:
                 lowerCamelCase__   	=					RagRetriever(
                     lowerCAmelCase_						,	question_encoder_tokenizer=lowerCAmelCase_						,	generator_tokenizer=lowerCAmelCase_						,	index=lowerCAmelCase_						,	init_retrieval=lowerCAmelCase_						,	)
                 lowerCamelCase__   	=					True




     def 			__snake_case (     self	: Tuple      )					->							Dict:
           self.retriever.index.init_index()




     def 			__snake_case (     self	: List[Any]      )					->							Any:
           # delete the old self.retriever object before assigning the new index
           del self.retriever
           lowerCamelCase__   	=					False




     def 			__snake_case (     self	: Tuple						,	A__	: Optional[int]						,	A__	: List[str]      )					->							Any:
           lowerCamelCase__	,		lowerCamelCase__   	=					self.retriever._main_retrieve(lowerCAmelCase_						,	lowerCAmelCase_      )
           lowerCamelCase__   	=					self.retriever.index.get_doc_dicts(lowerCAmelCase_      )
           return doc_ids, retrieved_doc_embeds, doc_dicts






class       A      (		lowerCamelCase_		):
     """simple docstring"""

     def __init__(     self	: Dict						,	A__	: Optional[int]						,	A__	: List[Any]						,	A__	: Optional[Any]						,	A__	: Optional[int]						,	A__	: Optional[int]=None      )					->							Dict:
           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_						,	)

           lowerCamelCase__   	=					retrieval_workers
           lowerCamelCase__   	=					question_encoder_tokenizer
           lowerCamelCase__   	=					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 			__snake_case (     self	: Any      )					->							Dict:
           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 			__snake_case (     self	: Any						,	A__	: List[Any]						,	A__	: Optional[Any]      )					->							int:
           if len(self.retrieval_workers      ) > 0:
                 # Select a random retrieval actor.
                 lowerCamelCase__   	=					self.retrieval_workers[random.randint(0						,	len(self.retrieval_workers      ) - 1      )]
                 lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					ray.get(
                     random_worker.retrieve.remote(lowerCAmelCase_						,	lowerCAmelCase_      )      )
           else:
                 lowerCamelCase__	,		lowerCamelCase__   	=					self._main_retrieve(lowerCAmelCase_						,	lowerCAmelCase_      )
                 lowerCamelCase__   	=					self.index.get_doc_dicts(lowerCAmelCase_      )
           return retrieved_doc_embeds, doc_ids, doc_dicts




     @classmethod
     def 			__snake_case (     cls	: Dict						,	A__	: Dict						,	A__	: Optional[Any]=None						,	**A__	: int      )					->							Tuple:
           return super(lowerCAmelCase_						,	cls      ).get_tokenizers(lowerCAmelCase_						,	lowerCAmelCase_						,	**lowerCAmelCase_      )




     @classmethod
     def 			__snake_case (     cls	: List[Any]						,	A__	: Dict						,	A__	: str						,	A__	: Optional[int]=None						,	**A__	: int      )					->							Optional[int]:
           lowerCamelCase__   	=					kwargs.pop('config'						,	lowerCAmelCase_      ) or RagConfig.from_pretrained(lowerCAmelCase_						,	**lowerCAmelCase_      )
           lowerCamelCase__   	=					RagTokenizer.from_pretrained(lowerCAmelCase_						,	config=lowerCAmelCase_      )
           lowerCamelCase__   	=					rag_tokenizer.question_encoder
           lowerCamelCase__   	=					rag_tokenizer.generator

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

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




     def 			__snake_case (     self	: Tuple      )					->							str:
           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
           lowerCamelCase__   	=					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
               ]      )
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'longt5'
	__SCREAMING_SNAKE_CASE              =					['past_key_values']
	__SCREAMING_SNAKE_CASE              =					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


	def __init__(   self       ,  lowerCAmelCase_=32128       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=2048       ,  lowerCAmelCase_=6       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=8       ,  lowerCAmelCase_=127       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=128       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=1e-6       ,  lowerCAmelCase_=1.0       ,  lowerCAmelCase_="relu"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_="local"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_=1       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =vocab_size
		_lowercase   =d_model
		_lowercase   =d_kv
		_lowercase   =d_ff
		_lowercase   =num_layers
		# default = symmetry
		_lowercase   =num_decoder_layers if num_decoder_layers is not None else self.num_layers
		_lowercase   =num_heads
		_lowercase   =local_radius
		_lowercase   =global_block_size
		_lowercase   =relative_attention_num_buckets
		_lowercase   =relative_attention_max_distance
		_lowercase   =dropout_rate
		_lowercase   =layer_norm_epsilon
		_lowercase   =initializer_factor
		_lowercase   =feed_forward_proj
		_lowercase   =encoder_attention_type
		_lowercase   =use_cache

		_lowercase   =self.feed_forward_proj.split("-"       )
		_lowercase   =act_info[-1]
		_lowercase   =act_info[0] == "gated"

		if len(lowerCAmelCase_       ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_       ) > 2:
			raise ValueError(
			    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
			    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
			    "'gated-gelu' or 'relu'"       )

		# for backwards compatibility
		if feed_forward_proj == "gated-gelu":
			_lowercase   ="gelu_new"

		super().__init__(
		    pad_token_id=lowerCAmelCase_       ,  eos_token_id=lowerCAmelCase_       ,  is_encoder_decoder=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   ={
		    "input_ids": {0: "batch", 1: "encoder_sequence"},
		    "attention_mask": {0: "batch", 1: "encoder_sequence"},
		}
		if self.use_past:
			_lowercase   ="past_encoder_sequence + sequence"
			_lowercase   ={0: "batch"}
			_lowercase   ={0: "batch", 1: "past_decoder_sequence + sequence"}
		else:
			_lowercase   ={0: "batch", 1: "decoder_sequence"}
			_lowercase   ={0: "batch", 1: "decoder_sequence"}

		if self.use_past:
			self.fill_with_past_key_values_(lowerCAmelCase_       ,  direction="inputs"       )

		return common_inputs


	@property
	def __lowerCAmelCase      (   self       ):
		return 13




