


'''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


_a      :      Optional[Any]       				=      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  _lowercase   (  UpperCAmelCase_       ):
			_SCREAMING_SNAKE_CASE     :     Dict									=  "layoutlm"
			def __init__(	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Any=3_0522					,      SCREAMING_SNAKE_CASE_					:			Dict=768					,      SCREAMING_SNAKE_CASE_					:			Any=12					,      SCREAMING_SNAKE_CASE_					:			str=12					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=3072					,      SCREAMING_SNAKE_CASE_					:			List[Any]="gelu"					,      SCREAMING_SNAKE_CASE_					:			List[Any]=0.1					,      SCREAMING_SNAKE_CASE_					:			int=0.1					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=512					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=2					,      SCREAMING_SNAKE_CASE_					:			int=0.0_2					,      SCREAMING_SNAKE_CASE_					:			Optional[Any]=1e-12					,      SCREAMING_SNAKE_CASE_					:			str=0					,      SCREAMING_SNAKE_CASE_					:			List[Any]="absolute"					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=True					,      SCREAMING_SNAKE_CASE_					:			Tuple=1024					,      **SCREAMING_SNAKE_CASE_					:			str					,      )   ->       Dict:
								super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_					,      **SCREAMING_SNAKE_CASE_			)
								__snake_case    			=	vocab_size
								__snake_case    			=	hidden_size
								__snake_case    			=	num_hidden_layers
								__snake_case    			=	num_attention_heads
								__snake_case    			=	hidden_act
								__snake_case    			=	intermediate_size
								__snake_case    			=	hidden_dropout_prob
								__snake_case    			=	attention_probs_dropout_prob
								__snake_case    			=	max_position_embeddings
								__snake_case    			=	type_vocab_size
								__snake_case    			=	initializer_range
								__snake_case    			=	layer_norm_eps
								__snake_case    			=	position_embedding_type
								__snake_case    			=	use_cache
								__snake_case    			=	max_ad_position_embeddings




class  _lowercase   (  UpperCAmelCase_       ):
			def __init__(	self					:			Dict					,      SCREAMING_SNAKE_CASE_					:			Tuple					,      SCREAMING_SNAKE_CASE_					:			List[str] = "default"					,      SCREAMING_SNAKE_CASE_					:			List[Any] = None					,      )   ->       Optional[Any]:
								super().__init__(SCREAMING_SNAKE_CASE_					,      task=SCREAMING_SNAKE_CASE_					,      patching_specs=SCREAMING_SNAKE_CASE_			)
								__snake_case    			=	config.max_ad_position_embeddings - 1
			@property
			def a     (	self					:			Any			)   ->       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 a     (	self					:			str					,      SCREAMING_SNAKE_CASE_					:			List[Any]					,      SCREAMING_SNAKE_CASE_					:			List[str] = -1					,      SCREAMING_SNAKE_CASE_					:			int = -1					,      SCREAMING_SNAKE_CASE_					:			Dict = False					,      SCREAMING_SNAKE_CASE_					:			int = None					,      )   ->       Mapping[str, Any]:
								__snake_case    			=	super().generate_dummy_inputs(
								    SCREAMING_SNAKE_CASE_					,      batch_size=SCREAMING_SNAKE_CASE_					,      seq_length=SCREAMING_SNAKE_CASE_					,      is_pair=SCREAMING_SNAKE_CASE_					,      framework=SCREAMING_SNAKE_CASE_			)

								# Generate a dummy bbox
								__snake_case    			=	[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

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








'''simple docstring'''




import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

_a      :      Optional[int]       				=      0.25 + 0.0J
_a      :      Dict       				=      -0.4 + 0.6J
_a      :      List[str]       				=      -0.1 + 0.651J
_a      :      Optional[int]       				=      -2.0
_a      :      List[str]       				=      56
_a      :      Tuple       				=      2.0
_a      :      List[str]       				=      666





def      _a		(lowercase__ :      complex       ,      lowercase__ :      numpy.ndarray	) -> numpy.ndarray:






     """simple docstring"""
     return numpy.exp(lowercase__	) + c_parameter





def      _a		(lowercase__ :      complex       ,      lowercase__ :      numpy.ndarray	) -> numpy.ndarray:






     """simple docstring"""
     return z_values * z_values + c_parameter





def      _a		(lowercase__ :      float       ,      lowercase__ :      int	) -> numpy.ndarray:






     """simple docstring"""
     __snake_case    			=	numpy.linspace(-window_size       ,      lowercase__       ,      lowercase__	)
     __snake_case    			=	x.reshape((nb_pixels, 1)	)
     __snake_case    			=	numpy.linspace(-window_size       ,      lowercase__       ,      lowercase__	)
     __snake_case    			=	y.reshape((1, nb_pixels)	)
     return x + 1.0J * y





def      _a		(lowercase__ :      Callable[[Any, numpy.ndarray], numpy.ndarray]       ,      lowercase__ :      Any       ,      lowercase__ :      int       ,      lowercase__ :      numpy.ndarray       ,      lowercase__ :      float | None = None       ,      ) -> numpy.ndarray:






     """simple docstring"""
     __snake_case    			=	z_a.astype('complex64'	)
     for _ in range(lowercase__	):
          __snake_case    			=	eval_function(lowercase__       ,      lowercase__	)
          if infinity is not None:
               numpy.nan_to_num(lowercase__       ,      copy=lowercase__       ,      nan=lowercase__	)
               __snake_case    			=	infinity
     return z_n





def      _a		(lowercase__ :      str       ,      lowercase__ :      Any       ,      lowercase__ :      float       ,      lowercase__ :      numpy.ndarray       ,      ) -> None:






     """simple docstring"""
     __snake_case    			=	(abs(lowercase__	)).transpose()
     __snake_case    			=	abs_z_final[::-1, :]
     pyplot.matshow(abs_z_final < escape_radius	)
     pyplot.title(f'Julia set of ${function_label}$, $c={function_params}$'	)
     pyplot.show()





def      _a		() -> None:






     """simple docstring"""
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in multiply'	)
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='invalid value encountered in multiply'       ,      )
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in absolute'	)
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in exp'	)


if __name__ == "__main__":
       _a      :      List[Any]       				=      prepare_grid(window_size, nb_pixels)

       ignore_overflow_warnings()  # See file header for explanations

       _a      :      Optional[Any]       				=      24
       _a      :      Optional[int]       				=      2 * abs(c_cauliflower) + 1
       _a      :      List[str]       				=      iterate_function(
           eval_quadratic_polynomial,
           c_cauliflower,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_cauliflower, escape_radius, z_final)

       _a      :      str       				=      64
       _a      :      Tuple       				=      2 * abs(c_polynomial_a) + 1
       _a      :      List[Any]       				=      iterate_function(
           eval_quadratic_polynomial,
           c_polynomial_a,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

       _a      :      List[Any]       				=      161
       _a      :      Tuple       				=      2 * abs(c_polynomial_a) + 1
       _a      :      int       				=      iterate_function(
           eval_quadratic_polynomial,
           c_polynomial_a,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

       _a      :      str       				=      12
       _a      :      Optional[Any]       				=      10_000.0
       _a      :      Dict       				=      iterate_function(
           eval_exponential,
           c_exponential,
           nb_iterations,
           z_a + 2,
           infinity=1.0e1_0,
       )
       show_results("e^z+c", c_exponential, escape_radius, z_final)




