"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


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

UpperCamelCase_							:							int				 =		{
    """layoutlmv2-base-uncased""": """https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json""",
    """layoutlmv2-large-uncased""": """https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json""",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
						import detectrona


class       _lowercase		( lowercase_	):
				lowerCamelCase__							=							'layoutlmv2'

				def __init__(  self							:     Optional[Any]							,     _lowercase							:     List[Any]=3_0_5_2_2							,     _lowercase							:     str=7_6_8							,     _lowercase							:     Optional[Any]=1_2							,     _lowercase							:     List[Any]=1_2							,     _lowercase							:     Tuple=3_0_7_2							,     _lowercase							:     Optional[Any]="gelu"							,     _lowercase							:     List[Any]=0.1							,     _lowercase							:     Tuple=0.1							,     _lowercase							:     str=5_1_2							,     _lowercase							:     Any=2							,     _lowercase							:     List[str]=0.02							,     _lowercase							:     List[Any]=1e-12							,     _lowercase							:     str=0							,     _lowercase							:     Tuple=1_0_2_4							,     _lowercase							:     Dict=1_2_8							,     _lowercase							:     Any=3_2							,     _lowercase							:     Optional[Any]=True							,     _lowercase							:     List[Any]=2_5_6							,     _lowercase							:     List[str]=6_4							,     _lowercase							:     int=True							,     _lowercase							:     int=[7, 7, 2_5_6]							,     _lowercase							:     List[str]=1_2_8							,     _lowercase							:     Dict=1_2_8							,     _lowercase							:     Tuple=True							,     _lowercase							:     Optional[Any]=True							,     _lowercase							:     Union[str, Any]=False							,     _lowercase							:     Optional[Any]=None							,     **_lowercase							:     Optional[Any]							,     ):


										'''simple docstring'''
										super().__init__(
										    vocab_size=a__							,     hidden_size=a__							,     num_hidden_layers=a__							,     num_attention_heads=a__							,     intermediate_size=a__							,     hidden_act=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__							,     pad_token_id=a__							,     **a__							,     )
										UpperCAmelCase__       						=		max_ad_position_embeddings
										UpperCAmelCase__       						=		max_rel_pos
										UpperCAmelCase__       						=		rel_pos_bins
										UpperCAmelCase__       						=		fast_qkv
										UpperCAmelCase__       						=		max_rel_ad_pos
										UpperCAmelCase__       						=		rel_ad_pos_bins
										UpperCAmelCase__       						=		convert_sync_batchnorm
										UpperCAmelCase__       						=		image_feature_pool_shape
										UpperCAmelCase__       						=		coordinate_size
										UpperCAmelCase__       						=		shape_size
										UpperCAmelCase__       						=		has_relative_attention_bias
										UpperCAmelCase__       						=		has_spatial_attention_bias
										UpperCAmelCase__       						=		has_visual_segment_embedding
										UpperCAmelCase__       						=		(
										    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
										)

				@classmethod
				def 							A			(  self							:     List[Any]  ):


										'''simple docstring'''
										return {
										    "MODEL.MASK_ON": True,
										    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
										    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
										    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.ANCHOR_GENERATOR.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
										    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
										    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
										    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
										    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
										    "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
										    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
										    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
										    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
										    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
										    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
										    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 1_4,
										    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
										    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
										    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
										    "MODEL.RESNETS.DEPTH": 1_0_1,
										    "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
										    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
										    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.RESNETS.NUM_GROUPS": 3_2,
										    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
										    "MODEL.RESNETS.STRIDE_IN_1X1": False,
										}

				def 							A			(  self							:     Dict  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		detectrona.config.get_cfg()
										for k, v in self.detectrona_config_args.items():
																UpperCAmelCase__       						=		k.split("""."""  )
																UpperCAmelCase__       						=		detectrona_config
																for attribute in attributes[:-1]:
																						UpperCAmelCase__       						=		getattr(a__							,     a__  )
																setattr(a__							,     attributes[-1]							,     a__  )

										return detectrona_config



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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_UpperCAmelCase    			=					logging.get_logger(__name__)


if is_vision_available():
  import PIL

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["pixel_values"]


  def __init__(					self	,		a__ = True	,		a__ = None	,		a__ = PILImageResampling.BILINEAR	,		a__ = True	,		a__ = False	,		a__ = True	,		a__ = True	,		a__ = 1 / 2_55	,		a__ = True	,		a__ = None	,		a__ = None	,		**a__	,		):
    super().__init__(**a__		)

    _lowerCamelCase      =						size if size is not None else {'''height''': 25_60, '''width''': 19_20}
    if isinstance(a__	,		(tuple, list)		):
      # The previous feature extractor size parameter was in (width, height) format
      _lowerCamelCase      =						size[::-1]
    _lowerCamelCase      =						get_size_dict(a__		)

    _lowerCamelCase      =						do_resize
    _lowerCamelCase      =						size
    _lowerCamelCase      =						resample
    _lowerCamelCase      =						do_thumbnail
    _lowerCamelCase      =						do_align_long_axis
    _lowerCamelCase      =						do_pad
    _lowerCamelCase      =						do_rescale
    _lowerCamelCase      =						rescale_factor
    _lowerCamelCase      =						do_normalize
    _lowerCamelCase      =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    _lowerCamelCase      =						image_std if image_std is not None else IMAGENET_STANDARD_STD


