import timeit




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):  # Initializes a matrix identical to the one provided.
											__lowercase			      =							arg
											__lowercase			      =							len(lowerCAmelCase__						)
							else:  # Initializes a square matrix of the given size and set values to zero.
											__lowercase			      =							arg
											__lowercase			      =							[[0 for _ in range(self.n						)] for _ in range(self.n						)]





			def __mul__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							Matrix(self.n						)
							for i in range(self.n						):
											for j in range(self.n						):
															for k in range(self.n						):
																			matrix.t[i][j] += self.t[i][k] * b.t[k][j]
							return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase			      =							Matrix([[1, 0], [0, 1]]							)
				while b > 0:
								if b & 1:
												matrix *= a
								a *= a
								b >>= 1
				return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa
				__lowercase			      =							Matrix([[1, 1], [1, 0]]							)
				__lowercase			      =							modular_exponentiation(lowercase							,      n - 2							)
				return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa

				__lowercase			      =							fa
				__lowercase			      =							fa
				n -= 2

				while n > 0:
								__lowercase      ,					__lowercase			      =							fn_a + fn_a, fn_a
								n -= 1

				return fn_a





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
				__lowercase			      =							'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print('''With matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import simple_fibonacci
'''
				__lowercase			      =							'''simple_fibonacci(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print(
				    '''Without matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				matrix_exponentiation_time()
				simple_fibonacci_time()


if __name__ == "__main__":
		main()
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  :					int          =  logging.get_logger(__name__)

__a  :					Optional[Any]          =  {
    """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				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''layoutlm'''





			def __init__(       self				,       lowerCAmelCase__=3_05_22				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=12				,       lowerCAmelCase__=12				,       lowerCAmelCase__=30_72				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=2				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=1E-12				,       lowerCAmelCase__=0				,       lowerCAmelCase__="absolute"				,       lowerCAmelCase__=True				,       lowerCAmelCase__=10_24				,       **lowerCAmelCase__				,       )					-> Tuple:

							'''simple docstring'''


							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        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = "default"				,       lowerCAmelCase__ = None				,       )					-> str:

							'''simple docstring'''


							super().__init__(lowerCAmelCase__				,       task=lowerCAmelCase__				,       patching_specs=lowerCAmelCase__						)
							__lowercase			      =							config.max_ad_position_embeddings - 1





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, 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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__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, 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

							__lowercase      ,					__lowercase			      =							input_dict['''input_ids'''].shape
							__lowercase			      =							torch.tensor([*[box] * seq_length]						).tile(lowerCAmelCase__				,       1				,       1						)
							return input_dict