






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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__UpperCAmelCase										=		logging.get_logger(__name__)

__UpperCAmelCase										=		{
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}



class 						lowerCamelCase   (lowercase__    ):







		'''simple docstring'''



		_snake_case					:					Optional[Any] 		=						"""layoutlm"""



		def __init__(							self       ,	_UpperCamelCase=3_0_5_2_2       ,	_UpperCamelCase=7_6_8       ,	_UpperCamelCase=1_2       ,	_UpperCamelCase=1_2       ,	_UpperCamelCase=3_0_7_2       ,	_UpperCamelCase="gelu"       ,	_UpperCamelCase=0.1       ,	_UpperCamelCase=0.1       ,	_UpperCamelCase=5_1_2       ,	_UpperCamelCase=2       ,	_UpperCamelCase=0.02       ,	_UpperCamelCase=1E-12       ,	_UpperCamelCase=0       ,	_UpperCamelCase="absolute"       ,	_UpperCamelCase=True       ,	_UpperCamelCase=1_0_2_4       ,	**_UpperCamelCase       ,	)			->							Optional[int]:
							super().__init__(pad_token_id=_A       ,	**_A     )
							UpperCAmelCase_					:   str   =		vocab_size
							UpperCAmelCase_					:   Optional[int]   =		hidden_size
							UpperCAmelCase_					:   Optional[Any]   =		num_hidden_layers
							UpperCAmelCase_					:   List[Any]   =		num_attention_heads
							UpperCAmelCase_					:   Any   =		hidden_act
							UpperCAmelCase_					:   List[str]   =		intermediate_size
							UpperCAmelCase_					:   Optional[int]   =		hidden_dropout_prob
							UpperCAmelCase_					:   Optional[int]   =		attention_probs_dropout_prob
							UpperCAmelCase_					:   Union[str, Any]   =		max_position_embeddings
							UpperCAmelCase_					:   Union[str, Any]   =		type_vocab_size
							UpperCAmelCase_					:   Dict   =		initializer_range
							UpperCAmelCase_					:   List[Any]   =		layer_norm_eps
							UpperCAmelCase_					:   Tuple   =		position_embedding_type
							UpperCAmelCase_					:   Union[str, Any]   =		use_cache
							UpperCAmelCase_					:   List[str]   =		max_ad_position_embeddings





class 						lowerCamelCase   (lowercase__    ):







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase       ,	_UpperCamelCase = "default"       ,	_UpperCamelCase = None       ,	)			->							List[Any]:
							super().__init__(_A       ,	task=_A       ,	patching_specs=_A     )
							UpperCAmelCase_					:   int   =		config.max_ad_position_embeddings - 1



		@property
		def       __UpperCAmelCase    (							self     )			->							Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'sequence'}),
							        ('bbox', {0: 'batch', 1: 'sequence'}),
							        ('attention_mask', {0: 'batch', 1: 'sequence'}),
							        ('token_type_ids', {0: 'batch', 1: 'sequence'}),
							    ]     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase = -1       ,	_UpperCamelCase = -1       ,	_UpperCamelCase = False       ,	_UpperCamelCase = None       ,	)			->							Mapping[str, Any]:
							UpperCAmelCase_					:   List[str]   =		super().generate_dummy_inputs(
							    _A       ,	batch_size=_A       ,	seq_length=_A       ,	is_pair=_A       ,	framework=_A     )

							# Generate a dummy bbox
							UpperCAmelCase_					:   Tuple   =		[4_8, 8_4, 7_3, 1_2_8]

							if not framework == TensorType.PYTORCH:
												raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'     )

							if not is_torch_available():
												raise ValueError('Cannot generate dummy inputs without PyTorch installed.'     )
							import torch

							UpperCAmelCase_					:   Dict   =		input_dict['input_ids'].shape
							UpperCAmelCase_					:   List[Any]   =		torch.tensor([*[box] * seq_length]     ).tile(_A       ,	1       ,	1     )
							return input_dict














from __future__ import annotations




def   lowercase__   (  __snake_case					:							Any  , __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[int]   =		0
					UpperCAmelCase_					:   Optional[int]   =		len(__snake_case						) - 1
					while left <= right:
										UpperCAmelCase_					:   Optional[Any]   =		(left + right) // 2
										if left == right:
															if sorted_list[middle] < item:
																				UpperCAmelCase_					:   List[Any]   =		middle + 1
															break
										elif sorted_list[middle] < item:
															UpperCAmelCase_					:   Union[str, Any]   =		middle + 1
										else:
															UpperCAmelCase_					:   Tuple   =		middle - 1
					sorted_list.insert(__snake_case  , __snake_case						)
					return sorted_list




def   lowercase__   (  __snake_case					:							Union[str, Any]  , __snake_case					:							int						):


					'''simple docstring'''


					UpperCAmelCase_					:   str   =		[]
					while left and right:
										if left[0][0] < right[0][0]:
															result.append(left.pop(0						)						)
										else:
															result.append(right.pop(0						)						)
					return result + left + right




def   lowercase__   (  __snake_case					:							Dict						):


					'''simple docstring'''


					UpperCAmelCase_					:   str   =		len(__snake_case						)
					if length <= 1:
										return list_ad
					UpperCAmelCase_					:   List[Any]   =		length // 2
					return merge(sortlist_ad(list_ad[:middle]						)  , sortlist_ad(list_ad[middle:]						)						)




def   lowercase__   (  __snake_case					:							list[int]						):


					'''simple docstring'''


					if len(__snake_case						) <= 1:
										return collection
					UpperCAmelCase_					:   Any   =		[]
					UpperCAmelCase_					:   Union[str, Any]   =		False
					for i in range(0  , len(__snake_case						)  , 2						):
										if i == len(__snake_case						) - 1:
															UpperCAmelCase_					:   Tuple   =		True
										else:
															if collection[i] < collection[i + 1]:
																				two_paired_list.append([collection[i], collection[i + 1]]						)
															else:
																				two_paired_list.append([collection[i + 1], collection[i]]						)
					UpperCAmelCase_					:   Union[str, Any]   =		sortlist_ad(__snake_case						)
					UpperCAmelCase_					:   Any   =		[i[0] for i in sorted_list_ad]
					result.append(sorted_list_ad[-1][1]						)
					if has_last_odd_item:
										UpperCAmelCase_					:   Union[str, Any]   =		collection[-1]
										UpperCAmelCase_					:   List[Any]   =		binary_search_insertion(__snake_case  , __snake_case						)
					UpperCAmelCase_					:   Tuple   =		False
					for i in range(len(__snake_case						) - 1						):
										if result[i] == collection[-1] and has_last_odd_item:
															UpperCAmelCase_					:   List[Any]   =		True
										UpperCAmelCase_					:   Optional[int]   =		sorted_list_ad[i][1]
										# If last_odd_item is inserted before the item's index,
										# you should forward index one more.
										if is_last_odd_item_inserted_before_this_index:
															UpperCAmelCase_					:   List[str]   =		result[: i + 2] + binary_search_insertion(result[i + 2 :]  , __snake_case						)
										else:
															UpperCAmelCase_					:   List[Any]   =		result[: i + 1] + binary_search_insertion(result[i + 1 :]  , __snake_case						)

					return result


if __name__ == "__main__":
		__UpperCAmelCase										=		input('Enter numbers separated by a comma:\n').strip()
		__UpperCAmelCase										=		[int(item) for item in user_input.split(',')]
		print(merge_insertion_sort(unsorted))






