


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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase    		=       logging.get_logger(__name__)







class        lowerCamelCase__	(							UpperCAmelCase      ):






   """simple docstring"""






   _UpperCamelCase		:					Union[str, Any]    =		['pixel_values']




   def __init__(							self      ,						snake_case = True      ,						snake_case = None      ,						snake_case = PILImageResampling.BICUBIC      ,						snake_case = True      ,						snake_case = True      ,						snake_case = 1 / 255      ,						snake_case = None      ,						snake_case = True      ,						snake_case = None      ,						snake_case = None      ,						**snake_case      ,						):

      '''simple docstring'''


      super().__init__(**snake_case     )
      UpperCamelCase__					=       size if size is not None else {"height": 224, "width": 224}
      UpperCamelCase__					=       get_size_dict(snake_case     )
      UpperCamelCase__					=       crop_size if crop_size is not None else {"height": 224, "width": 224}
      UpperCamelCase__					=       get_size_dict(snake_case      ,						default_to_square=snake_case      ,						param_name="crop_size"     )

      UpperCamelCase__					=       do_resize
      UpperCamelCase__					=       do_rescale
      UpperCamelCase__					=       do_normalize
      UpperCamelCase__					=       do_center_crop
      UpperCamelCase__					=       crop_size
      UpperCamelCase__					=       size
      UpperCamelCase__					=       resample
      UpperCamelCase__					=       rescale_factor
      UpperCamelCase__					=       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      UpperCamelCase__					=       image_std if image_std is not None else IMAGENET_DEFAULT_STD




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = PILImageResampling.BILINEAR      ,						snake_case = None      ,						**snake_case      ,						):

      '''simple docstring'''


      UpperCamelCase__					=       get_size_dict(snake_case     )

      if "shortest_edge" in size:
         UpperCamelCase__					=       get_resize_output_image_size(snake_case      ,						size=size["shortest_edge"]      ,						default_to_square=snake_case     )
         # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
         UpperCamelCase__					=       (size["height"], size["width"])
      else:
         raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''     )
      return resize(snake_case      ,						size=snake_case      ,						resample=snake_case      ,						data_format=snake_case      ,						**snake_case     )




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case      ,						):

      '''simple docstring'''


      UpperCamelCase__					=       get_size_dict(snake_case     )
      if "height" not in size or "width" not in size:
         raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''     )
      return center_crop(snake_case      ,						size=(size["height"], size["width"])      ,						data_format=snake_case      ,						**snake_case     )




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case     ):

      '''simple docstring'''


      return rescale(snake_case      ,						scale=snake_case      ,						data_format=snake_case      ,						**snake_case     )




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case      ,						):

      '''simple docstring'''


      return normalize(snake_case      ,						mean=snake_case      ,						std=snake_case      ,						data_format=snake_case      ,						**snake_case     )







   def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = ChannelDimension.FIRST      ,						**snake_case      ,						):

      '''simple docstring'''


      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__					=       do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCamelCase__					=       crop_size if crop_size is not None else self.crop_size
      UpperCamelCase__					=       get_size_dict(snake_case      ,						param_name="crop_size"      ,						default_to_square=snake_case     )
      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(snake_case     )

      if not is_batched(snake_case     ):
         UpperCamelCase__					=       [images]

      if not valid_images(snake_case     ):
         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_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop 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(snake_case     ) for image in images]

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

      if do_center_crop:
         UpperCamelCase__					=       [self.center_crop(image=snake_case      ,						size=snake_case     ) for image in images]

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

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

      UpperCamelCase__					=       [to_channel_dimension_format(snake_case      ,						snake_case     ) for image in images]

      UpperCamelCase__					=       {"pixel_values": images}
      return BatchFeature(data=snake_case      ,						tensor_type=snake_case     )






from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Any    =		'ibert'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=False      ,						snake_case="none"      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case      ,						bos_token_id=snake_case      ,						eos_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__					=       position_embedding_type
						UpperCamelCase__					=       quant_mode
						UpperCamelCase__					=       force_dequant







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''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),
						    ]     )


