






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

lowerCAmelCase_		:			Optional[int]				   =					{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}





class      UpperCamelCase_       (				a_     ):
  _A				:    Optional[Any] =		MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  _A				:    Dict =		'mobilebert'



  def __init__(					self       ,						snake_case__=3_05_22       ,						snake_case__=5_12       ,						snake_case__=24       ,						snake_case__=4       ,						snake_case__=5_12       ,						snake_case__="relu"       ,						snake_case__=0.0       ,						snake_case__=0.1       ,						snake_case__=5_12       ,						snake_case__=2       ,						snake_case__=0.02       ,						snake_case__=1e-12       ,						snake_case__=0       ,						snake_case__=1_28       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=1_28       ,						snake_case__=False       ,						snake_case__=True       ,						snake_case__=4       ,						snake_case__="no_norm"       ,						snake_case__=True       ,						snake_case__=None       ,						**snake_case__       ,						)	->	List[str]:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__       ,						**snake_case__ )

         UpperCAmelCase					=	vocab_size
         UpperCAmelCase					=	hidden_size
         UpperCAmelCase					=	num_hidden_layers
         UpperCAmelCase					=	num_attention_heads
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	intermediate_size
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	attention_probs_dropout_prob
         UpperCAmelCase					=	max_position_embeddings
         UpperCAmelCase					=	type_vocab_size
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	layer_norm_eps
         UpperCAmelCase					=	embedding_size
         UpperCAmelCase					=	trigram_input
         UpperCAmelCase					=	use_bottleneck
         UpperCAmelCase					=	intra_bottleneck_size
         UpperCAmelCase					=	use_bottleneck_attention
         UpperCAmelCase					=	key_query_shared_bottleneck
         UpperCAmelCase					=	num_feedforward_networks
         UpperCAmelCase					=	normalization_type
         UpperCAmelCase					=	classifier_activation

         if self.use_bottleneck:
                UpperCAmelCase					=	intra_bottleneck_size
         else:
                UpperCAmelCase					=	hidden_size

         UpperCAmelCase					=	classifier_dropout





class      UpperCamelCase_       (				a_     ):



  @property
  def 						UpperCamelCase_ (					self )	->	Optional[int]:



         """simple docstring"""





         if self.task == "multiple-choice":
                UpperCAmelCase					=	{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
                UpperCAmelCase					=	{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
                 ("""token_type_ids""", dynamic_axis),
             ] )








"""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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCamelCase     			=  logging.get_logger(__name__)







class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			['''pixel_values''']






    def __init__( self	,A = True	,A = None	,A = PILImageResampling.BILINEAR	,A = True	,A = 1 / 255	,A = True	,A = None	,A = None	,**A	,):
          super().__init__(**A					)
          UpperCAmelCase							=       size if size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase							=       get_size_dict(A					)
          UpperCAmelCase							=       do_resize
          UpperCAmelCase							=       do_rescale
          UpperCAmelCase							=       do_normalize
          UpperCAmelCase							=       size
          UpperCAmelCase							=       resample
          UpperCAmelCase							=       rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCAmelCase							=       image_std if image_std is not None else IMAGENET_STANDARD_STD






    def 				_UpperCamelCase							( self	,A	,A	,A = PILImageResampling.BILINEAR	,A = None	,**A	,):
          UpperCAmelCase							=       get_size_dict(A					)
          if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''					)
          return resize(
              A	,size=(size["""height"""], size["""width"""])	,resample=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A = None	,**A					):
          return rescale(A	,scale=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,**A	,):
          return normalize(A	,mean=A	,std=A	,data_format=A	,**A					)






    def 				_UpperCamelCase							( self	,A	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = None	,A = ChannelDimension.FIRST	,**A	,):
          UpperCAmelCase							=       do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase							=       do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase							=       do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase							=       resample if resample is not None else self.resample
          UpperCAmelCase							=       rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase							=       image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase							=       image_std if image_std is not None else self.image_std

          UpperCAmelCase							=       size if size is not None else self.size
          UpperCAmelCase							=       get_size_dict(A					)

          UpperCAmelCase							=       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:
                raise ValueError("""Size must be specified if do_resize is True."""					)

          if do_rescale and rescale_factor is None:
                raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

          # All transformations expect numpy arrays.
          UpperCAmelCase							=       [to_numpy_array(A					) for image in images]

          if do_resize:
                UpperCAmelCase							=       [self.resize(image=A	,size=A	,resample=A					) for image in images]

          if do_rescale:
                UpperCAmelCase							=       [self.rescale(image=A	,scale=A					) for image in images]

          if do_normalize:
                UpperCAmelCase							=       [self.normalize(image=A	,mean=A	,std=A					) for image in images]

          UpperCAmelCase							=       [to_channel_dimension_format(A	,A					) for image in images]

          UpperCAmelCase							=       {"""pixel_values""": images}
          return BatchFeature(data=A	,tensor_type=A					)



