
"""simple docstring"""

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'sew'




	def __init__(			self		,			UpperCAmelCase_=32		,			UpperCAmelCase_=768		,			UpperCAmelCase_=12		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3072		,			UpperCAmelCase_=2		,			UpperCAmelCase_="gelu"		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-5		,			UpperCAmelCase_="group"		,			UpperCAmelCase_="gelu"		,			UpperCAmelCase_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,			UpperCAmelCase_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,			UpperCAmelCase_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,			UpperCAmelCase_=False		,			UpperCAmelCase_=128		,			UpperCAmelCase_=16		,			UpperCAmelCase_=True		,			UpperCAmelCase_=0.05		,			UpperCAmelCase_=10		,			UpperCAmelCase_=2		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=10		,			UpperCAmelCase_=0		,			UpperCAmelCase_="mean"		,			UpperCAmelCase_=False		,			UpperCAmelCase_=False		,			UpperCAmelCase_=256		,			UpperCAmelCase_=0		,			UpperCAmelCase_=1		,			UpperCAmelCase_=2		,			**UpperCAmelCase_		,			)		->							int:
			super().__init__(**UpperCAmelCase_		,			pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_ )
			lowerCamelCase					:      Union[str, Any]          =   hidden_size
			lowerCamelCase					:      str          =   feat_extract_norm
			lowerCamelCase					:      str          =   feat_extract_activation
			lowerCamelCase					:      Any          =   list(UpperCAmelCase_ )
			lowerCamelCase					:      str          =   list(UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   list(UpperCAmelCase_ )
			lowerCamelCase					:      Optional[Any]          =   conv_bias
			lowerCamelCase					:      Dict          =   num_conv_pos_embeddings
			lowerCamelCase					:      int          =   num_conv_pos_embedding_groups
			lowerCamelCase					:      Tuple          =   len(self.conv_dim )
			lowerCamelCase					:      str          =   num_hidden_layers
			lowerCamelCase					:      int          =   intermediate_size
			lowerCamelCase					:      Dict          =   squeeze_factor
			lowerCamelCase					:      Any          =   hidden_act
			lowerCamelCase					:      int          =   num_attention_heads
			lowerCamelCase					:      Any          =   hidden_dropout
			lowerCamelCase					:      str          =   attention_dropout
			lowerCamelCase					:      Optional[int]          =   activation_dropout
			lowerCamelCase					:      int          =   feat_proj_dropout
			lowerCamelCase					:      int          =   final_dropout
			lowerCamelCase					:      Optional[Any]          =   layerdrop
			lowerCamelCase					:      Tuple          =   layer_norm_eps
			lowerCamelCase					:      str          =   initializer_range
			lowerCamelCase					:      List[str]          =   vocab_size

			if (
			    (len(self.conv_stride ) != self.num_feat_extract_layers)
			    or (len(self.conv_kernel ) != self.num_feat_extract_layers)
			    or (len(self.conv_dim ) != self.num_feat_extract_layers)
			):
					raise ValueError(
					    'Configuration for convolutional layers is incorrect.'
					    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
					    F"""but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"""
					    F"""= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.""" )

			# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
			lowerCamelCase					:      Optional[Any]          =   apply_spec_augment
			lowerCamelCase					:      Tuple          =   mask_time_prob
			lowerCamelCase					:      Optional[Any]          =   mask_time_length
			lowerCamelCase					:      Tuple          =   mask_time_min_masks
			lowerCamelCase					:      Tuple          =   mask_feature_prob
			lowerCamelCase					:      Any          =   mask_feature_length
			lowerCamelCase					:      Optional[Any]          =   mask_feature_min_masks

			# ctc loss
			lowerCamelCase					:      Any          =   ctc_loss_reduction
			lowerCamelCase					:      Any          =   ctc_zero_infinity

			# sequence classification
			lowerCamelCase					:      Dict          =   use_weighted_layer_sum
			lowerCamelCase					:      Optional[Any]          =   classifier_proj_size




	@property
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			return functools.reduce(operator.mul		,			self.conv_stride		,			1 )








"""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_		,			)






