
"""simple docstring"""


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						)				->  int:
				_lowerCAmelCase			=False



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  str:
				if not self.initialized:
					_lowerCAmelCase			=RagRetriever(
					    __UpperCAmelCase  ,  question_encoder_tokenizer=__UpperCAmelCase  ,  generator_tokenizer=__UpperCAmelCase  ,  index=__UpperCAmelCase  ,  init_retrieval=__UpperCAmelCase  ,  )
					_lowerCAmelCase			=True



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				self.retriever.index.init_index()



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				# delete the old self.retriever object before assigning the new index
				del self.retriever
				_lowerCAmelCase			=False



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  Dict:
				_lowerCAmelCase		,							_lowerCAmelCase			=self.retriever._main_retrieve(__UpperCAmelCase  ,  __UpperCAmelCase						)
				_lowerCAmelCase			=self.retriever.index.get_doc_dicts(__UpperCAmelCase						)
				return doc_ids, retrieved_doc_embeds, doc_dicts







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



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

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



			def       _lowerCAmelCase	(							self						)				->  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       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  List[Any]:
				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(__UpperCAmelCase  ,  __UpperCAmelCase						)						)
				else:
					_lowerCAmelCase		,							_lowerCAmelCase			=self._main_retrieve(__UpperCAmelCase  ,  __UpperCAmelCase						)
					_lowerCAmelCase			=self.index.get_doc_dicts(__UpperCAmelCase						)
				return retrieved_doc_embeds, doc_ids, doc_dicts



			@classmethod
			def       _lowerCAmelCase	(							cls  ,  __UpperCAmelCase  ,  __UpperCAmelCase=None  ,  **__UpperCAmelCase						)				->  Any:
				return super(__UpperCAmelCase  ,  cls						).get_tokenizers(__UpperCAmelCase  ,  __UpperCAmelCase  ,  **__UpperCAmelCase						)



			@classmethod
			def       _lowerCAmelCase	(							cls  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase=None  ,  **__UpperCAmelCase						)				->  Any:
				_lowerCAmelCase			=kwargs.pop("""config"""  ,  __UpperCAmelCase						) or RagConfig.from_pretrained(__UpperCAmelCase  ,  **__UpperCAmelCase						)
				_lowerCAmelCase			=RagTokenizer.from_pretrained(__UpperCAmelCase  ,  config=__UpperCAmelCase						)
				_lowerCAmelCase			=rag_tokenizer.question_encoder
				_lowerCAmelCase			=rag_tokenizer.generator

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

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



			def       _lowerCAmelCase	(							self						)				->  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  ,  __UpperCAmelCase						)
				        for worker in self.retrieval_workers
				    ]						)





"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A 		= {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConvNextV2ForImageClassification',
			    'ConvNextV2Model',
			    'ConvNextV2PreTrainedModel',
			    'ConvNextV2Backbone',
			]


if TYPE_CHECKING:
			from .configuration_convnextva import (
			    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ConvNextVaConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnextva import (
						    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextVaBackbone,
						    ConvNextVaForImageClassification,
						    ConvNextVaModel,
						    ConvNextVaPreTrainedModel,
						)

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure)



