"""simple docstring"""






import logging
import random

import ray

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


__lowerCAmelCase    :       int					     =logging.getLogger(__name__)





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	False

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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

      def 				A__       (						self						):







            """simple docstring"""



            self.retriever.index.init_index()







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.retriever._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return doc_ids, retrieved_doc_embeds






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """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		,       )
            lowercase														=	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 				A__       (						self						):







            """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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



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







      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



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




"""simple docstring"""






from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


__lowerCAmelCase    :       List[Any]					     =logging.get_logger(__name__)

__lowerCAmelCase    :       Union[str, Any]					     ={
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}





class  _A						(       lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	'efficientnet'

      def __init__(						self		,       __lowerCAmelCase = 3		,       __lowerCAmelCase = 600		,       __lowerCAmelCase = 2.0		,       __lowerCAmelCase = 3.1		,       __lowerCAmelCase = 8		,       __lowerCAmelCase = [3, 3, 5, 3, 5, 5, 3]		,       __lowerCAmelCase = [32, 16, 24, 40, 80, 112, 192]		,       __lowerCAmelCase = [16, 24, 40, 80, 112, 192, 320]		,       __lowerCAmelCase = []		,       __lowerCAmelCase = [1, 2, 2, 2, 1, 2, 1]		,       __lowerCAmelCase = [1, 2, 2, 3, 3, 4, 1]		,       __lowerCAmelCase = [1, 6, 6, 6, 6, 6, 6]		,       __lowerCAmelCase = 0.2_5		,       __lowerCAmelCase = "swish"		,       __lowerCAmelCase = 2560		,       __lowerCAmelCase = "mean"		,       __lowerCAmelCase = 0.0_2		,       __lowerCAmelCase = 0.0_0_1		,       __lowerCAmelCase = 0.9_9		,       __lowerCAmelCase = 0.5		,       __lowerCAmelCase = 0.2		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase						)

            lowercase														=	num_channels
            lowercase														=	image_size
            lowercase														=	width_coefficient
            lowercase														=	depth_coefficient
            lowercase														=	depth_divisor
            lowercase														=	kernel_sizes
            lowercase														=	in_channels
            lowercase														=	out_channels
            lowercase														=	depthwise_padding
            lowercase														=	strides
            lowercase														=	num_block_repeats
            lowercase														=	expand_ratios
            lowercase														=	squeeze_expansion_ratio
            lowercase														=	hidden_act
            lowercase														=	hidden_dim
            lowercase														=	pooling_type
            lowercase														=	initializer_range
            lowercase														=	batch_norm_eps
            lowercase														=	batch_norm_momentum
            lowercase														=	dropout_rate
            lowercase														=	drop_connect_rate
            lowercase														=	sum(__lowerCAmelCase						) * 4






class  _A						(       lowerCAmelCase	):
      snake_case__			:   Dict							  =	version.parse('1.11'	)

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return OrderedDict(
                [
                    ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ]						)







      @property
      def 				A__       (						self						):







            """simple docstring"""



            return 1E-5



