




import logging
import random

import ray

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


__A								=       logging.getLogger(__name__)







class        __lowerCAmelCase					:

			"""simple docstring"""







			def __init__( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       False


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> str:



							'''simple docstring'''
							if not self.initialized:
											__lowerCamelCase									=       RagRetriever(
											    lowerCamelCase__     ,		question_encoder_tokenizer=lowerCamelCase__     ,		generator_tokenizer=lowerCamelCase__     ,		index=lowerCamelCase__     ,		init_retrieval=lowerCamelCase__     ,		)
											__lowerCamelCase									=       True


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							self.retriever.index.init_index()






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self.retriever._main_retrieve(lowerCamelCase__     ,		lowerCamelCase__				)
							return doc_ids, retrieved_doc_embeds







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None				)       -> List[Any]:



							'''simple docstring'''
							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
							if len(self.retrieval_workers				) > 0:
											ray.get(
											    [
											        worker.create_rag_retriever.remote(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)
											        for worker in self.retrieval_workers
											    ]				)


			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							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									=       ray.get(random_worker.retrieve.remote(lowerCamelCase__     ,		lowerCamelCase__				)				)
							else:
											__lowerCamelCase			,		__lowerCamelCase									=       self._main_retrieve(lowerCamelCase__     ,		lowerCamelCase__				)
							return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCamelCase__				)


			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__     ,		lowerCamelCase__=None     ,		**lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							return super(lowerCamelCase__     ,		cls				).get_tokenizers(lowerCamelCase__     ,		lowerCamelCase__     ,		**lowerCamelCase__				)






			@classmethod
			def  lowercase_       ( cls     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None     ,		**lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__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__     ,		)








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.activations import gelu_new, gelu_python, get_activation







@require_torch
class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							__lowerCamelCase									=       get_activation('gelu'				)
							self.assertTrue(torch.allclose(gelu_python(lowerCamelCase__				)     ,		torch_builtin(lowerCamelCase__				)				)				)
							self.assertFalse(torch.allclose(gelu_python(lowerCamelCase__				)     ,		gelu_new(lowerCamelCase__				)				)				)


			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							__lowerCamelCase									=       get_activation('gelu'				)
							__lowerCamelCase									=       get_activation('gelu_10'				)

							__lowerCamelCase									=       torch_builtin(lowerCamelCase__				)
							__lowerCamelCase									=       geluaa(lowerCamelCase__				)

							__lowerCamelCase									=       torch.where(y_gelu_aa < 10.0     ,		1     ,		0				)

							self.assertTrue(torch.max(lowerCamelCase__				).item() == 10.0				)
							self.assertTrue(torch.allclose(y_gelu * clipped_mask     ,		y_gelu_aa * clipped_mask				)				)


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							get_activation('gelu'				)
							get_activation('gelu_10'				)
							get_activation('gelu_fast'				)
							get_activation('gelu_new'				)
							get_activation('gelu_python'				)
							get_activation('gelu_pytorch_tanh'				)
							get_activation('linear'				)
							get_activation('mish'				)
							get_activation('quick_gelu'				)
							get_activation('relu'				)
							get_activation('sigmoid'				)
							get_activation('silu'				)
							get_activation('swish'				)
							get_activation('tanh'				)
							with self.assertRaises(lowerCamelCase__				):
											get_activation('bogus'				)
							with self.assertRaises(lowerCamelCase__				):
											get_activation(lowerCamelCase__				)






			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       get_activation('gelu'				)
							__lowerCamelCase									=       1
							__lowerCamelCase									=       get_activation('gelu'				)
							self.assertEqual(acta.a     ,		1				)
							with self.assertRaises(lowerCamelCase__				):
											__lowerCamelCase									=       acta.a


