"""simple docstring"""



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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "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    (_snake_case     ):







			'''simple docstring'''
			_UpperCamelCase  :						Tuple					     =				'realm'





			def __init__(			self   ,      snake_case_=30_522   ,      snake_case_=768   ,      snake_case_=128   ,      snake_case_=12   ,      snake_case_=12   ,      snake_case_=8   ,      snake_case_=3_072   ,      snake_case_="gelu_new"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=512   ,      snake_case_=2   ,      snake_case_=0.02   ,      snake_case_=1E-12   ,      snake_case_=256   ,      snake_case_=10   ,      snake_case_=1E-3   ,      snake_case_=5   ,      snake_case_=320   ,      snake_case_=13_353_718   ,      snake_case_=5_000   ,      snake_case_=1   ,      snake_case_=0   ,      snake_case_=2   ,      **snake_case_   ,      ):







										'''simple docstring'''


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

										# Common config
										A__       :     List[str]								=			vocab_size
										A__       :     Optional[Any]								=			max_position_embeddings
										A__       :     int								=			hidden_size
										A__       :     Any								=			retriever_proj_size
										A__       :     str								=			num_hidden_layers
										A__       :     Optional[Any]								=			num_attention_heads
										A__       :     Dict								=			num_candidates
										A__       :     int								=			intermediate_size
										A__       :     Optional[int]								=			hidden_act
										A__       :     Any								=			hidden_dropout_prob
										A__       :     Dict								=			attention_probs_dropout_prob
										A__       :     Optional[int]								=			initializer_range
										A__       :     str								=			type_vocab_size
										A__       :     Any								=			layer_norm_eps

										# Reader config
										A__       :     Union[str, Any]								=			span_hidden_size
										A__       :     Union[str, Any]								=			max_span_width
										A__       :     List[Any]								=			reader_layer_norm_eps
										A__       :     List[str]								=			reader_beam_size
										A__       :     Optional[int]								=			reader_seq_len

										# Retrieval config
										A__       :     Tuple								=			num_block_records
										A__       :     Any								=			searcher_beam_size









'''simple docstring'''





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["image_processor", "tokenizer"]
 UpperCAmelCase				=					"OwlViTImageProcessor"
 UpperCAmelCase				=					("CLIPTokenizer", "CLIPTokenizerFast")


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int]=None			,       __lowerCamelCase							:	Optional[Any]=None			,       **__lowerCamelCase							:	str ):
    UpperCAmelCase__	:int      =      None
    if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.'''			,       __lowerCamelCase			,       )
       UpperCAmelCase__	:Union[str, Any]      =      kwargs.pop('''feature_extractor''' )

    UpperCAmelCase__	:List[str]      =      image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.''' )
    if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.''' )

    super().__init__(__lowerCamelCase			,       __lowerCamelCase )


 def __call__(      self							:	int			,       __lowerCamelCase							:	str=None			,       __lowerCamelCase							:	List[Any]=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	str="max_length"			,       __lowerCamelCase							:	Any="np"			,       **__lowerCamelCase							:	Tuple ):

    if text is None and query_images is None and images is None:
       raise ValueError(
           '''You have to specify at least one text or query image or image. All three cannot be none.''' )

    if text is not None:
       if isinstance(__lowerCamelCase			,       __lowerCamelCase ) or (isinstance(__lowerCamelCase			,       __lowerCamelCase ) and not isinstance(text[0]			,       __lowerCamelCase )):
          UpperCAmelCase__	:Any      =      [self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )]

       elif isinstance(__lowerCamelCase			,       __lowerCamelCase ) and isinstance(text[0]			,       __lowerCamelCase ):
          UpperCAmelCase__	:Tuple      =      []

          # Maximum number of queries across batch
          UpperCAmelCase__	:List[str]      =      max([len(__lowerCamelCase ) for t in text] )

          # Pad all batch samples to max number of text queries
          for t in text:
             if len(__lowerCamelCase ) != max_num_queries:
                UpperCAmelCase__	:str      =      t + [''' '''] * (max_num_queries - len(__lowerCamelCase ))

             UpperCAmelCase__	:Tuple      =      self.tokenizer(__lowerCamelCase			,       padding=__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )
             encodings.append(__lowerCamelCase )
       else:
          raise TypeError('''Input text should be a string, a list of strings or a nested list of strings''' )

       if return_tensors == "np":
          UpperCAmelCase__	:List[Any]      =      np.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Any      =      np.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       elif return_tensors == "jax" and is_flax_available():
          import jax.numpy as jnp

          UpperCAmelCase__	:List[Any]      =      jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:Union[str, Any]      =      jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       elif return_tensors == "pt" and is_torch_available():
          import torch

          UpperCAmelCase__	:Any      =      torch.cat([encoding['''input_ids'''] for encoding in encodings]			,       dim=0 )
          UpperCAmelCase__	:List[str]      =      torch.cat([encoding['''attention_mask'''] for encoding in encodings]			,       dim=0 )

       elif return_tensors == "tf" and is_tf_available():
          import tensorflow as tf

          UpperCAmelCase__	:Optional[Any]      =      tf.stack([encoding['''input_ids'''] for encoding in encodings]			,       axis=0 )
          UpperCAmelCase__	:int      =      tf.stack([encoding['''attention_mask'''] for encoding in encodings]			,       axis=0 )

       else:
          raise ValueError('''Target return tensor type could not be returned''' )

       UpperCAmelCase__	:List[Any]      =      BatchEncoding()
       UpperCAmelCase__	:Union[str, Any]      =      input_ids
       UpperCAmelCase__	:Dict      =      attention_mask

    if query_images is not None:
       UpperCAmelCase__	:Tuple      =      BatchEncoding()
       UpperCAmelCase__	:int      =      self.image_processor(
           __lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase ).pixel_values
       UpperCAmelCase__	:Optional[int]      =      query_pixel_values

    if images is not None:
       UpperCAmelCase__	:str      =      self.image_processor(__lowerCamelCase			,       return_tensors=__lowerCamelCase			,       **__lowerCamelCase )

    if text is not None and images is not None:
       UpperCAmelCase__	:Dict      =      image_features.pixel_values
       return encoding
    elif query_images is not None and images is not None:
       UpperCAmelCase__	:Tuple      =      image_features.pixel_values
       return encoding
    elif text is not None or query_images is not None:
       return encoding
    else:
       return BatchEncoding(data=dict(**__lowerCamelCase )			,       tensor_type=__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[int]			,       **__lowerCamelCase							:	int ):
    return self.image_processor.post_process(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       *__lowerCamelCase							:	Any			,       **__lowerCamelCase							:	Tuple ):
    return self.image_processor.post_process_object_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any]			,       *__lowerCamelCase							:	Tuple			,       **__lowerCamelCase							:	str ):
    return self.image_processor.post_process_image_guided_detection(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       *__lowerCamelCase							:	Optional[Any]			,       **__lowerCamelCase							:	Union[str, Any] ):
    return self.tokenizer.batch_decode(*__lowerCamelCase			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       *__lowerCamelCase							:	Union[str, Any]			,       **__lowerCamelCase							:	Dict ):
    return self.tokenizer.decode(*__lowerCamelCase			,       **__lowerCamelCase )


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor_class





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,       __lowerCamelCase			,       )
    return self.image_processor



