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


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "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 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'layoutlm'


	def __init__(   self       ,  lowerCAmelCase_=30522       ,  lowerCAmelCase_=768       ,  lowerCAmelCase_=12       ,  lowerCAmelCase_=12       ,  lowerCAmelCase_=3072       ,  lowerCAmelCase_="gelu"       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=0.0_2       ,  lowerCAmelCase_=1e-12       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_="absolute"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=1024       ,  **lowerCAmelCase_       ,  ):
		super().__init__(pad_token_id=lowerCAmelCase_       ,  **lowerCAmelCase_       )
		_lowercase   =vocab_size
		_lowercase   =hidden_size
		_lowercase   =num_hidden_layers
		_lowercase   =num_attention_heads
		_lowercase   =hidden_act
		_lowercase   =intermediate_size
		_lowercase   =hidden_dropout_prob
		_lowercase   =attention_probs_dropout_prob
		_lowercase   =max_position_embeddings
		_lowercase   =type_vocab_size
		_lowercase   =initializer_range
		_lowercase   =layer_norm_eps
		_lowercase   =position_embedding_type
		_lowercase   =use_cache
		_lowercase   =max_ad_position_embeddings

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = "default"       ,  lowerCAmelCase_ = None       ,  ):
		super().__init__(lowerCAmelCase_       ,  task=lowerCAmelCase_       ,  patching_specs=lowerCAmelCase_       )
		_lowercase   =config.max_ad_position_embeddings - 1


	@property
	def __lowerCAmelCase      (   self       ):
		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 __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = -1       ,  lowerCAmelCase_ = -1       ,  lowerCAmelCase_ = False       ,  lowerCAmelCase_ = None       ,  ):
		_lowercase   =super().generate_dummy_inputs(
		    lowerCAmelCase_       ,  batch_size=lowerCAmelCase_       ,  seq_length=lowerCAmelCase_       ,  is_pair=lowerCAmelCase_       ,  framework=lowerCAmelCase_       )

		# Generate a dummy bbox
		_lowercase   =[48, 84, 73, 128]

		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

		_lowercase   ,  _lowercase   =input_dict["input_ids"].shape
		_lowercase   =torch.tensor([*[box] * seq_length]       ).tile(lowerCAmelCase_       ,  1       ,  1       )
		return input_dict






"""simple docstring"""







def       __UpperCAmelCase		(		a_      :		list[int]					)  ->							int:

   # validate:
   if not isinstance(a_     ,	a_					) or not all(
       isinstance(a_     ,	a_					) and item >= 0 for item in citations					):
      raise ValueError("The citations should be a list of non negative integers."					)

   citations.sort()
   _UpperCAmelCase 						=       len(a_					)

   for i in range(a_					):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
  import doctest

  doctest.testmod()

