
"""simple docstring"""

import logging
import random

import ray

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


UpperCamelCase      :							Any          =					logging.getLogger(__name__)

class __lowerCAmelCase						:





     def __init__(      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			False





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self.retriever.index.init_index()





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      del self.retriever
      __UpperCamelCase							    =			False







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase    ,      __UpperCamelCase							    =			self.retriever._main_retrieve(__UpperCAmelCase      ,	__UpperCAmelCase )
      __UpperCamelCase							    =			self.retriever.index.get_doc_dicts(__UpperCAmelCase )
      return doc_ids, retrieved_doc_embeds, doc_dicts


class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):





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

      '''simple docstring'''




      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      ,	)

      __UpperCamelCase							    =			retrieval_workers
      __UpperCamelCase							    =			question_encoder_tokenizer
      __UpperCamelCase							    =			generator_tokenizer
      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 ):

      '''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      ,	__UpperCAmelCase      ,	__UpperCAmelCase ):

      '''simple docstring'''




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





     @classmethod
     def 		UpperCAmelCase   (      cls      ,	__UpperCAmelCase      ,	__UpperCAmelCase=None      ,	**__UpperCAmelCase ):

      '''simple docstring'''




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





     @classmethod
     def 		UpperCAmelCase   (      cls      ,	__UpperCAmelCase      ,	__UpperCAmelCase      ,	__UpperCAmelCase=None      ,	**__UpperCAmelCase ):

      '''simple docstring'''




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

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

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







     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
      __UpperCamelCase							    =			self._build_index(self.config )

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





"""simple docstring"""

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase      :							List[str]          =					logging.get_logger(__name__)

UpperCamelCase      :							Dict          =					{
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"oneformer"
     lowercase		  =		{"hidden_size": "hidden_dim"}





     def __init__(      self      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = 255      ,	__UpperCAmelCase = 150      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 0.5      ,	__UpperCAmelCase = 0.0_7      ,	__UpperCAmelCase = 1_2544      ,	__UpperCAmelCase = 3.0      ,	__UpperCAmelCase = 0.7_5      ,	__UpperCAmelCase = 0.0_2      ,	__UpperCAmelCase = 1.0      ,	__UpperCAmelCase = 1E-05      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = [4, 8, 16, 32]      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 4_9408      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 16      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 1024      ,	__UpperCAmelCase = "GN"      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 10      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = 8      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2048      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 4      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      if backbone_config is None:
       logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.' )
       __UpperCamelCase							    =			CONFIG_MAPPING['swin'](
           image_size=224      ,	in_channels=3      ,	patch_size=4      ,	embed_dim=96      ,	depths=[2, 2, 6, 2]      ,	num_heads=[3, 6, 12, 24]      ,	window_size=7      ,	drop_path_rate=0.3      ,	use_absolute_embeddings=__UpperCAmelCase      ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,	)
      elif isinstance(__UpperCAmelCase      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			backbone_config.get('model_type' )
       __UpperCamelCase							    =			CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase							    =			config_class.from_dict(__UpperCAmelCase )

      __UpperCamelCase							    =			backbone_config

      __UpperCamelCase							    =			ignore_value
      __UpperCamelCase							    =			num_queries
      __UpperCamelCase							    =			no_object_weight
      __UpperCamelCase							    =			class_weight
      __UpperCamelCase							    =			mask_weight
      __UpperCamelCase							    =			dice_weight
      __UpperCamelCase							    =			contrastive_weight
      __UpperCamelCase							    =			contrastive_temperature
      __UpperCamelCase							    =			train_num_points
      __UpperCamelCase							    =			oversample_ratio
      __UpperCamelCase							    =			importance_sample_ratio
      __UpperCamelCase							    =			init_std
      __UpperCamelCase							    =			init_xavier_std
      __UpperCamelCase							    =			layer_norm_eps
      __UpperCamelCase							    =			is_training
      __UpperCamelCase							    =			use_auxiliary_loss
      __UpperCamelCase							    =			output_auxiliary_logits
      __UpperCamelCase							    =			strides
      __UpperCamelCase							    =			task_seq_len
      __UpperCamelCase							    =			text_encoder_width
      __UpperCamelCase							    =			text_encoder_context_length
      __UpperCamelCase							    =			text_encoder_num_layers
      __UpperCamelCase							    =			text_encoder_vocab_size
      __UpperCamelCase							    =			text_encoder_proj_layers
      __UpperCamelCase							    =			text_encoder_n_ctx
      __UpperCamelCase							    =			conv_dim
      __UpperCamelCase							    =			mask_dim
      __UpperCamelCase							    =			hidden_dim
      __UpperCamelCase							    =			encoder_feedforward_dim
      __UpperCamelCase							    =			norm
      __UpperCamelCase							    =			encoder_layers
      __UpperCamelCase							    =			decoder_layers
      __UpperCamelCase							    =			use_task_norm
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			dropout
      __UpperCamelCase							    =			dim_feedforward
      __UpperCamelCase							    =			pre_norm
      __UpperCamelCase							    =			enforce_input_proj
      __UpperCamelCase							    =			query_dec_layers
      __UpperCamelCase							    =			common_stride
      __UpperCamelCase							    =			decoder_layers

      super().__init__(**__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			copy.deepcopy(self.__dict__ )
      __UpperCamelCase							    =			self.backbone_config.to_dict()
      __UpperCamelCase							    =			self.__class__.model_type
      return output



