




"""simple docstring"""







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


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =					{
    '''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 							snake_case_ (				lowercase__ ):







			"""simple docstring"""




			A_										=					"""layoutlm"""







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

class 							snake_case_ (				lowercase__ ):







			"""simple docstring"""




			def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_ = "default"					,				lowerCamelCase_ = None					,				)							->      List[Any]:
							super().__init__(_A					,				task=_A					,				patching_specs=_A)
							UpperCamelCase										=  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					,				lowerCamelCase_					,				lowerCamelCase_ = -1					,				lowerCamelCase_ = -1					,				lowerCamelCase_ = False					,				lowerCamelCase_ = None					,				)							->      Mapping[str, Any]:
							UpperCamelCase										=  super().generate_dummy_inputs(
							    _A					,				batch_size=_A					,				seq_length=_A					,				is_pair=_A					,				framework=_A)

							# Generate a dummy bbox
							UpperCamelCase										=  [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										=  input_dict['input_ids'].shape
							UpperCamelCase										=  torch.tensor([*[box] * seq_length]).tile(_A					,				1					,				1)
							return input_dict





"""simple docstring"""







class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_=None)							->      Tuple:
       UpperCamelCase										=  data
       UpperCamelCase										=  None







   def __repr__(		self)							->      Union[str, Any]:
       UpperCamelCase										=  ''''''
       UpperCamelCase										=  self
       while temp:
           string_rep += F'<{temp.data}> ---> '
           UpperCamelCase										=  temp.next
       string_rep += "<END>"
       return string_rep





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    if not elements_list:
        raise Exception('''The Elements List is empty'''						)

    # Set first element as Head
    UpperCamelCase										=  Node(elements_list[0]						)
    UpperCamelCase										=  head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase										=  Node(_lowercase						)
        UpperCamelCase										=  current.next
    return head


SCREAMING_SNAKE_CASE_      =					[1, 3, 5, 32, 44, 12, 43]
print(f'List: {list_data}')
print('Creating Linked List from List.')
SCREAMING_SNAKE_CASE_      =					make_linked_list(list_data)
print('Linked List:')


print(linked_list)