






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
				from ...processing_utils import ProcessorMixin
				from ...utils import TensorType


a_		:						Tuple             =    logging.get_logger(__name__)

a_		:						Optional[Any]             =    {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}

class 		_snake_case  (  A__					):
			_lowercase		:    Tuple			     =							'''layoutlmv3'''






			def __init__(       self		,					a=5_0265		,					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-5		,					a=1		,					a=0		,					a=2		,					a=1024		,					a=128		,					a=128		,					a=True		,					a=32		,					a=128		,					a=64		,					a=256		,					a=True		,					a=True		,					a=True		,					a=224		,					a=3		,					a=16		,					a=None		,					**a		,					)      ->       Tuple:
						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		,					bos_token_id=a		,					eos_token_id=a		,					**a		,					)
						SCREAMING_SNAKE_CASE						= max_ad_position_embeddings
						SCREAMING_SNAKE_CASE						= coordinate_size
						SCREAMING_SNAKE_CASE						= shape_size
						SCREAMING_SNAKE_CASE						= has_relative_attention_bias
						SCREAMING_SNAKE_CASE						= rel_pos_bins
						SCREAMING_SNAKE_CASE						= max_rel_pos
						SCREAMING_SNAKE_CASE						= has_spatial_attention_bias
						SCREAMING_SNAKE_CASE						= rel_ad_pos_bins
						SCREAMING_SNAKE_CASE						= max_rel_ad_pos
						SCREAMING_SNAKE_CASE						= text_embed
						SCREAMING_SNAKE_CASE						= visual_embed
						SCREAMING_SNAKE_CASE						= input_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= patch_size
						SCREAMING_SNAKE_CASE						= classifier_dropout



class 		_snake_case  (  A__					):
			_lowercase		:    Union[str, Any]			     =							version.parse('''1.12'''					)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Mapping[str, Mapping[int, str]]:
						# The order of inputs is different for question answering and sequence classification
						if self.task in ["question-answering", "sequence-classification"]:
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									        ('bbox', {0: 'batch', 1: 'sequence'}),
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ])
						else:
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('bbox', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
									    ])






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       float:
						return 1E-5






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						return 12







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a = -1		,					a = -1		,					a = False		,					a = None		,					a = 3		,					a = 40		,					a = 40		,					)      ->       Mapping[str, Any]:
						setattr(processor.image_processor		,					'apply_ocr'		,					a)

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						SCREAMING_SNAKE_CASE						= compute_effective_axis_dimension(
						    a		,					fixed_dimension=OnnxConfig.default_fixed_batch		,					num_token_to_add=0)
						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						SCREAMING_SNAKE_CASE						= processor.tokenizer.num_special_tokens_to_add(a)
						SCREAMING_SNAKE_CASE						= compute_effective_axis_dimension(
						    a		,					fixed_dimension=OnnxConfig.default_fixed_sequence		,					num_token_to_add=a)
						# Generate dummy inputs according to compute batch and sequence
						SCREAMING_SNAKE_CASE						= [[' '.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						SCREAMING_SNAKE_CASE						= [[[48, 84, 73, 128]]] * batch_size

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
						SCREAMING_SNAKE_CASE						= self._generate_dummy_images(a		,					a		,					a		,					a)

						SCREAMING_SNAKE_CASE						= dict(
						    processor(
						        a		,					text=a		,					boxes=a		,					return_tensors=a		,					))

						return inputs












from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

a_		:						str             =    (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a_		:						list[int]             =    [ord(letter) for letter in string.ascii_lowercase]
a_		:						set[int]             =    {ord(char) for char in VALID_CHARS}

a_		:						list[str]             =    ["the", "be", "to", "of", "and", "in", "that", "have"]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= ""
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42

			for keychar, cipherchar in zip(cycle(_UpperCAmelCase)							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= cipherchar ^ keychar
						if decodedchar not in VALID_INTS:
									return None
						decoded += chr(_UpperCAmelCase)

			return decoded






def 				lowerCamelCase__				(_UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= []
			for key in product(_UpperCAmelCase							,       repeat=3):
						SCREAMING_SNAKE_CASE						= try_key(_UpperCAmelCase							,       _UpperCAmelCase)
						if encoded is not None:
									possibles.append(_UpperCAmelCase)
			return possibles






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			return [possible for possible in possibles if common_word in possible.lower()]






def 				lowerCamelCase__				(_UpperCAmelCase = "p059_cipher.txt"):
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= 42
			SCREAMING_SNAKE_CASE						= Path(_UpperCAmelCase).parent.joinpath(_UpperCAmelCase).read_text(encoding='utf-8')

			SCREAMING_SNAKE_CASE						= [int(_UpperCAmelCase) for number in data.strip().split(',')]

			SCREAMING_SNAKE_CASE						= filter_valid_chars(_UpperCAmelCase)
			for common_word in COMMON_WORDS:
						SCREAMING_SNAKE_CASE						= filter_common_word(_UpperCAmelCase							,       _UpperCAmelCase)
						if len(_UpperCAmelCase) == 1:
									break

			SCREAMING_SNAKE_CASE						= possibles[0]
			return sum(ord(_UpperCAmelCase) for char in decoded_text)


if __name__ == "__main__":
				print(f"""{solution() = }""")




