



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
		from transformers import TvltImageProcessor

if is_speech_available():
		from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class    snake_case__				(unittest.TestCase		):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(   self  )					->      int:
									"""simple docstring"""




									a__   :						Optional[Any]						       =  """ZinengTang/tvlt-base"""
									a__   :						Dict						       =  tempfile.mkdtemp()

						def 							SCREAMING_SNAKE_CASE__(   self					,   **__lowercase  )					->      Optional[Any]:
									"""simple docstring"""




									return TvltImageProcessor.from_pretrained(self.checkpoint					,   **__SCREAMING_SNAKE_CASE  )

						def 							SCREAMING_SNAKE_CASE__(   self					,   **__lowercase  )					->      Optional[int]:
									"""simple docstring"""




									return TvltFeatureExtractor.from_pretrained(self.checkpoint					,   **__SCREAMING_SNAKE_CASE  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      List[Any]:
									"""simple docstring"""




									shutil.rmtree(self.tmpdirname  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      str:
									"""simple docstring"""




									a__   :						Any						       =  self.get_image_processor()
									a__   :						Optional[int]						       =  self.get_feature_extractor()

									a__   :						str						       =  TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,   feature_extractor=__SCREAMING_SNAKE_CASE  )
									processor.save_pretrained(self.tmpdirname  )
									a__   :						Optional[Any]						       =  TvltProcessor.from_pretrained(self.tmpdirname  )

									self.assertIsInstance(processor.feature_extractor					,   __SCREAMING_SNAKE_CASE  )
									self.assertIsInstance(processor.image_processor					,   __SCREAMING_SNAKE_CASE  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      int:
									"""simple docstring"""




									a__   :						List[str]						       =  self.get_image_processor()
									a__   :						str						       =  self.get_feature_extractor()

									a__   :						List[str]						       =  TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,   feature_extractor=__SCREAMING_SNAKE_CASE  )

									a__   :						Optional[int]						       =  np.ones([1_2_0_0_0]  )

									a__   :						List[str]						       =  feature_extractor(__SCREAMING_SNAKE_CASE					,   return_tensors="""np"""  )
									a__   :						Any						       =  processor(audio=__SCREAMING_SNAKE_CASE					,   return_tensors="""np"""  )

									for key in audio_dict.keys():
												self.assertAlmostEqual(audio_dict[key].sum()					,   input_processor[key].sum()					,   delta=1E-2  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									a__   :						str						       =  self.get_image_processor()
									a__   :						int						       =  self.get_feature_extractor()

									a__   :						str						       =  TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,   feature_extractor=__SCREAMING_SNAKE_CASE  )

									a__   :						List[str]						       =  np.ones([3, 2_2_4, 2_2_4]  )

									a__   :						Optional[Any]						       =  image_processor(__SCREAMING_SNAKE_CASE					,   return_tensors="""np"""  )
									a__   :						List[str]						       =  processor(images=__SCREAMING_SNAKE_CASE					,   return_tensors="""np"""  )

									for key in image_dict.keys():
												self.assertAlmostEqual(image_dict[key].sum()					,   input_processor[key].sum()					,   delta=1E-2  )

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Dict:
									"""simple docstring"""




									a__   :						Optional[Any]						       =  self.get_image_processor()
									a__   :						Any						       =  self.get_feature_extractor()

									a__   :						int						       =  TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,   feature_extractor=__SCREAMING_SNAKE_CASE  )

									a__   :						Optional[int]						       =  np.ones([1_2_0_0_0]  )
									a__   :						Optional[Any]						       =  np.ones([3, 2_2_4, 2_2_4]  )

									a__   :						Union[str, Any]						       =  processor(audio=__SCREAMING_SNAKE_CASE					,   images=__SCREAMING_SNAKE_CASE  )

									self.assertListEqual(list(inputs.keys()  )					,   ["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""]  )

									# test if it raises when no input is passed
									with pytest.raises(__SCREAMING_SNAKE_CASE  ):
												processor()

						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Union[str, Any]:
									"""simple docstring"""




									a__   :						Optional[int]						       =  self.get_image_processor()
									a__   :						Any						       =  self.get_feature_extractor()

									a__   :						Any						       =  TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE					,   feature_extractor=__SCREAMING_SNAKE_CASE  )

									self.assertListEqual(
									    processor.model_input_names					,   image_processor.model_input_names + feature_extractor.model_input_names					,   msg="""`processor` and `image_processor`+`feature_extractor` model input names do not match"""					,   )









import logging
import random

import ray

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


__snake_case						:List[Any]      		=     logging.getLogger(__name__)

class     _A       :



       def __init__(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				False



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           if not self.initialized:
               __a						       =				RagRetriever(
                   __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				True



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           self.retriever.index.init_index()





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return doc_ids, retrieved_doc_embeds


class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None):


           '''simple docstring'''

           if index is not None and index.is_initialized() and len(__SCREAMING_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__(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				retrieval_workers
           if len(self.retrieval_workers) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
                       for worker in self.retrieval_workers
                   ])



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''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 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

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



       @classmethod
       def 		_lowerCamelCase			(		cls							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           return super(__SCREAMING_SNAKE_CASE     ,		cls).get_tokenizers(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)





       @classmethod
       def 		_lowerCamelCase			(		cls							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				kwargs.pop('''config'''     ,		__SCREAMING_SNAKE_CASE) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     ,		config=__SCREAMING_SNAKE_CASE)
           __a						       =				rag_tokenizer.question_encoder
           __a						       =				rag_tokenizer.generator
           if indexed_dataset is not None:
               __a						       =				'''custom'''
               __a						       =				CustomHFIndex(config.retrieval_vector_size     ,		__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				cls._build_index(__SCREAMING_SNAKE_CASE)
           return cls(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		retrieval_workers=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		)





