




'''simple docstring'''


import logging
import random

import ray

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


_snake_case : int  				=						logging.getLogger(__name__)

class      A       :







							def __init__(    self			:    Union[str, Any]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							False







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    Any   )     ->				int:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[str]:


														"""simple docstring"""


														self.retriever.index.init_index()







							def   __lowerCAmelCase     (    self			:    str				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    Optional[int]   )     ->				Any:


														"""simple docstring"""


														_a	,				_a      	=							self.retriever._main_retrieve(lowerCAmelCase_				,     lowerCAmelCase_   )
														return doc_ids, retrieved_doc_embeds



class      A       (						_a       ):







							def __init__(    self			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    int=None   )     ->				Union[str, Any]:


														"""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_				,     )
														_a      	=							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   __lowerCAmelCase     (    self			:    int   )     ->				int:


														"""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			:    List[str]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Union[str, Any]   )     ->				int:


														"""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(lowerCAmelCase_				,     lowerCAmelCase_   )   )
														else:
																					_a	,				_a      	=							self._main_retrieve(lowerCAmelCase_				,     lowerCAmelCase_   )
														return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(lowerCAmelCase_   )







							@classmethod
							def   __lowerCAmelCase     (    cls			:    Tuple				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    Union[str, Any]=None				,     **lowerCAmelCase_			:    Union[str, Any]   )     ->				List[Any]:


														"""simple docstring"""


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







							@classmethod
							def   __lowerCAmelCase     (    cls			:    Dict				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Optional[Any]				,     lowerCAmelCase_			:    Union[str, Any]=None				,     **lowerCAmelCase_			:    List[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							kwargs.pop('''config'''				,     lowerCAmelCase_   ) or RagConfig.from_pretrained(lowerCAmelCase_				,     **lowerCAmelCase_   )
														_a      	=							RagTokenizer.from_pretrained(lowerCAmelCase_				,     config=lowerCAmelCase_   )
														_a      	=							rag_tokenizer.question_encoder
														_a      	=							rag_tokenizer.generator
														if indexed_dataset is not None:
																					_a      	=							'''custom'''
																					_a      	=							CustomHFIndex(config.retrieval_vector_size				,     lowerCAmelCase_   )
														else:
																					_a      	=							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 Mapping

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


_snake_case : Optional[int]  				=						logging.get_logger(__name__)

_snake_case : Any  				=						{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class      A       (						_a       ):
							lowercase_							     =							'roformer'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int=5_00_00				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    Tuple=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[str]=30_72				,     lowerCAmelCase_			:    Dict="gelu"				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    int=15_36				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.0_2				,     lowerCAmelCase_			:    Dict=1e-12				,     lowerCAmelCase_			:    Any=0				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Tuple=True				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				str:


														"""simple docstring"""


														super().__init__(pad_token_id=lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							vocab_size
														_a      	=							hidden_size if embedding_size is None else embedding_size
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							hidden_act
														_a      	=							intermediate_size
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							max_position_embeddings
														_a      	=							type_vocab_size
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							rotary_value
														_a      	=							use_cache



class      A       (						_a       ):







							@property
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														if self.task == "multiple-choice":
																					_a      	=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					_a      	=							{0: '''batch''', 1: '''sequence'''}
														_a      	=							{0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														        ('''token_type_ids''', dynamic_axis),
														    ]   )

