'''simple docstring'''




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


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

__snake_case  : List[str]		       =    {
    'microsoft/layoutlm-base-uncased': (
        'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'
    ),
    'microsoft/layoutlm-large-uncased': (
        'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'
    ),
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   Tuple  					=							'layoutlm'

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=3_0522       ,	_SCREAMING_SNAKE_CASE=768       ,	_SCREAMING_SNAKE_CASE=12       ,	_SCREAMING_SNAKE_CASE=12       ,	_SCREAMING_SNAKE_CASE=3072       ,	_SCREAMING_SNAKE_CASE="gelu"       ,	_SCREAMING_SNAKE_CASE=0.1       ,	_SCREAMING_SNAKE_CASE=0.1       ,	_SCREAMING_SNAKE_CASE=512       ,	_SCREAMING_SNAKE_CASE=2       ,	_SCREAMING_SNAKE_CASE=0.02       ,	_SCREAMING_SNAKE_CASE=1E-12       ,	_SCREAMING_SNAKE_CASE=0       ,	_SCREAMING_SNAKE_CASE="absolute"       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=1024       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	Tuple:
           super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           A_      =  vocab_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_      =  position_embedding_type
           A_      =  use_cache
           A_      =  max_ad_position_embeddings




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = "default"       ,	_SCREAMING_SNAKE_CASE = None       ,	)						->	List[str]:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	task=_SCREAMING_SNAKE_CASE       ,	patching_specs=_SCREAMING_SNAKE_CASE					)
           A_      =  config.max_ad_position_embeddings - 1

       @property
       def    __A			( self					)						->	Mapping[str, Mapping[int, str]]:
           return OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                   ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                   ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
               ]					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = -1       ,	_SCREAMING_SNAKE_CASE = -1       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = None       ,	)						->	Mapping[str, Any]:
           A_      =  super().generate_dummy_inputs(
               _SCREAMING_SNAKE_CASE       ,	batch_size=_SCREAMING_SNAKE_CASE       ,	seq_length=_SCREAMING_SNAKE_CASE       ,	is_pair=_SCREAMING_SNAKE_CASE       ,	framework=_SCREAMING_SNAKE_CASE					)

           # Generate a dummy bbox
           A_      =  [48, 84, 73, 128]

           if not framework == TensorType.PYTORCH:
               raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''					)

           if not is_torch_available():
               raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''					)
           import torch

           A_  ,A_      =  input_dict['''input_ids'''].shape
           A_      =  torch.tensor([*[box] * seq_length]					).tile(_SCREAMING_SNAKE_CASE       ,	1       ,	1					)
           return input_dict







'''simple docstring'''




import logging
import random

import ray

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


__snake_case  : Optional[int]		       =    logging.getLogger(__name__)




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self					)						->	List[str]:
           A_      =  False

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

       def    __A			( self					)						->	List[Any]:
           self.retriever.index.init_index()

       def    __A			( self					)						->	Dict:
           # delete the old self.retriever object before assigning the new index
           del self.retriever
           A_      =  False

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           A_  ,A_      =  self.retriever._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  self.retriever.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return doc_ids, retrieved_doc_embeds, doc_dicts




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None					)						->	List[Any]:
           if index is not None and index.is_initialized() and len(_SCREAMING_SNAKE_CASE					) > 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__(
               _SCREAMING_SNAKE_CASE       ,	question_encoder_tokenizer=_SCREAMING_SNAKE_CASE       ,	generator_tokenizer=_SCREAMING_SNAKE_CASE       ,	index=_SCREAMING_SNAKE_CASE       ,	init_retrieval=_SCREAMING_SNAKE_CASE       ,	)

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

       def    __A			( self					)						->	List[Any]:
           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       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Optional[int]:
           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_  ,A_      =  ray.get(
                   random_worker.retrieve.remote(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)					)
           else:
               A_  ,A_      =  self._main_retrieve(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
               A_      =  self.index.get_doc_dicts(_SCREAMING_SNAKE_CASE					)
           return retrieved_doc_embeds, doc_ids, doc_dicts

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	List[Any]:
           return super(_SCREAMING_SNAKE_CASE       ,	cls					).get_tokenizers(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @classmethod
       def    __A			( cls       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None       ,	**_SCREAMING_SNAKE_CASE					)						->	int:
           A_      =  kwargs.pop('''config'''       ,	_SCREAMING_SNAKE_CASE					) or RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           A_      =  RagTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE       ,	config=_SCREAMING_SNAKE_CASE					)
           A_      =  rag_tokenizer.question_encoder
           A_      =  rag_tokenizer.generator

           if indexed_dataset is not None:
               A_      =  '''custom'''
               A_      =  CustomHFIndex(config.retrieval_vector_size       ,	_SCREAMING_SNAKE_CASE					)
           else:
               A_      =  cls._build_index(_SCREAMING_SNAKE_CASE					)

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

       def    __A			( self					)						->	Optional[int]:
           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
           A_      =  self._build_index(self.config					)

           ray.get(
               [
                   worker.create_rag_retriever.remote(
                       self.config       ,	self.question_encoder_tokenizer       ,	self.generator_tokenizer       ,	_SCREAMING_SNAKE_CASE					)
                   for worker in self.retrieval_workers
               ]					)






