



"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Union[str, Any]:
  a_     :      Union[str, Any] 						=     b.T
  a_     :      Dict 						=     np.sum(np.square(SCREAMING_SNAKE_CASE__	), axis=1	)
  a_     :      Optional[int] 						=     np.sum(np.square(SCREAMING_SNAKE_CASE__	), axis=0	)
  a_     :      Optional[int] 						=     np.matmul(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
  a_     :      Tuple 						=     aa[:, None] - 2 * ab + ba[None, :]
  return d



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
  a_     :      List[Any] 						=     x.reshape(-1, 3	)
  a_     :      List[Any] 						=     squared_euclidean_distance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
  return np.argmin(SCREAMING_SNAKE_CASE__, axis=1	)




class 			snake_case_						(						a_				):
       __lowerCAmelCase    					=					["pixel_values"]






       def __init__(   self			,     a_ = None			,     a_ = True			,     a_ = None			,     a_ = PILImageResampling.BILINEAR			,     a_ = True			,     a_ = True			,     **a_			,     ):
         super().__init__(**a_      )
         a_     :      Tuple 						=     size if size is not None else {"height": 2_5_6, "width": 2_5_6}
         a_     :      Any 						=     get_size_dict(a_      )
         a_     :      List[str] 						=     np.array(a_      ) if clusters is not None else None
         a_     :      List[Any] 						=     do_resize
         a_     :      int 						=     size
         a_     :      Optional[Any] 						=     resample
         a_     :      int 						=     do_normalize
         a_     :      Optional[Any] 						=     do_color_quantize






       def snake_case_  (   self			,     a_			,     a_			,     a_ = PILImageResampling.BILINEAR			,     a_ = None			,     **a_			,     ):
         a_     :      Union[str, Any] 						=     get_size_dict(a_      )
         if "height" not in size or "width" not in size:
           raise ValueError(F"""Size dictionary must contain both height and width keys. Got {size.keys()}"""      )
         return resize(
             a_			,     size=(size["height"], size["width"])			,     resample=a_			,     data_format=a_			,     **a_      )






       def snake_case_  (   self			,     a_			,     a_ = None			,     ):
         a_     :      List[Any] 						=     rescale(image=a_			,     scale=1 / 127.5			,     data_format=a_      )
         a_     :      Union[str, Any] 						=     image - 1
         return image






       def snake_case_  (   self			,     a_			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = None			,     a_ = ChannelDimension.FIRST			,     **a_			,     ):
         a_     :      List[Any] 						=     do_resize if do_resize is not None else self.do_resize
         a_     :      Optional[Any] 						=     size if size is not None else self.size
         a_     :      List[Any] 						=     get_size_dict(a_      )
         a_     :      Dict 						=     resample if resample is not None else self.resample
         a_     :      str 						=     do_normalize if do_normalize is not None else self.do_normalize
         a_     :      str 						=     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         a_     :      Tuple 						=     clusters if clusters is not None else self.clusters
         a_     :      Tuple 						=     np.array(a_      )

         a_     :      Optional[int] 						=     make_list_of_images(a_      )

         if not valid_images(a_      ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."      )

         if do_resize and size is None or resample is None:
           raise ValueError("Size and resample must be specified if do_resize is True."      )

         if do_color_quantize and clusters is None:
           raise ValueError("Clusters must be specified if do_color_quantize is True."      )

         # All transformations expect numpy arrays.
         a_     :      Dict 						=     [to_numpy_array(a_      ) for image in images]

         if do_resize:
           a_     :      Union[str, Any] 						=     [self.resize(image=a_			,     size=a_			,     resample=a_      ) for image in images]

         if do_normalize:
           a_     :      List[str] 						=     [self.normalize(image=a_      ) for image in images]

         if do_color_quantize:
           a_     :      str 						=     [to_channel_dimension_format(a_			,     ChannelDimension.LAST      ) for image in images]
           # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
           a_     :      Union[str, Any] 						=     np.array(a_      )
           a_     :      Union[str, Any] 						=     color_quantize(a_			,     a_      ).reshape(images.shape[:-1]      )

           # flatten to (batch_size, height*width)
           a_     :      List[str] 						=     images.shape[0]
           a_     :      Union[str, Any] 						=     images.reshape(a_			,     -1      )

           # We need to convert back to a list of images to keep consistent behaviour across processors.
           a_     :      Tuple 						=     list(a_      )
         else:
           a_     :      List[str] 						=     [to_channel_dimension_format(a_			,     a_      ) for image in images]

         a_     :      str 						=     {"input_ids": images}
         return BatchFeature(data=a_			,     tensor_type=a_      )




"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				= {
    """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 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					"realm"






							def __init__(   self			,     a_=3_0_5_2_2			,     a_=7_6_8			,     a_=1_2_8			,     a_=1_2			,     a_=1_2			,     a_=8			,     a_=3_0_7_2			,     a_="gelu_new"			,     a_=0.1			,     a_=0.1			,     a_=5_1_2			,     a_=2			,     a_=0.02			,     a_=1e-12			,     a_=2_5_6			,     a_=1_0			,     a_=1e-3			,     a_=5			,     a_=3_2_0			,     a_=1_3_3_5_3_7_1_8			,     a_=5_0_0_0			,     a_=1			,     a_=0			,     a_=2			,     **a_			,     ):
									super().__init__(pad_token_id=a_			,     bos_token_id=a_			,     eos_token_id=a_			,     **a_      )

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

									# Reader config
									a_     :      str 						=     span_hidden_size
									a_     :      Union[str, Any] 						=     max_span_width
									a_     :      Tuple 						=     reader_layer_norm_eps
									a_     :      List[Any] 						=     reader_beam_size
									a_     :      str 						=     reader_seq_len

									# Retrieval config
									a_     :      str 						=     num_block_records
									a_     :      int 						=     searcher_beam_size