
"""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, 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, is_vision_available, logging


if is_vision_available():
							import PIL


lowerCamelCase__           =	logging.get_logger(__name__)




class 				A__ (			a__):
		A_		: int            =			['pixel_values']

		def __init__(   self ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 1 / 2_55 ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_A				)
			__lowerCAmelCase    :		int		    =   size if size is not None else {'height': 2_56, 'width': 2_56}
			__lowerCAmelCase    :		List[Any]		    =   get_size_dict(_A				)
			__lowerCAmelCase    :		Optional[Any]		    =   crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
			__lowerCAmelCase    :		int		    =   get_size_dict(_A ,	param_name='crop_size'				)

			__lowerCAmelCase    :		int		    =   do_resize
			__lowerCAmelCase    :		int		    =   size
			__lowerCAmelCase    :		str		    =   resample
			__lowerCAmelCase    :		Dict		    =   do_center_crop
			__lowerCAmelCase    :		Dict		    =   crop_size
			__lowerCAmelCase    :		Any		    =   do_rescale
			__lowerCAmelCase    :		int		    =   rescale_factor
			__lowerCAmelCase    :		int		    =   do_normalize
			__lowerCAmelCase    :		int		    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__lowerCAmelCase    :		str		    =   image_std if image_std is not None else IMAGENET_STANDARD_STD

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		Optional[Any]		    =   get_size_dict(_A				)
			if "height" not in size or "width" not in size:
				raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
			return resize(
			    _A ,	size=(size['height'], size['width']) ,	resample=_A ,	data_format=_A ,	**_A				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		Union[str, Any]		    =   get_size_dict(_A				)
			if "height" not in size or "width" not in size:
				raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
			return center_crop(_A ,	size=(size['height'], size['width']) ,	data_format=_A ,	**_A				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			return rescale(_A ,	scale=_A ,	data_format=_A ,	**_A				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			return normalize(_A ,	mean=_A ,	std=_A ,	data_format=_A ,	**_A				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,	**_SCREAMING_SNAKE_CASE ,	):
			__lowerCAmelCase    :		Any		    =   do_resize if do_resize is not None else self.do_resize
			__lowerCAmelCase    :		str		    =   resample if resample is not None else self.resample
			__lowerCAmelCase    :		List[Any]		    =   do_center_crop if do_center_crop is not None else self.do_center_crop
			__lowerCAmelCase    :		Any		    =   do_rescale if do_rescale is not None else self.do_rescale
			__lowerCAmelCase    :		Dict		    =   rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowerCAmelCase    :		Optional[Any]		    =   do_normalize if do_normalize is not None else self.do_normalize
			__lowerCAmelCase    :		Any		    =   image_mean if image_mean is not None else self.image_mean
			__lowerCAmelCase    :		List[str]		    =   image_std if image_std is not None else self.image_std

			__lowerCAmelCase    :		List[str]		    =   size if size is not None else self.size
			__lowerCAmelCase    :		List[str]		    =   get_size_dict(_A				)
			__lowerCAmelCase    :		int		    =   crop_size if crop_size is not None else self.crop_size
			__lowerCAmelCase    :		str		    =   get_size_dict(_A ,	param_name='crop_size'				)

			__lowerCAmelCase    :		Dict		    =   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_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.'				)

			# All transformations expect numpy arrays.
			__lowerCAmelCase    :		Optional[int]		    =   [to_numpy_array(_A				) for image in images]

			if do_resize:
				__lowerCAmelCase    :		int		    =   [self.resize(image=_A ,	size=_A ,	resample=_A				) for image in images]

			if do_center_crop:
				__lowerCAmelCase    :		List[Any]		    =   [self.center_crop(image=_A ,	size=_A				) for image in images]

			if do_rescale:
				__lowerCAmelCase    :		Optional[Any]		    =   [self.rescale(image=_A ,	scale=_A				) for image in images]

			if do_normalize:
				__lowerCAmelCase    :		Union[str, Any]		    =   [self.normalize(image=_A ,	mean=_A ,	std=_A				) for image in images]

			__lowerCAmelCase    :		Optional[Any]		    =   [to_channel_dimension_format(_A ,	_A				) for image in images]

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

"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			'beit'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=81_92 ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=2_24 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=[3, 5, 7, 11] ,	_SCREAMING_SNAKE_CASE=[1, 2, 3, 6] ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.4 ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=2_55 ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Optional[int]		    =   vocab_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
			__lowerCAmelCase    :		Dict		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   num_attention_heads
			__lowerCAmelCase    :		Union[str, Any]		    =   intermediate_size
			__lowerCAmelCase    :		Union[str, Any]		    =   hidden_act
			__lowerCAmelCase    :		List[Any]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Optional[Any]		    =   initializer_range
			__lowerCAmelCase    :		Optional[Any]		    =   layer_norm_eps

			__lowerCAmelCase    :		Any		    =   image_size
			__lowerCAmelCase    :		Union[str, Any]		    =   patch_size
			__lowerCAmelCase    :		List[Any]		    =   num_channels
			__lowerCAmelCase    :		List[Any]		    =   use_mask_token
			__lowerCAmelCase    :		Any		    =   use_absolute_position_embeddings
			__lowerCAmelCase    :		int		    =   use_relative_position_bias
			__lowerCAmelCase    :		List[str]		    =   use_shared_relative_position_bias
			__lowerCAmelCase    :		Tuple		    =   layer_scale_init_value
			__lowerCAmelCase    :		int		    =   drop_path_rate
			__lowerCAmelCase    :		Dict		    =   use_mean_pooling
			# decode head attributes (semantic segmentation)
			__lowerCAmelCase    :		Optional[Any]		    =   out_indices
			__lowerCAmelCase    :		Optional[Any]		    =   pool_scales
			# auxiliary head attributes (semantic segmentation)
			__lowerCAmelCase    :		Union[str, Any]		    =   use_auxiliary_head
			__lowerCAmelCase    :		Optional[int]		    =   auxiliary_loss_weight
			__lowerCAmelCase    :		Optional[Any]		    =   auxiliary_channels
			__lowerCAmelCase    :		List[str]		    =   auxiliary_num_convs
			__lowerCAmelCase    :		List[str]		    =   auxiliary_concat_input
			__lowerCAmelCase    :		Optional[int]		    =   semantic_loss_ignore_index




class 				A__ (			_lowerCamelCase):
		A_		: List[str]            =			version.parse('1.11')

		@property
		def 							__lowerCamelCase							(   self				):
			return OrderedDict(
			    [
			        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
			    ]				)







		@property
		def 							__lowerCamelCase							(   self				):
			return 1E-4