
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


__a							  =						logging.get_logger(__name__)

__a							  =						{
    "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        UpperCamelCase__(    snake_case__	):

	"""simple docstring"""

	_A  =		"layoutlm"







	def __init__(      self				:			int							,			snake_case__				:			Optional[Any]=3_05_22							,			snake_case__				:			Any=7_68							,			snake_case__				:			Tuple=12							,			snake_case__				:			str=12							,			snake_case__				:			Optional[int]=30_72							,			snake_case__				:			Optional[int]="gelu"							,			snake_case__				:			int=0.1							,			snake_case__				:			str=0.1							,			snake_case__				:			str=5_12							,			snake_case__				:			str=2							,			snake_case__				:			Optional[int]=0.02							,			snake_case__				:			Tuple=1E-12							,			snake_case__				:			Tuple=0							,			snake_case__				:			Any="absolute"							,			snake_case__				:			int=True							,			snake_case__				:			List[str]=10_24							,			**snake_case__				:			Optional[Any]							,			):
			"""simple docstring"""





			super().__init__(pad_token_id=UpperCAmelCase_							,			**UpperCAmelCase_ )
			A				=vocab_size
			A				=hidden_size
			A				=num_hidden_layers
			A				=num_attention_heads
			A				=hidden_act
			A				=intermediate_size
			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				=position_embedding_type
			A				=use_cache
			A				=max_ad_position_embeddings


class        UpperCamelCase__(    snake_case__	):

	"""simple docstring"""

	def __init__(      self				:			Any							,			snake_case__				:			PretrainedConfig							,			snake_case__				:			str = "default"							,			snake_case__				:			List[PatchingSpec] = None							,			):
			"""simple docstring"""





			super().__init__(UpperCAmelCase_							,			task=UpperCAmelCase_							,			patching_specs=UpperCAmelCase_ )
			A				=config.max_ad_position_embeddings - 1







	@property
	def        _a					(      self				:			List[str] ):
			"""simple docstring"""





			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        _a					(      self				:			List[Any]							,			snake_case__				:			PreTrainedTokenizer							,			snake_case__				:			int = -1							,			snake_case__				:			int = -1							,			snake_case__				:			bool = False							,			snake_case__				:			Optional[TensorType] = None							,			):
			"""simple docstring"""





			A				=super().generate_dummy_inputs(
			    UpperCAmelCase_							,			batch_size=UpperCAmelCase_							,			seq_length=UpperCAmelCase_							,			is_pair=UpperCAmelCase_							,			framework=UpperCAmelCase_ )

			# Generate a dummy bbox
			A				=[48, 84, 73, 1_28]

			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

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











'''simple docstring'''

def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       ):
		__UpperCAmelCase	:							Optional[int]     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     len(_UpperCAmelCase  )

		__UpperCAmelCase	:							int     =     [
		    [0 for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]
		__UpperCAmelCase	:							Any     =     [
		    ["0" for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]

		for i in range(1,       len_source_seq + 1  ):
				__UpperCAmelCase	:							Optional[Any]     =     i * delete_cost
				__UpperCAmelCase	:							Optional[int]     =     F"D{source_seq[i - 1]:c}"

		for i in range(1,       len_destination_seq + 1  ):
				__UpperCAmelCase	:							Optional[int]     =     i * insert_cost
				__UpperCAmelCase	:							List[Any]     =     F"I{destination_seq[i - 1]:c}"

		for i in range(1,       len_source_seq + 1  ):
				for j in range(1,       len_destination_seq + 1  ):
						if source_seq[i - 1] == destination_seq[j - 1]:
								__UpperCAmelCase	:							List[str]     =     costs[i - 1][j - 1] + copy_cost
								__UpperCAmelCase	:							Tuple     =     F"C{source_seq[i - 1]:c}"
						else:
								__UpperCAmelCase	:							Optional[int]     =     costs[i - 1][j - 1] + replace_cost
								__UpperCAmelCase	:							Dict     =     F"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]  )

						if costs[i - 1][j] + delete_cost < costs[i][j]:
								__UpperCAmelCase	:							Union[str, Any]     =     costs[i - 1][j] + delete_cost
								__UpperCAmelCase	:							int     =     F"D{source_seq[i - 1]:c}"

						if costs[i][j - 1] + insert_cost < costs[i][j]:
								__UpperCAmelCase	:							List[str]     =     costs[i][j - 1] + insert_cost
								__UpperCAmelCase	:							Dict     =     F"I{destination_seq[j - 1]:c}"

		return costs, ops


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		if i == 0 and j == 0:
				return []
		else:
				if ops[i][j][0] == "C" or ops[i][j][0] == "R":
						__UpperCAmelCase	:							int     =     assemble_transformation(_UpperCAmelCase,       i - 1,       j - 1  )
						seq.append(ops[i][j]  )
						return seq
				elif ops[i][j][0] == "D":
						__UpperCAmelCase	:							List[Any]     =     assemble_transformation(_UpperCAmelCase,       i - 1,       _UpperCAmelCase  )
						seq.append(ops[i][j]  )
						return seq
				else:
						__UpperCAmelCase	:							Optional[Any]     =     assemble_transformation(_UpperCAmelCase,       _UpperCAmelCase,       j - 1  )
						seq.append(ops[i][j]  )
						return seq


if __name__ == "__main__":
			lowerCAmelCase__	,			lowerCAmelCase__      :   Optional[int]      	=			compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

			lowerCAmelCase__      :   Optional[Any]      	=			len(operations)
			lowerCAmelCase__      :   int      	=			len(operations[0])
			lowerCAmelCase__      :   Dict      	=			assemble_transformation(operations, m - 1, n - 1)

			lowerCAmelCase__      :   str      	=			list("Python")
			lowerCAmelCase__      :   Any      	=			0
			lowerCAmelCase__      :   Dict      	=			0

			with open("min_cost.txt", "w") as file:
						for op in sequence:
									print("".join(string))

									if op[0] == "C":
												file.write("%-16s" % "Copy %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost -= 1
									elif op[0] == "R":
												lowerCAmelCase__      :   Optional[int]      	=			op[2]

												file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
												file.write("\t\t" + "".join(string))
												file.write("\r\n")

												cost += 1
									elif op[0] == "D":
												string.pop(i)

												file.write("%-16s" % "Delete %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost += 2
									else:
												string.insert(i, op[1])

												file.write("%-16s" % "Insert %c" % op[1])
												file.write("\t\t\t" + "".join(string))
												file.write("\r\n")

												cost += 2

									i += 1

						print("".join(string))
						print("Cost: ", cost)

						file.write("\r\nMinimum cost: " + str(cost))




