"""simple docstring"""







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_a					:					Dict=			logging.get_logger(__name__)

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Optional[Any]   =		["""pixel_values"""]



				def __init__(self    :				Optional[Any]     ,  _A    :				bool = True     ,  _A    :				Union[int, float] = 1 / 2_55     ,  _A    :				bool = True     ,  _A    :				int = 8     ,  **_A    :				Tuple     ,  )   ->     None:
					super().__init__(**_A)

					__snake_case      :							List[str]						=			do_rescale
					__snake_case      :							Dict						=			rescale_factor
					__snake_case      :							int						=			do_pad
					__snake_case      :							Dict						=			pad_size



				def  _lowercase					(self    :				int     ,  _A    :				np.ndarray     ,  _A    :				float     ,  _A    :				Optional[Union[str, ChannelDimension]] = None     ,  **_A    :				int)   ->     np.ndarray:
					return rescale(_A     ,  scale=_A     ,  data_format=_A     ,  **_A)



				def  _lowercase					(self    :				Tuple     ,  _A    :				np.ndarray     ,  _A    :				int     ,  _A    :				Optional[Union[str, ChannelDimension]] = None)   ->     Optional[Any]:
					__snake_case		,    __snake_case      :							int						=			get_image_size(_A)
					__snake_case      :							str						=			(old_height // size + 1) * size - old_height
					__snake_case      :							Any						=			(old_width // size + 1) * size - old_width

					return pad(_A     ,  ((0, pad_height), (0, pad_width))     ,  mode='symmetric'     ,  data_format=_A)






				def  _lowercase					(self    :				Dict     ,  _A    :				ImageInput     ,  _A    :				Optional[bool] = None     ,  _A    :				Optional[float] = None     ,  _A    :				Optional[bool] = None     ,  _A    :				Optional[int] = None     ,  _A    :				Optional[Union[str, TensorType]] = None     ,  _A    :				Union[str, ChannelDimension] = ChannelDimension.FIRST     ,  **_A    :				Any     ,  )   ->     List[Any]:
					__snake_case      :							List[str]						=			do_rescale if do_rescale is not None else self.do_rescale
					__snake_case      :							str						=			rescale_factor if rescale_factor is not None else self.rescale_factor
					__snake_case      :							int						=			do_pad if do_pad is not None else self.do_pad
					__snake_case      :							str						=			pad_size if pad_size is not None else self.pad_size

					__snake_case      :							List[Any]						=			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_rescale and rescale_factor is None:
						raise ValueError('Rescale factor must be specified if do_rescale is True.')

					# All transformations expect numpy arrays.
					__snake_case      :							Any						=			[to_numpy_array(_A) for image in images]

					if do_rescale:
						__snake_case      :							int						=			[self.rescale(image=_A     ,  scale=_A) for image in images]

					if do_pad:
						__snake_case      :							List[str]						=			[self.pad(_A     ,  size=_A) for image in images]

					__snake_case      :							int						=			[to_channel_dimension_format(_A     ,  _A) for image in images]

					__snake_case      :							Optional[Any]						=			{'pixel_values': images}
					return BatchFeature(data=_A     ,  tensor_type=_A)






"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_a					:					Tuple=			{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				str   =		"""albert"""



				def __init__(self    :				Union[str, Any]     ,  _A    :				int=3_00_00     ,  _A    :				Any=1_28     ,  _A    :				Tuple=40_96     ,  _A    :				int=12     ,  _A    :				Tuple=1     ,  _A    :				int=64     ,  _A    :				Optional[Any]=1_63_84     ,  _A    :				Optional[Any]=1     ,  _A    :				List[str]="gelu_new"     ,  _A    :				Any=0     ,  _A    :				Optional[Any]=0     ,  _A    :				List[Any]=5_12     ,  _A    :				List[Any]=2     ,  _A    :				Dict=0.02     ,  _A    :				Union[str, Any]=1E-12     ,  _A    :				Tuple=0.1     ,  _A    :				int="absolute"     ,  _A    :				List[Any]=0     ,  _A    :				str=2     ,  _A    :				int=3     ,  **_A    :				Tuple     ,  )   ->     Union[str, Any]:
					super().__init__(pad_token_id=_A     ,  bos_token_id=_A     ,  eos_token_id=_A     ,  **_A)

					__snake_case      :							str						=			vocab_size
					__snake_case      :							List[Any]						=			embedding_size
					__snake_case      :							Tuple						=			hidden_size
					__snake_case      :							Optional[Any]						=			num_hidden_layers
					__snake_case      :							List[str]						=			num_hidden_groups
					__snake_case      :							Dict						=			num_attention_heads
					__snake_case      :							Any						=			inner_group_num
					__snake_case      :							Union[str, Any]						=			hidden_act
					__snake_case      :							List[Any]						=			intermediate_size
					__snake_case      :							List[str]						=			hidden_dropout_prob
					__snake_case      :							int						=			attention_probs_dropout_prob
					__snake_case      :							Any						=			max_position_embeddings
					__snake_case      :							Optional[int]						=			type_vocab_size
					__snake_case      :							Optional[Any]						=			initializer_range
					__snake_case      :							List[Any]						=			layer_norm_eps
					__snake_case      :							Optional[Any]						=			classifier_dropout_prob
					__snake_case      :							List[str]						=			position_embedding_type







class 		UpperCamelCase      (   lowercase						):



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





