






from ...processing_utils import ProcessorMixin


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					["image_processor", "feature_extractor"]
     SCREAMING_SNAKE_CASE     =					"TvltImageProcessor"
     SCREAMING_SNAKE_CASE     =					"TvltFeatureExtractor"




     def __init__(					self							:      Any			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         super().__init__(image_processor=UpperCamelCase_			,						feature_extractor=UpperCamelCase_					)

         __A       =						image_processor
         __A       =						feature_extractor




     def __call__(					self							:      Dict			,						UpperCamelCase_							:      List[Any]=None			,						UpperCamelCase_							:      Tuple=None			,						UpperCamelCase_							:      Optional[int]=None			,						UpperCamelCase_							:      Dict=None			,						UpperCamelCase_							:      List[str]=False			,						UpperCamelCase_							:      List[str]=False			,						*UpperCamelCase_							:      List[str]			,						**UpperCamelCase_							:      Any			,						):


         """simple docstring"""



         if images is None and audio is None:
             raise ValueError("""You need to specify either an `images` or `audio` input to process."""					)

         __A       =						None
         if images is not None:
             __A       =						self.image_processor(UpperCamelCase_			,						mask_pixel=UpperCamelCase_			,						*UpperCamelCase_			,						**UpperCamelCase_					)
         if images_mixed is not None:
             __A       =						self.image_processor(UpperCamelCase_			,						is_mixed=UpperCamelCase_			,						*UpperCamelCase_			,						**UpperCamelCase_					)
         if audio is not None:
             __A       =						self.feature_extractor(
                 UpperCamelCase_			,						*UpperCamelCase_			,						sampling_rate=UpperCamelCase_			,						mask_audio=UpperCamelCase_			,						**UpperCamelCase_					)

         __A       =						{}
         if audio is not None:
             output_dict.update(UpperCamelCase_					)
         if images is not None:
             output_dict.update(UpperCamelCase_					)
         if images_mixed_dict is not None:
             output_dict.update(UpperCamelCase_					)
         return output_dict





     @property
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						self.image_processor.model_input_names
         __A       =						self.feature_extractor.model_input_names
         return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names					)					)












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


__a   :							Optional[Any]						= logging.get_logger(__name__)

__a   :							Dict						= {
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"realm"




     def __init__(					self							:      str			,						UpperCamelCase_							:      List[Any]=30_522			,						UpperCamelCase_							:      Dict=768			,						UpperCamelCase_							:      Union[str, Any]=128			,						UpperCamelCase_							:      Tuple=12			,						UpperCamelCase_							:      Optional[Any]=12			,						UpperCamelCase_							:      Optional[int]=8			,						UpperCamelCase_							:      str=3_072			,						UpperCamelCase_							:      List[str]="gelu_new"			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      Optional[int]=0.1			,						UpperCamelCase_							:      str=512			,						UpperCamelCase_							:      int=2			,						UpperCamelCase_							:      str=0.02			,						UpperCamelCase_							:      List[Any]=1e-12			,						UpperCamelCase_							:      str=256			,						UpperCamelCase_							:      List[str]=10			,						UpperCamelCase_							:      int=1e-3			,						UpperCamelCase_							:      List[str]=5			,						UpperCamelCase_							:      Tuple=320			,						UpperCamelCase_							:      List[str]=13_353_718			,						UpperCamelCase_							:      Tuple=5_000			,						UpperCamelCase_							:      Optional[int]=1			,						UpperCamelCase_							:      List[str]=0			,						UpperCamelCase_							:      Union[str, Any]=2			,						**UpperCamelCase_							:      List[Any]			,						):


         """simple docstring"""



         super().__init__(pad_token_id=UpperCamelCase_			,						bos_token_id=UpperCamelCase_			,						eos_token_id=UpperCamelCase_			,						**UpperCamelCase_					)

         # Common config
         __A       =						vocab_size
         __A       =						max_position_embeddings
         __A       =						hidden_size
         __A       =						retriever_proj_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						num_candidates
         __A       =						intermediate_size
         __A       =						hidden_act
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						initializer_range
         __A       =						type_vocab_size
         __A       =						layer_norm_eps

         # Reader config
         __A       =						span_hidden_size
         __A       =						max_span_width
         __A       =						reader_layer_norm_eps
         __A       =						reader_beam_size
         __A       =						reader_seq_len

         # Retrieval config
         __A       =						num_block_records
         __A       =						searcher_beam_size