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = None		):
    _lowerCamelCase    ,    _lowerCamelCase      =						get_image_size(a__		)
    _lowerCamelCase    ,    _lowerCamelCase      =						size['''height'''], size['''width''']

    if (output_width < output_height and input_width > input_height) or (
        output_width > output_height and input_width < input_height
    ):
      _lowerCamelCase      =						np.rotaa(a__	,		3		)

    if data_format is not None:
      _lowerCamelCase      =						to_channel_dimension_format(a__	,		a__		)

    return image


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = False	,		a__ = None	,		):
    _lowerCamelCase    ,    _lowerCamelCase      =						size['''height'''], size['''width''']
    _lowerCamelCase    ,    _lowerCamelCase      =						get_image_size(a__		)

    _lowerCamelCase      =						output_width - input_width
    _lowerCamelCase      =						output_height - input_height

    if random_padding:
      _lowerCamelCase      =						np.random.randint(low=0	,		high=delta_height + 1		)
      _lowerCamelCase      =						np.random.randint(low=0	,		high=delta_width + 1		)
    else:
      _lowerCamelCase      =						delta_height // 2
      _lowerCamelCase      =						delta_width // 2

    _lowerCamelCase      =						delta_height - pad_top
    _lowerCamelCase      =						delta_width - pad_left

    _lowerCamelCase      =						((pad_top, pad_bottom), (pad_left, pad_right))
    return pad(a__	,		a__	,		data_format=a__		)


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''		)
    return self.pad_image(*a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = PILImageResampling.BICUBIC	,		a__ = None	,		**a__	,		):
    _lowerCamelCase    ,    _lowerCamelCase      =						get_image_size(a__		)
    _lowerCamelCase    ,    _lowerCamelCase      =						size['''height'''], size['''width''']

    # We always resize to the smallest of either the input or output size.
    _lowerCamelCase      =						min(a__	,		a__		)
    _lowerCamelCase      =						min(a__	,		a__		)

    if height == input_height and width == input_width:
      return image

    if input_height > input_width:
      _lowerCamelCase      =						int(input_width * height / input_height		)
    elif input_width > input_height:
      _lowerCamelCase      =						int(input_height * width / input_width		)

    return resize(
        a__	,		size=(height, width)	,		resample=a__	,		reducing_gap=2.0	,		data_format=a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = PILImageResampling.BICUBIC	,		a__ = None	,		**a__	,		):
    _lowerCamelCase      =						get_size_dict(a__		)
    _lowerCamelCase      =						min(size['''height''']	,		size['''width''']		)
    _lowerCamelCase      =						get_resize_output_image_size(a__	,		size=a__	,		default_to_square=a__		)
    _lowerCamelCase      =						resize(a__	,		size=a__	,		resample=a__	,		data_format=a__	,		**a__		)
    return resized_image


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__ = None	,		**a__	,		):
    return rescale(a__	,		scale=a__	,		data_format=a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__ = None	,		**a__	,		):
    return normalize(a__	,		mean=a__	,		std=a__	,		data_format=a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = False	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = None	,		a__ = ChannelDimension.FIRST	,		**a__	,		):
    _lowerCamelCase      =						do_resize if do_resize is not None else self.do_resize
    _lowerCamelCase      =						size if size is not None else self.size
    if isinstance(a__	,		(tuple, list)		):
      # Previous feature extractor had size in (width, height) format
      _lowerCamelCase      =						size[::-1]
    _lowerCamelCase      =						get_size_dict(a__		)
    _lowerCamelCase      =						resample if resample is not None else self.resample
    _lowerCamelCase      =						do_thumbnail if do_thumbnail is not None else self.do_thumbnail
    _lowerCamelCase      =						do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
    _lowerCamelCase      =						do_pad if do_pad is not None else self.do_pad
    _lowerCamelCase      =						do_rescale if do_rescale is not None else self.do_rescale
    _lowerCamelCase      =						rescale_factor if rescale_factor is not None else self.rescale_factor
    _lowerCamelCase      =						do_normalize if do_normalize is not None else self.do_normalize
    _lowerCamelCase      =						image_mean if image_mean is not None else self.image_mean
    _lowerCamelCase      =						image_std if image_std is not None else self.image_std

    _lowerCamelCase      =						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:
      raise ValueError('''Size 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_pad and size is None:
      raise ValueError('''Size must be specified if do_pad 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      =						[to_numpy_array(a__		) for image in images]

    if do_align_long_axis:
      _lowerCamelCase      =						[self.align_long_axis(a__	,		size=a__		) for image in images]

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

    if do_thumbnail:
      _lowerCamelCase      =						[self.thumbnail(image=a__	,		size=a__		) for image in images]

    if do_pad:
      _lowerCamelCase      =						[self.pad_image(image=a__	,		size=a__	,		random_padding=a__		) for image in images]

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

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

    _lowerCamelCase      =						[to_channel_dimension_format(a__	,		a__		) for image in images]

    _lowerCamelCase      =						{'''pixel_values''': images}
    return BatchFeature(data=a__	,		tensor_type=a__		)




