


"""simple docstring"""




import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class    UpperCAmelCase__	( snake_case__		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  ["""image_processor""", """tokenizer"""]
    UpperCAmelCase__						:		List[Any]              =  """BlipImageProcessor"""
    UpperCAmelCase__						:		Optional[Any]              =  """AutoTokenizer"""






    def __init__(      self ,       A_ ,       A_ ,       A_   )  ->	Any:
         super().__init__(_A ,       _A   )

         # add QFormer tokenizer
         __UpperCamelCase   							=qformer_tokenizer






    def __call__(      self ,       A_ = None ,       A_ = None ,       A_ = True ,       A_ = False ,       A_ = None ,       A_ = None ,       A_ = 0 ,       A_ = None ,       A_ = None ,       A_ = False ,       A_ = False ,       A_ = False ,       A_ = False ,       A_ = False ,       A_ = True ,       A_ = None ,       **A_ ,       )  ->	Any:



         if images is None and text is None:
              raise ValueError('You have to specify at least images or text.'   )

         __UpperCamelCase   							=BatchFeature()

         if text is not None:
              __UpperCamelCase   							=self.tokenizer(
                  text=_A ,       add_special_tokens=_A ,       padding=_A ,       truncation=_A ,       max_length=_A ,       stride=_A ,       pad_to_multiple_of=_A ,       return_attention_mask=_A ,       return_overflowing_tokens=_A ,       return_special_tokens_mask=_A ,       return_offsets_mapping=_A ,       return_token_type_ids=_A ,       return_length=_A ,       verbose=_A ,       return_tensors=_A ,       **_A ,       )
              encoding.update(_A   )
              __UpperCamelCase   							=self.qformer_tokenizer(
                  text=_A ,       add_special_tokens=_A ,       padding=_A ,       truncation=_A ,       max_length=_A ,       stride=_A ,       pad_to_multiple_of=_A ,       return_attention_mask=_A ,       return_overflowing_tokens=_A ,       return_special_tokens_mask=_A ,       return_offsets_mapping=_A ,       return_token_type_ids=_A ,       return_length=_A ,       verbose=_A ,       return_tensors=_A ,       **_A ,       )
              __UpperCamelCase   							=qformer_text_encoding.pop('input_ids'   )
              __UpperCamelCase   							=qformer_text_encoding.pop('attention_mask'   )

         if images is not None:
              __UpperCamelCase   							=self.image_processor(_A ,       return_tensors=_A   )
              encoding.update(_A   )

         return encoding






    def _a (      self ,       *A_ ,       **A_   )  ->	List[Any]:
         return self.tokenizer.batch_decode(*_A ,       **_A   )






    def _a (      self ,       *A_ ,       **A_   )  ->	int:
         return self.tokenizer.decode(*_A ,       **_A   )






    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def _a (      self   )  ->	List[str]:
         __UpperCamelCase   							=self.tokenizer.model_input_names
         __UpperCamelCase   							=self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






    def _a (      self ,       A_ ,       **A_   )  ->	Union[str, Any]:



         if os.path.isfile(_A   ):
              raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'   )
         os.makedirs(_A ,       exist_ok=_A   )
         __UpperCamelCase   							=os.path.join(_A ,       'qformer_tokenizer'   )
         self.qformer_tokenizer.save_pretrained(_A   )
         return super().save_pretrained(_A ,       **_A   )






    @classmethod
    def _a (      cls ,       A_ ,       **A_   )  ->	Any:
         __UpperCamelCase   							=AutoTokenizer.from_pretrained(_A ,       subfolder='qformer_tokenizer'   )
         __UpperCamelCase   							=cls._get_arguments_from_pretrained(_A ,       **_A   )
         args.append(_A   )
         return cls(*_A   )









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


_A			=						logging.get_logger(__name__)

_A			=						{
    '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    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str              =  "realm"






    def __init__(      self ,       A_=30522 ,       A_=768 ,       A_=128 ,       A_=12 ,       A_=12 ,       A_=8 ,       A_=3072 ,       A_="gelu_new" ,       A_=0.1 ,       A_=0.1 ,       A_=512 ,       A_=2 ,       A_=0.02 ,       A_=1E-12 ,       A_=256 ,       A_=10 ,       A_=1E-3 ,       A_=5 ,       A_=320 ,       A_=13353718 ,       A_=5000 ,       A_=1 ,       A_=0 ,       A_=2 ,       **A_ ,       )  ->	Dict:
         super().__init__(pad_token_id=A_ ,       bos_token_id=A_ ,       eos_token_id=A_ ,       **A_   )

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

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

         # Retrieval config
         __UpperCamelCase   							=num_block_records
         __UpperCamelCase   							=searcher_beam_size





