
"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_A						=			logging.get_logger(__name__)

_A						=			{
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'levit'




	def __init__(			self		,			UpperCAmelCase_=224		,			UpperCAmelCase_=3		,			UpperCAmelCase_=3		,			UpperCAmelCase_=2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=16		,			UpperCAmelCase_=[128, 256, 384]		,			UpperCAmelCase_=[4, 8, 12]		,			UpperCAmelCase_=[4, 4, 4]		,			UpperCAmelCase_=[16, 16, 16]		,			UpperCAmelCase_=0		,			UpperCAmelCase_=[2, 2, 2]		,			UpperCAmelCase_=[2, 2, 2]		,			UpperCAmelCase_=0.02		,			**UpperCAmelCase_		,			)		->							Tuple:
			super().__init__(**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   image_size
			lowerCamelCase					:      int          =   num_channels
			lowerCamelCase					:      Tuple          =   kernel_size
			lowerCamelCase					:      Any          =   stride
			lowerCamelCase					:      Union[str, Any]          =   padding
			lowerCamelCase					:      Any          =   hidden_sizes
			lowerCamelCase					:      Optional[Any]          =   num_attention_heads
			lowerCamelCase					:      int          =   depths
			lowerCamelCase					:      int          =   key_dim
			lowerCamelCase					:      Tuple          =   drop_path_rate
			lowerCamelCase					:      Any          =   patch_size
			lowerCamelCase					:      Tuple          =   attention_ratio
			lowerCamelCase					:      List[str]          =   mlp_ratio
			lowerCamelCase					:      Optional[Any]          =   initializer_range
			lowerCamelCase					:      Any          =   [
			    ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
			    ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
			]






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     version.parse('1.11'		)




	@property
	def       _UpperCamelCase   (			self )		->							Mapping[str, Mapping[int, str]]:
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ] )




	@property
	def       _UpperCamelCase   (			self )		->							float:
			return 1E-4








"""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 	_lowercase       :




	def __init__(			self )		->							Tuple:
			lowerCamelCase					:      Dict          =   False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			if not self.initialized:
					lowerCamelCase					:      Optional[int]          =   RagRetriever(
					    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
					lowerCamelCase					:      Union[str, Any]          =   True




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			self.retriever.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase     ,   lowerCamelCase					:      int          =   self.retriever._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return doc_ids, retrieved_doc_embeds






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Dict:
			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					:      List[str]          =   retrieval_workers
			if len(self.retrieval_workers ) > 0:
					ray.get(
					    [
					        worker.create_rag_retriever.remote(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
					        for worker in self.retrieval_workers
					    ] )




	def       _UpperCamelCase   (			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       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			if len(self.retrieval_workers ) > 0:
					# Select a random retrieval actor.
					lowerCamelCase					:      Tuple          =   self.retrieval_workers[random.randint(0		,			len(self.retrieval_workers ) - 1 )]
					lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   ray.get(random_worker.retrieve.remote(UpperCAmelCase_		,			UpperCAmelCase_ ) )
			else:
					lowerCamelCase     ,   lowerCamelCase					:      int          =   self._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			return super(UpperCAmelCase_		,			cls ).get_tokenizers(UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      str          =   kwargs.pop('config'		,			UpperCAmelCase_ ) or RagConfig.from_pretrained(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   RagTokenizer.from_pretrained(UpperCAmelCase_		,			config=UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   rag_tokenizer.question_encoder
			lowerCamelCase					:      Union[str, Any]          =   rag_tokenizer.generator
			if indexed_dataset is not None:
					lowerCamelCase					:      int          =   'custom'
					lowerCamelCase					:      int          =   CustomHFIndex(config.retrieval_vector_size		,			UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[Any]          =   cls._build_index(UpperCAmelCase_ )
			return cls(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			retrieval_workers=UpperCAmelCase_		,			index=UpperCAmelCase_		,			)






