import logging
import random

import ray

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


__snake_case         =     logging.getLogger(__name__)







class       __snake_case :
 def __init__(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=False
 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       if not self.initialized:
             UpperCAmelCase     :			Tuple     	=RagRetriever(
                 snake_case__		, question_encoder_tokenizer=snake_case__		, generator_tokenizer=snake_case__		, index=snake_case__		, init_retrieval=snake_case__		, )
             UpperCAmelCase     :			Optional[int]     	=True
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''simple docstring'''





       self.retriever.index.init_index()
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       del self.retriever
       UpperCAmelCase     :			List[Any]     	=False






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase	,    UpperCAmelCase     :			int     	=self.retriever._main_retrieve(snake_case__		, snake_case__    )
       UpperCAmelCase     :			str     	=self.retriever.index.get_doc_dicts(snake_case__    )
       return doc_ids, retrieved_doc_embeds, doc_dicts







class       __snake_case (  lowerCamelCase__ ):
 def __init__(	self		, snake_case__		, snake_case__		, snake_case__		, snake_case__		, snake_case__=None    )						->				Optional[Any]:
       '''simple docstring'''





       if index is not None and index.is_initialized() and len(snake_case__    ) > 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__(
           snake_case__		, question_encoder_tokenizer=snake_case__		, generator_tokenizer=snake_case__		, index=snake_case__		, init_retrieval=snake_case__		, )

       UpperCAmelCase     :			Optional[int]     	=retrieval_workers
       UpperCAmelCase     :			Union[str, Any]     	=question_encoder_tokenizer
       UpperCAmelCase     :			Dict     	=generator_tokenizer
       if len(self.retrieval_workers    ) > 0:
             ray.get(
                 [
                     worker.create_rag_retriever.remote(snake_case__		, snake_case__		, snake_case__		, snake_case__    )
                     for worker in self.retrieval_workers
                 ]    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[int]:
       '''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 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       if len(self.retrieval_workers    ) > 0:
             # Select a random retrieval actor.
             UpperCAmelCase     :			List[Any]     	=self.retrieval_workers[random.randint(0		, len(self.retrieval_workers    ) - 1    )]
             UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			Tuple     	=ray.get(
                 random_worker.retrieve.remote(snake_case__		, snake_case__    )    )
       else:
             UpperCAmelCase	,    UpperCAmelCase     :			Optional[Any]     	=self._main_retrieve(snake_case__		, snake_case__    )
             UpperCAmelCase     :			str     	=self.index.get_doc_dicts(snake_case__    )
       return retrieved_doc_embeds, doc_ids, doc_dicts
 @classmethod
 def 						UpperCAmelCase__							(	cls		, snake_case__		, snake_case__=None		, **snake_case__    )						->				int:
       '''simple docstring'''





       return super(snake_case__		, cls    ).get_tokenizers(snake_case__		, snake_case__		, **snake_case__    )
 @classmethod
 def 						UpperCAmelCase__							(	cls		, snake_case__		, snake_case__		, snake_case__=None		, **snake_case__    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=kwargs.pop('''config'''		, snake_case__    ) or RagConfig.from_pretrained(snake_case__		, **snake_case__    )
       UpperCAmelCase     :			Union[str, Any]     	=RagTokenizer.from_pretrained(snake_case__		, config=snake_case__    )
       UpperCAmelCase     :			Optional[int]     	=rag_tokenizer.question_encoder
       UpperCAmelCase     :			str     	=rag_tokenizer.generator

       if indexed_dataset is not None:
             UpperCAmelCase     :			List[str]     	='''custom'''
             UpperCAmelCase     :			int     	=CustomHFIndex(config.retrieval_vector_size		, snake_case__    )
       else:
             UpperCAmelCase     :			List[Any]     	=cls._build_index(snake_case__    )

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






 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       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
       UpperCAmelCase     :			Optional[Any]     	=self._build_index(self.config    )

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

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   SpeechaTextaTokenizer
 __lowerCamelCase     : Tuple 	=   False
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       super().setUp()

       UpperCAmelCase     :			Union[str, Any]     	='''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''    )
       UpperCAmelCase     :			int     	=['''he re</w> 123''', '''here a 1456''']
       UpperCAmelCase     :			int     	=dict(zip(snake_case__		, range(len(snake_case__    )    )    )    )

       UpperCAmelCase     :			Tuple     	={'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

       UpperCAmelCase     :			Tuple     	=tempfile.mkdtemp()
       UpperCAmelCase     :			Optional[Any]     	=os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''vocab_file''']    )
       UpperCAmelCase     :			Dict     	=os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''merges_file''']    )

       with open(self.vocab_file		, '''w'''		, encoding='''utf-8'''    ) as fp:
             fp.write(json.dumps(snake_case__    ) + '''\n'''    )

       with open(self.merges_file		, '''w'''    ) as fp:
             fp.write('''\n'''.join(snake_case__    )    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]		, '''<s>'''    )
       self.assertEqual(vocab_keys[1]		, '''<pad>'''    )
       self.assertEqual(vocab_keys[-1]		, '''vocab'''    )
       self.assertEqual(len(snake_case__    )		, 14    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       self.assertEqual(self.get_tokenizer().vocab_size		, 14    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

       # make sure @@ is correctly concatenated
       UpperCAmelCase     :			str     	=[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
       UpperCAmelCase     :			Optional[Any]     	=tokenizer.decode(snake_case__    )

       self.assertTrue(output_string == '''herecouple words ofthe'''    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

       with tempfile.TemporaryDirectory() as tmp_dirname:
             tokenizer.save_pretrained(snake_case__    )
             os.remove(os.path.join(snake_case__		, '''merges.txt'''    )    )

             # load tokenizer without merges file should not throw an error
             UpperCAmelCase     :			Any     	=SpeechaTextaTokenizer.from_pretrained(snake_case__    )

       with tempfile.TemporaryDirectory() as tmp_dirname:
             # save tokenizer and load again
             tokenizer.save_pretrained(snake_case__    )
             UpperCAmelCase     :			List[str]     	=SpeechaTextaTokenizer.from_pretrained(snake_case__    )

       self.assertIsNotNone(snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       if not self.test_slow_tokenizer:
             return

       UpperCAmelCase     :			List[str]     	=inspect.signature(self.tokenizer_class.__init__    )
       UpperCAmelCase     :			Union[str, Any]     	=self.get_tokenizer()

       for parameter_name, parameter in signature.parameters.items():
             if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                   self.assertIn(snake_case__		, tokenizer.init_kwargs    )
