





"""simple docstring"""
import logging
import random

import ray

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


__UpperCamelCase			:		Optional[Any]		    =   logging.getLogger(__name__)







class 	a				:



 def __init__(			self			):







  """simple docstring"""






  lowerCAmelCase  							=      False



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self.retriever.index.init_index()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  del self.retriever
  lowerCAmelCase  							=      False



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase  							=      self.retriever._main_retrieve(_snake_case							,    _snake_case			)
  lowerCAmelCase  							=      self.retriever.index.get_doc_dicts(_snake_case			)
  return doc_ids, retrieved_doc_embeds, doc_dicts







class 	a				(					a__   ):



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







  """simple docstring"""






  if index is not None and index.is_initialized() and len(_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__(
      _snake_case							,    question_encoder_tokenizer=_snake_case							,    generator_tokenizer=_snake_case							,    index=_snake_case							,    init_retrieval=_snake_case							,    )

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



 def 					UpperCamelCase__   (			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 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






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



 @classmethod
 def 					UpperCamelCase__   (			cls							,    _snake_case							,    _snake_case=None							,    **_snake_case			):







  """simple docstring"""






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



 @classmethod
 def 					UpperCamelCase__   (			cls							,    _snake_case							,    _snake_case							,    _snake_case=None							,    **_snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      kwargs.pop('config'							,    _snake_case			) or RagConfig.from_pretrained(_snake_case							,    **_snake_case			)
  lowerCAmelCase  							=      RagTokenizer.from_pretrained(_snake_case							,    config=_snake_case			)
  lowerCAmelCase  							=      rag_tokenizer.question_encoder
  lowerCAmelCase  							=      rag_tokenizer.generator

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

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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  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
  lowerCAmelCase  							=      self._build_index(self.config			)

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












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


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

__UpperCamelCase			:		Union[str, Any]		    =   {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
       import detectrona







class 	a				(					a__   ):
 snake_case__    	=    '''layoutlmv2'''



 def __init__(			self							,    _snake_case=3_05_22							,    _snake_case=7_68							,    _snake_case=12							,    _snake_case=12							,    _snake_case=30_72							,    _snake_case="gelu"							,    _snake_case=0.1							,    _snake_case=0.1							,    _snake_case=5_12							,    _snake_case=2							,    _snake_case=0.02							,    _snake_case=1E-12							,    _snake_case=0							,    _snake_case=10_24							,    _snake_case=1_28							,    _snake_case=32							,    _snake_case=True							,    _snake_case=2_56							,    _snake_case=64							,    _snake_case=True							,    _snake_case=[7, 7, 2_56]							,    _snake_case=1_28							,    _snake_case=1_28							,    _snake_case=True							,    _snake_case=True							,    _snake_case=False							,    _snake_case=None							,    **_snake_case							,    ):







  """simple docstring"""






  super().__init__(
      vocab_size=_snake_case							,    hidden_size=_snake_case							,    num_hidden_layers=_snake_case							,    num_attention_heads=_snake_case							,    intermediate_size=_snake_case							,    hidden_act=_snake_case							,    hidden_dropout_prob=_snake_case							,    attention_probs_dropout_prob=_snake_case							,    max_position_embeddings=_snake_case							,    type_vocab_size=_snake_case							,    initializer_range=_snake_case							,    layer_norm_eps=_snake_case							,    pad_token_id=_snake_case							,    **_snake_case							,    )
  lowerCAmelCase  							=      max_ad_position_embeddings
  lowerCAmelCase  							=      max_rel_pos
  lowerCAmelCase  							=      rel_pos_bins
  lowerCAmelCase  							=      fast_qkv
  lowerCAmelCase  							=      max_rel_ad_pos
  lowerCAmelCase  							=      rel_ad_pos_bins
  lowerCAmelCase  							=      convert_sync_batchnorm
  lowerCAmelCase  							=      image_feature_pool_shape
  lowerCAmelCase  							=      coordinate_size
  lowerCAmelCase  							=      shape_size
  lowerCAmelCase  							=      has_relative_attention_bias
  lowerCAmelCase  							=      has_spatial_attention_bias
  lowerCAmelCase  							=      has_visual_segment_embedding
  lowerCAmelCase  							=      (
      detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  )



 @classmethod
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return {
      "MODEL.MASK_ON": True,
      "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
      "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
      "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
      "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
      "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
      "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
      "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
      "MODEL.POST_NMS_TOPK_TEST": 10_00,
      "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
      "MODEL.ROI_HEADS.NUM_CLASSES": 5,
      "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
      "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
      "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
      "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
      "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
      "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
      "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
      "MODEL.RESNETS.DEPTH": 1_01,
      "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
      "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
      "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.RESNETS.NUM_GROUPS": 32,
      "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
      "MODEL.RESNETS.STRIDE_IN_1X1": False,
  }



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      detectrona.config.get_cfg()
  for k, v in self.detectrona_config_args.items():
   lowerCAmelCase  							=      k.split('.'			)
   lowerCAmelCase  							=      detectrona_config
   for attribute in attributes[:-1]:
    lowerCAmelCase  							=      getattr(_snake_case							,    _snake_case			)
   setattr(_snake_case							,    attributes[-1]							,    _snake_case			)

  return detectrona_config





