





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


snake_case_				=      logging.get_logger(__name__)

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_		(      UpperCAmelCase__):
		lowerCamelCase     :Any							=  "layoutlm"







		def __init__(		self      ,  __lowercase=3_0_5_2_2      ,  __lowercase=7_6_8      ,  __lowercase=1_2      ,  __lowercase=1_2      ,  __lowercase=3_0_7_2      ,  __lowercase="gelu"      ,  __lowercase=0.1      ,  __lowercase=0.1      ,  __lowercase=5_1_2      ,  __lowercase=2      ,  __lowercase=0.0_2      ,  __lowercase=1e-12      ,  __lowercase=0      ,  __lowercase="absolute"      ,  __lowercase=True      ,  __lowercase=1_0_2_4      ,  **__lowercase      ,  )					-> List[Any]:
				super().__init__(pad_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase )
				lowerCamelCase :			Any    		=vocab_size
				lowerCamelCase :			Union[str, Any]    		=hidden_size
				lowerCamelCase :			List[Any]    		=num_hidden_layers
				lowerCamelCase :			Tuple    		=num_attention_heads
				lowerCamelCase :			Optional[Any]    		=hidden_act
				lowerCamelCase :			Union[str, Any]    		=intermediate_size
				lowerCamelCase :			Union[str, Any]    		=hidden_dropout_prob
				lowerCamelCase :			int    		=attention_probs_dropout_prob
				lowerCamelCase :			str    		=max_position_embeddings
				lowerCamelCase :			Tuple    		=type_vocab_size
				lowerCamelCase :			int    		=initializer_range
				lowerCamelCase :			Any    		=layer_norm_eps
				lowerCamelCase :			Tuple    		=position_embedding_type
				lowerCamelCase :			List[str]    		=use_cache
				lowerCamelCase :			int    		=max_ad_position_embeddings

class 							snake_case_		(      UpperCAmelCase__):







		def __init__(		self      ,  __lowercase      ,  __lowercase = "default"      ,  __lowercase = None      ,  )					-> Optional[int]:
				super().__init__(_lowerCAmelCase      ,  task=_lowerCAmelCase      ,  patching_specs=_lowerCAmelCase )
				lowerCamelCase :			List[str]    		=config.max_ad_position_embeddings - 1







		@property
		def       __lowercase (		self )					-> List[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       __lowercase (		self      ,  __lowercase      ,  __lowercase = -1      ,  __lowercase = -1      ,  __lowercase = False      ,  __lowercase = None      ,  )					-> int:
				lowerCamelCase :			List[str]    		=super().generate_dummy_inputs(
				    _lowerCAmelCase      ,  batch_size=_lowerCAmelCase      ,  seq_length=_lowerCAmelCase      ,  is_pair=_lowerCAmelCase      ,  framework=_lowerCAmelCase )

				# Generate a dummy bbox
				lowerCamelCase :			int    		=[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

				lowerCamelCase		, lowerCamelCase :			List[Any]    		=input_dict['''input_ids'''].shape
				lowerCamelCase :			str    		=torch.tensor([*[box] * seq_length] ).tile(_lowerCAmelCase      ,  1      ,  1 )
				return input_dict







from __future__ import annotations

from typing import Any


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):  # noqa: A002
            a			   =item
            a			   =next



class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self	):
            a			   =None
            a			   =0
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            a			   =Node(_lowerCAmelCase ,    self.head	)
            self.size += 1
     def        lowerCAmelCase__  (				self	):
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   a			   =self.head.item
                   a			   =self.head.next
                   self.size -= 1
                   return item
     def        lowerCAmelCase__  (				self	):
            return self.head is None
     def __str__(				self	):
            if self.is_empty():
                   return ""
            else:
                   a			   =self.head
                   a			   =""""""
                   a			   =[]
                   while iterate:
                          item_list.append(str(iterate.item	)	)
                          a			   =iterate.next

                   a			   =""" --> """.join(_lowerCAmelCase	)

                   return item_str






     def __len__(				self	):
            return self.size
