






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      :       Optional[int]    =						logging.get_logger(__name__)

lowerCamelCase      :       Tuple    =						{
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}



class 				_UpperCamelCase      (__lowercase      ):
      snake_case_	=				'''data2vec-vision'''


      def __init__(						self       ,    __UpperCamelCase=7_6_8       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=1_2       ,    __UpperCamelCase=3_0_7_2       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0       ,    __UpperCamelCase=0.0_2       ,    __UpperCamelCase=1e-12       ,    __UpperCamelCase=2_2_4       ,    __UpperCamelCase=1_6       ,    __UpperCamelCase=3       ,    __UpperCamelCase=False       ,    __UpperCamelCase=False       ,    __UpperCamelCase=False       ,    __UpperCamelCase=False       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=True       ,    __UpperCamelCase=[3, 5, 7, 1_1]       ,    __UpperCamelCase=[1, 2, 3, 6]       ,    __UpperCamelCase=True       ,    __UpperCamelCase=0.4       ,    __UpperCamelCase=2_5_6       ,    __UpperCamelCase=1       ,    __UpperCamelCase=False       ,    __UpperCamelCase=2_5_5       ,    **__UpperCamelCase       ,    )->						int:
           super().__init__(**_lowerCAmelCase						)

           __lowerCAmelCase             =       hidden_size
           __lowerCAmelCase             =       num_hidden_layers
           __lowerCAmelCase             =       num_attention_heads
           __lowerCAmelCase             =       intermediate_size
           __lowerCAmelCase             =       hidden_act
           __lowerCAmelCase             =       hidden_dropout_prob
           __lowerCAmelCase             =       attention_probs_dropout_prob
           __lowerCAmelCase             =       initializer_range
           __lowerCAmelCase             =       layer_norm_eps

           __lowerCAmelCase             =       image_size
           __lowerCAmelCase             =       patch_size
           __lowerCAmelCase             =       num_channels
           __lowerCAmelCase             =       use_mask_token
           __lowerCAmelCase             =       use_absolute_position_embeddings
           __lowerCAmelCase             =       use_relative_position_bias
           __lowerCAmelCase             =       use_shared_relative_position_bias
           __lowerCAmelCase             =       layer_scale_init_value
           __lowerCAmelCase             =       drop_path_rate
           __lowerCAmelCase             =       use_mean_pooling
           # decode head attributes (semantic segmentation)
           __lowerCAmelCase             =       out_indices
           __lowerCAmelCase             =       pool_scales
           # auxiliary head attributes (semantic segmentation)
           __lowerCAmelCase             =       use_auxiliary_head
           __lowerCAmelCase             =       auxiliary_loss_weight
           __lowerCAmelCase             =       auxiliary_channels
           __lowerCAmelCase             =       auxiliary_num_convs
           __lowerCAmelCase             =       auxiliary_concat_input
           __lowerCAmelCase             =       semantic_loss_ignore_index



class 				_UpperCamelCase      (__lowercase      ):
      snake_case_	=				version.parse("""1.11"""      )


      @property
      def 			__UpperCAmelCase		(						self						)->						int:
           return OrderedDict(
               [
                   ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
               ]						)


      @property
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           return 1e-4













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_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							:       str					 =	logging.get_logger(__name__)





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ['''pixel_values''']






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

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size if size is not None else {'''shortest_edge''': 384}
										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   do_resize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size
										# Default value set here for backwards compatibility where the value in config is None
										__SCREAMING_SNAKE_CASE:				List[str]           =   crop_pct if crop_pct is not None else 224 / 256
										__SCREAMING_SNAKE_CASE:				List[Any]           =   resample
										__SCREAMING_SNAKE_CASE:				Any           =   do_rescale
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   rescale_factor
										__SCREAMING_SNAKE_CASE:				Any           =   do_normalize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_std if image_std is not None else IMAGENET_STANDARD_STD






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = PILImageResampling.BICUBIC			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)
										if "shortest_edge" not in size:
																raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""					)
										__SCREAMING_SNAKE_CASE:				str           =   size['''shortest_edge''']

										if shortest_edge < 384:
																# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																__SCREAMING_SNAKE_CASE:				Dict           =   int(shortest_edge / crop_pct					)
																__SCREAMING_SNAKE_CASE:				Any           =   get_resize_output_image_size(_lowerCAmelCase			, size=_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   resize(image=_lowerCAmelCase			, size=_lowerCAmelCase			, resample=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)
																# then crop to (shortest_edge, shortest_edge)
																return center_crop(image=_lowerCAmelCase			, size=(shortest_edge, shortest_edge)			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)
										else:
																# warping (no cropping) when evaluated at 384 or larger
																return resize(
																    _lowerCAmelCase			, size=(shortest_edge, shortest_edge)			, resample=_lowerCAmelCase			, data_format=_lowerCAmelCase			, **_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = ChannelDimension.FIRST			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   do_resize if do_resize is not None else self.do_resize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   crop_pct if crop_pct is not None else self.crop_pct
										__SCREAMING_SNAKE_CASE:				Tuple           =   resample if resample is not None else self.resample
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   do_rescale if do_rescale is not None else self.do_rescale
										__SCREAMING_SNAKE_CASE:				Any           =   rescale_factor if rescale_factor is not None else self.rescale_factor
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   do_normalize if do_normalize is not None else self.do_normalize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_mean if image_mean is not None else self.image_mean
										__SCREAMING_SNAKE_CASE:				str           =   image_std if image_std is not None else self.image_std

										__SCREAMING_SNAKE_CASE:				List[Any]           =   size if size is not None else self.size
										__SCREAMING_SNAKE_CASE:				Dict           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																raise ValueError('''crop_pct must be specified if size < 384.'''					)

										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.
										__SCREAMING_SNAKE_CASE:				Tuple           =   [to_numpy_array(_lowerCAmelCase					) for image in images]

										if do_resize:
																__SCREAMING_SNAKE_CASE:				str           =   [self.resize(image=_lowerCAmelCase			, size=_lowerCAmelCase			, crop_pct=_lowerCAmelCase			, resample=_lowerCAmelCase					) for image in images]

										if do_rescale:
																__SCREAMING_SNAKE_CASE:				Any           =   [self.rescale(image=_lowerCAmelCase			, scale=_lowerCAmelCase					) for image in images]

										if do_normalize:
																__SCREAMING_SNAKE_CASE:				int           =   [self.normalize(image=_lowerCAmelCase			, mean=_lowerCAmelCase			, std=_lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				str           =   [to_channel_dimension_format(_lowerCAmelCase			, _lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				List[str]           =   {'''pixel_values''': images}
										return BatchFeature(data=_lowerCAmelCase			, tensor_type=_lowerCAmelCase					)



