



'''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


UpperCamelCase__:  List[Any]				      =     logging.get_logger(__name__)

UpperCamelCase__:  Optional[int]				      =     {
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}

class 							SCREAMING_SNAKE_CASE(				A__     ):







	"""simple docstring"""






	lowerCamelCase__     					=			"data2vec-vision"







	def __init__(		self     :	Dict						,     __snake_case     :	Any=768						,     __snake_case     :	Dict=12						,     __snake_case     :	Dict=12						,     __snake_case     :	List[Any]=3072						,     __snake_case     :	int="gelu"						,     __snake_case     :	Dict=0.0						,     __snake_case     :	List[str]=0.0						,     __snake_case     :	List[Any]=0.02						,     __snake_case     :	Optional[Any]=1E-12						,     __snake_case     :	List[Any]=224						,     __snake_case     :	Dict=16						,     __snake_case     :	Optional[Any]=3						,     __snake_case     :	int=False						,     __snake_case     :	str=False						,     __snake_case     :	Optional[int]=False						,     __snake_case     :	Dict=False						,     __snake_case     :	Optional[int]=0.1						,     __snake_case     :	Optional[Any]=0.1						,     __snake_case     :	Optional[Any]=True						,     __snake_case     :	Optional[Any]=[3, 5, 7, 11]						,     __snake_case     :	int=[1, 2, 3, 6]						,     __snake_case     :	Union[str, Any]=True						,     __snake_case     :	Tuple=0.4						,     __snake_case     :	str=256						,     __snake_case     :	int=1						,     __snake_case     :	int=False						,     __snake_case     :	List[Any]=255						,     **__snake_case     :	int						,     )     ->						Optional[int]:
								super().__init__(**__snake_case						)

								UpperCAmelCase					:			Any									=						hidden_size
								UpperCAmelCase					:			Optional[Any]									=						num_hidden_layers
								UpperCAmelCase					:			int									=						num_attention_heads
								UpperCAmelCase					:			Any									=						intermediate_size
								UpperCAmelCase					:			Tuple									=						hidden_act
								UpperCAmelCase					:			Tuple									=						hidden_dropout_prob
								UpperCAmelCase					:			str									=						attention_probs_dropout_prob
								UpperCAmelCase					:			Any									=						initializer_range
								UpperCAmelCase					:			Tuple									=						layer_norm_eps

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

class 							SCREAMING_SNAKE_CASE(				A__     ):







	"""simple docstring"""






	lowerCamelCase__     					=			version.parse("""1.11"""     )







	@property
	def 	A      (		self     :	Union[str, Any]						)     ->						List[str]:
								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]						)







	@property
	def 	A      (		self     :	Any						)     ->						str:
								return 1E-4









'''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 convert_to_rgb, 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


if is_vision_available():
							import PIL


__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Union[str, Any]   						=["pixel_values"]






						def __init__(  self							:      Optional[int] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[int, float] = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      bool = True ,  **lowerCAmelCase							:      List[Any] ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 3_84, "width": 3_84}
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													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 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)
													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()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






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






						def 				_UpperCAmelCase       (  self							:      Optional[int] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Union[float, List[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Dict ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Dict[str, int]] = None ,  lowerCAmelCase							:      PILImageResampling = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[float] = None ,  lowerCAmelCase							:      Optional[bool] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[float, List[float]]] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		resample if resample is not None else self.resample
													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_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase ,  default_to_square=lowerCAmelCase	)

													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 or resample is None:
																				raise ValueError("Size and resample 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."	)

													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_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_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													return encoded_outputs





