

'''simple docstring'''

import copy
import os
from typing import Union

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


lowercase__			    =			logging.get_logger(__name__)

lowercase__			    =			{
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}




class        A_ (				_snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:	List[Any]  		=  'git_vision_model'


     def __init__(		self :     List[Any]     ,			lowercase_ :     str=768     ,			lowercase_ :     Tuple=3_072     ,			lowercase_ :     Dict=12     ,			lowercase_ :     Any=12     ,			lowercase_ :     Dict=3     ,			lowercase_ :     Tuple=224     ,			lowercase_ :     List[Any]=16     ,			lowercase_ :     int="quick_gelu"     ,			lowercase_ :     List[Any]=1E-5     ,			lowercase_ :     List[str]=0.0     ,			lowercase_ :     List[Any]=0.02     ,			**lowercase_ :     Tuple     ,			)						->	Optional[Any]:
      super().__init__(**__lowerCAmelCase       )

      UpperCAmelCase       :	List[str]											= hidden_size
      UpperCAmelCase       :	Optional[int]											= intermediate_size
      UpperCAmelCase       :	int											= num_hidden_layers
      UpperCAmelCase       :	int											= num_attention_heads
      UpperCAmelCase       :	Any											= num_channels
      UpperCAmelCase       :	str											= patch_size
      UpperCAmelCase       :	Dict											= image_size
      UpperCAmelCase       :	Tuple											= initializer_range
      UpperCAmelCase       :	Dict											= attention_dropout
      UpperCAmelCase       :	Tuple											= layer_norm_eps
      UpperCAmelCase       :	Any											= hidden_act




     @classmethod
     def    UpperCAmelCase_       (		cls :     Optional[int]     ,			lowercase_ :     List[str]     ,			**lowercase_ :     Optional[Any]       )						->	Tuple:
      cls._set_token_in_kwargs(__lowerCAmelCase       )

      UpperCAmelCase					, UpperCAmelCase       :	int											= cls.get_config_dict(__lowerCAmelCase     ,			**__lowerCAmelCase       )

      # get the vision config dict if we are loading from GITConfig
      if config_dict.get('model_type'       ) == "git":
       UpperCAmelCase       :	List[str]											= config_dict['vision_config']

      if "model_type" in config_dict and hasattr(cls     ,			'model_type'       ) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
           f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

      return cls.from_dict(__lowerCAmelCase     ,			**__lowerCAmelCase       )




class        A_ (				_snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:	List[str]  		=  'git'


     def __init__(		self :     str     ,			lowercase_ :     Union[str, Any]=None     ,			lowercase_ :     str=30_522     ,			lowercase_ :     Optional[int]=768     ,			lowercase_ :     Optional[Any]=6     ,			lowercase_ :     Union[str, Any]=12     ,			lowercase_ :     List[Any]=3_072     ,			lowercase_ :     str="gelu"     ,			lowercase_ :     int=0.1     ,			lowercase_ :     int=0.1     ,			lowercase_ :     Any=1_024     ,			lowercase_ :     List[str]=0.02     ,			lowercase_ :     Optional[int]=1E-12     ,			lowercase_ :     Optional[Any]=0     ,			lowercase_ :     Tuple="absolute"     ,			lowercase_ :     Tuple=True     ,			lowercase_ :     str=False     ,			lowercase_ :     Union[str, Any]=101     ,			lowercase_ :     Union[str, Any]=102     ,			lowercase_ :     Tuple=None     ,			**lowercase_ :     int     ,			)						->	Dict:
      super().__init__(bos_token_id=__lowerCAmelCase     ,			eos_token_id=__lowerCAmelCase     ,			pad_token_id=__lowerCAmelCase     ,			**__lowerCAmelCase       )

      if vision_config is None:
       UpperCAmelCase       :	Optional[Any]											= {}
       logger.info('vision_config is None. initializing the GitVisionConfig with default values.'       )

      UpperCAmelCase       :	List[str]											= GitVisionConfig(**__lowerCAmelCase       )
      UpperCAmelCase       :	int											= vocab_size
      UpperCAmelCase       :	List[str]											= hidden_size
      UpperCAmelCase       :	Any											= num_hidden_layers
      UpperCAmelCase       :	Union[str, Any]											= num_attention_heads
      UpperCAmelCase       :	Optional[int]											= hidden_act
      UpperCAmelCase       :	Tuple											= intermediate_size
      UpperCAmelCase       :	Dict											= hidden_dropout_prob
      UpperCAmelCase       :	Optional[Any]											= attention_probs_dropout_prob
      UpperCAmelCase       :	List[Any]											= max_position_embeddings
      UpperCAmelCase       :	int											= initializer_range
      UpperCAmelCase       :	List[Any]											= layer_norm_eps
      UpperCAmelCase       :	List[Any]											= position_embedding_type
      UpperCAmelCase       :	List[Any]											= use_cache
      UpperCAmelCase       :	str											= tie_word_embeddings
      UpperCAmelCase       :	List[str]											= num_image_with_embedding

      UpperCAmelCase       :	Optional[int]											= bos_token_id
      UpperCAmelCase       :	Tuple											= eos_token_id




     def    UpperCAmelCase_       (		self :     Any       )						->	Optional[Any]:
      UpperCAmelCase       :	int											= copy.deepcopy(self.__dict__       )
      UpperCAmelCase       :	str											= self.vision_config.to_dict()
      UpperCAmelCase       :	List[Any]											= self.__class__.model_type
      return output





"""simple docstring"""






import logging
import random

import ray

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


__lowerCAmelCase    :       int					     =logging.getLogger(__name__)





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	False

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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

      def 				A__       (						self						):







            """simple docstring"""



            self.retriever.index.init_index()







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.retriever._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return doc_ids, retrieved_doc_embeds






class  _A						(       lowerCAmelCase	):

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







            """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		,       )
            lowercase														=	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 				A__       (						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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



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

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



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







      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	kwargs.pop("""config"""		,       __lowerCAmelCase						) or RagConfig.from_pretrained(__lowerCAmelCase		,       **__lowerCAmelCase						)
            lowercase														=	RagTokenizer.from_pretrained(__lowerCAmelCase		,       config=__lowerCAmelCase						)
            lowercase														=	rag_tokenizer.question_encoder
            lowercase														=	rag_tokenizer.generator
            if indexed_dataset is not None:
                  lowercase														=	"""custom"""
                  lowercase														=	CustomHFIndex(config.retrieval_vector_size		,       __lowerCAmelCase						)
            else:
                  lowercase														=	cls._build_index(__lowerCAmelCase						)
            return cls(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       retrieval_workers=__lowerCAmelCase		,       index=__lowerCAmelCase		,       )



