


'''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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__      =							logging.get_logger(__name__)


if is_vision_available():
					import PIL
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							List[str]							= ['''pixel_values''']
			def __init__(     self       : List[str] ,						lowerCAmelCase_       : bool = True ,						lowerCAmelCase_       : Dict[str, int] = None ,						lowerCAmelCase_       : PILImageResampling = PILImageResampling.BICUBIC ,						lowerCAmelCase_       : bool = True ,						lowerCAmelCase_       : Dict[str, int] = None ,						lowerCAmelCase_       : bool = True ,						lowerCAmelCase_       : Union[int, float] = 1 / 255 ,						lowerCAmelCase_       : bool = True ,						lowerCAmelCase_       : Optional[Union[float, List[float]]] = None ,						lowerCAmelCase_       : Optional[Union[float, List[float]]] = None ,						lowerCAmelCase_       : bool = True ,						**lowerCAmelCase_       : List[str] ,						)			->		None:
						super().__init__(**lowerCAmelCase_  )
						__A=  size if size is not None else {'shortest_edge': 224}
						__A=  get_size_dict(lowerCAmelCase_ ,						default_to_square=lowerCAmelCase_  )
						__A=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
						__A=  get_size_dict(lowerCAmelCase_  )

						__A=  do_resize
						__A=  size
						__A=  resample
						__A=  do_center_crop
						__A=  crop_size
						__A=  do_rescale
						__A=  rescale_factor
						__A=  do_normalize
						__A=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						__A=  image_std if image_std is not None else OPENAI_CLIP_STD
						__A=  do_convert_rgb
			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : np.ndarray ,						lowerCAmelCase_       : Dict[str, int] ,						lowerCAmelCase_       : PILImageResampling = PILImageResampling.BICUBIC ,						lowerCAmelCase_       : Optional[Union[str, ChannelDimension]] = None ,						**lowerCAmelCase_       : Optional[int] ,						)			->		np.ndarray:
						__A=  get_size_dict(lowerCAmelCase_ ,						default_to_square=lowerCAmelCase_  )
						__A=  get_resize_output_image_size(
						    lowerCAmelCase_ ,						size=(size['height'], size['width']) ,						default_to_square=lowerCAmelCase_  )
						return resize(lowerCAmelCase_ ,						size=lowerCAmelCase_ ,						resample=lowerCAmelCase_ ,						data_format=lowerCAmelCase_ ,						**lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : np.ndarray ,						lowerCAmelCase_       : Dict[str, int] ,						lowerCAmelCase_       : Optional[Union[str, ChannelDimension]] = None ,						**lowerCAmelCase_       : Optional[int] ,						)			->		np.ndarray:
						__A=  get_size_dict(lowerCAmelCase_  )
						return center_crop(lowerCAmelCase_ ,						size=(size['height'], size['width']) ,						data_format=lowerCAmelCase_ ,						**lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : np.ndarray ,						lowerCAmelCase_       : Union[int, float] ,						lowerCAmelCase_       : Optional[Union[str, ChannelDimension]] = None ,						**lowerCAmelCase_       : Optional[Any] ,						)			->		Any:
						return rescale(lowerCAmelCase_ ,						scale=lowerCAmelCase_ ,						data_format=lowerCAmelCase_ ,						**lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : np.ndarray ,						lowerCAmelCase_       : Union[float, List[float]] ,						lowerCAmelCase_       : Union[float, List[float]] ,						lowerCAmelCase_       : Optional[Union[str, ChannelDimension]] = None ,						**lowerCAmelCase_       : List[Any] ,						)			->		np.ndarray:
						return normalize(lowerCAmelCase_ ,						mean=lowerCAmelCase_ ,						std=lowerCAmelCase_ ,						data_format=lowerCAmelCase_ ,						**lowerCAmelCase_  )

			def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : ImageInput ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : Dict[str, int] = None ,						lowerCAmelCase_       : PILImageResampling = None ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : int = None ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : float = None ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : Optional[Union[float, List[float]]] = None ,						lowerCAmelCase_       : Optional[Union[float, List[float]]] = None ,						lowerCAmelCase_       : bool = None ,						lowerCAmelCase_       : Optional[Union[str, TensorType]] = None ,						lowerCAmelCase_       : Optional[ChannelDimension] = ChannelDimension.FIRST ,						**lowerCAmelCase_       : Tuple ,						)			->		PIL.Image.Image:
						__A=  do_resize if do_resize is not None else self.do_resize
						__A=  size if size is not None else self.size
						__A=  get_size_dict(lowerCAmelCase_ ,						default_to_square=lowerCAmelCase_  )
						__A=  resample if resample is not None else self.resample
						__A=  do_center_crop if do_center_crop is not None else self.do_center_crop
						__A=  crop_size if crop_size is not None else self.crop_size
						__A=  get_size_dict(lowerCAmelCase_  )
						__A=  do_rescale if do_rescale is not None else self.do_rescale
						__A=  rescale_factor if rescale_factor is not None else self.rescale_factor
						__A=  do_normalize if do_normalize is not None else self.do_normalize
						__A=  image_mean if image_mean is not None else self.image_mean
						__A=  image_std if image_std is not None else self.image_std
						__A=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						__A=  make_list_of_images(lowerCAmelCase_  )

						if not valid_images(lowerCAmelCase_  ):
									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.'  )

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError('Image mean and std must be specified if do_normalize is True.'  )

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
									__A=  [convert_to_rgb(lowerCAmelCase_  ) for image in images]

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

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

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

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

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

						__A=  [to_channel_dimension_format(lowerCAmelCase_ ,						lowerCAmelCase_  ) for image in images]

						__A=  {'pixel_values': images}
						return BatchFeature(data=lowerCAmelCase_ ,						tensor_type=lowerCAmelCase_  )







'''simple docstring'''





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__      =							{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}
class 	a__				(				a_      ):





			'''simple docstring'''

			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			A	:							Optional[int]							= '''mobilebert'''
			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )

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

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

						__A=  classifier_dropout


class 	a__				(				a_      ):





			'''simple docstring'''

			@property
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=  {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						    ]  )



