


"""simple docstring"""







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


lowerCamelCase_  :  Optional[Any]     		=    logging.get_logger(__name__)

lowerCamelCase_  :  Optional[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  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    "layoutlmv2"


 def __init__(						self					,       __A=3_0522					,       __A=768					,       __A=12					,       __A=12					,       __A=3072					,       __A="gelu"					,       __A=0.1					,       __A=0.1					,       __A=512					,       __A=2					,       __A=0.02					,       __A=1E-1_2					,       __A=0					,       __A=1024					,       __A=128					,       __A=32					,       __A=True					,       __A=256					,       __A=64					,       __A=True					,       __A=[7, 7, 256]					,       __A=128					,       __A=128					,       __A=True					,       __A=True					,       __A=False					,       __A=None					,       **__A					,       )   ->	Union[str, Any]:
     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					,       )
     a											=max_ad_position_embeddings
     a											=max_rel_pos
     a											=rel_pos_bins
     a											=fast_qkv
     a											=max_rel_ad_pos
     a											=rel_ad_pos_bins
     a											=convert_sync_batchnorm
     a											=image_feature_pool_shape
     a											=coordinate_size
     a											=shape_size
     a											=has_relative_attention_bias
     a											=has_spatial_attention_bias
     a											=has_visual_segment_embedding
     a											=(
         detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
     )


 @classmethod
 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     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": [[32], [64], [128], [256], [512]],
         "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
         "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
         "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
         "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
         "MODEL.POST_NMS_TOPK_TEST": 1000,
         "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": 14,
         "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
         "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
         "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
         "MODEL.RESNETS.DEPTH": 101,
         "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
         "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
         "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
         "MODEL.RESNETS.NUM_GROUPS": 32,
         "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
         "MODEL.RESNETS.STRIDE_IN_1X1": False,
     }





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=detectrona.config.get_cfg()
     for k, v in self.detectrona_config_args.items():
         a											=k.split('''.'''		)
         a											=detectrona_config
         for attribute in attributes[:-1]:
             a											=getattr(__A					,       __A		)
         setattr(__A					,       attributes[-1]					,       __A		)

     return detectrona_config



"""simple docstring"""







from __future__ import annotations

import random
import string






class  __A							:






 """simple docstring"""




 def __init__(						self					,       __A = None		)   ->	None:
     a											=passcode or self.__passcode_creator()
     a											=self.__make_key_list()
     a											=self.__make_shift_key()


 def __str__(						self		)   ->	str:
     return "".join(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	list[int]:
     for i in range(1					,       len(__A		)					,       2		):
         iterlist[i] *= -1
     return iterlist


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=string.ascii_letters + string.digits
     a											=[random.choice(__A		) for _ in range(random.randint(10					,       20		)		)]
     return password


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	list[str]:
     a											=(
         string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
     )

     a											=[]

     # creates points known as breakpoints to break the key_list_options at those
     # points and pivot each substring
     a											=sorted(set(self.__passcode		)		)
     a											=[]

     # algorithm for creating a new shuffled list, keys_l, out of key_list_options
     for i in key_list_options:
         temp_list.extend(__A		)

         # checking breakpoints at which to pivot temporary sublist and add it into
         # keys_l
         if i in breakpoints or i == key_list_options[-1]:
             keys_l.extend(temp_list[::-1]		)
             temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
     return keys_l


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	int:
     a											=sum(self.__neg_pos([ord(__A		) for x in self.__passcode]		)		)
     return num if num > 0 else len(self.__passcode		)


 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # decoding shift like Caesar cipher algorithm implementing negative shift or
     # reverse shift or left shift
     for i in encoded_message:
         a											=self.__key_list.index(__A		)
         decoded_message += self.__key_list[
             (position - self.__shift_key) % -len(self.__key_list		)
         ]

     return decoded_message





 def  SCREAMING_SNAKE_CASE				(						self					,       __A		)   ->	str:
     a											=''''''

     # encoding shift like Caesar cipher algorithm implementing positive shift or
     # forward shift or right shift
     for i in plaintext:
         a											=self.__key_list.index(__A		)
         encoded_message += self.__key_list[
             (position + self.__shift_key) % len(self.__key_list		)
         ]

     return encoded_message

def _A		(		lowercase = "Hello, this is a modified Caesar cipher"						):







    """simple docstring"""
    a											=ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(lowercase						)						)


if __name__ == "__main__":
       import doctest

       doctest.testmod()