

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


lowerCAmelCase_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							"layoutlmv3"







			def __init__(	self					:							int				,  _UpperCamelCase					:							Union[str, Any]=5_0_2_6_5				,  _UpperCamelCase					:							Optional[int]=7_6_8				,  _UpperCamelCase					:							Any=1_2				,  _UpperCamelCase					:							Dict=1_2				,  _UpperCamelCase					:							Any=3_0_7_2				,  _UpperCamelCase					:							Union[str, Any]="gelu"				,  _UpperCamelCase					:							Any=0.1				,  _UpperCamelCase					:							int=0.1				,  _UpperCamelCase					:							Optional[Any]=5_1_2				,  _UpperCamelCase					:							Tuple=2				,  _UpperCamelCase					:							Optional[int]=0.02				,  _UpperCamelCase					:							str=1e-5				,  _UpperCamelCase					:							Optional[Any]=1				,  _UpperCamelCase					:							Tuple=0				,  _UpperCamelCase					:							List[Any]=2				,  _UpperCamelCase					:							Dict=1_0_2_4				,  _UpperCamelCase					:							Tuple=1_2_8				,  _UpperCamelCase					:							List[str]=1_2_8				,  _UpperCamelCase					:							str=True				,  _UpperCamelCase					:							Union[str, Any]=3_2				,  _UpperCamelCase					:							Any=1_2_8				,  _UpperCamelCase					:							Dict=6_4				,  _UpperCamelCase					:							Optional[Any]=2_5_6				,  _UpperCamelCase					:							int=True				,  _UpperCamelCase					:							Any=True				,  _UpperCamelCase					:							str=True				,  _UpperCamelCase					:							List[Any]=2_2_4				,  _UpperCamelCase					:							int=3				,  _UpperCamelCase					:							str=1_6				,  _UpperCamelCase					:							Optional[int]=None				,  **_UpperCamelCase					:							List[Any]				,  ) ->Any:
									super().__init__(
									    vocab_size=_UpperCamelCase				,  hidden_size=_UpperCamelCase				,  num_hidden_layers=_UpperCamelCase				,  num_attention_heads=_UpperCamelCase				,  intermediate_size=_UpperCamelCase				,  hidden_act=_UpperCamelCase				,  hidden_dropout_prob=_UpperCamelCase				,  attention_probs_dropout_prob=_UpperCamelCase				,  max_position_embeddings=_UpperCamelCase				,  type_vocab_size=_UpperCamelCase				,  initializer_range=_UpperCamelCase				,  layer_norm_eps=_UpperCamelCase				,  pad_token_id=_UpperCamelCase				,  bos_token_id=_UpperCamelCase				,  eos_token_id=_UpperCamelCase				,  **_UpperCamelCase				,  )
									snake_case_  =      max_ad_position_embeddings
									snake_case_  =      coordinate_size
									snake_case_  =      shape_size
									snake_case_  =      has_relative_attention_bias
									snake_case_  =      rel_pos_bins
									snake_case_  =      max_rel_pos
									snake_case_  =      has_spatial_attention_bias
									snake_case_  =      rel_ad_pos_bins
									snake_case_  =      max_rel_ad_pos
									snake_case_  =      text_embed
									snake_case_  =      visual_embed
									snake_case_  =      input_size
									snake_case_  =      num_channels
									snake_case_  =      patch_size
									snake_case_  =      classifier_dropout






class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  str		     =							version.parse("1.12"   )







			@property
			def 							snake_case__(	self					:							str				) ->Mapping[str, Mapping[int, str]]:
									# The order of inputs is different for question answering and sequence classification
									if self.task in ["question-answering", "sequence-classification"]:
															return OrderedDict(
															    [
															        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
															        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
															        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
															        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
															    ]				)
									else:
															return OrderedDict(
															    [
															        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
															        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
															        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
															        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
															    ]				)







			@property
			def 							snake_case__(	self					:							Any				) ->float:
									return 1e-5







			@property
			def 							snake_case__(	self					:							str				) ->int:
									return 1_2







			def 							snake_case__(	self					:							Tuple				,  _UpperCamelCase					:							"ProcessorMixin"				,  _UpperCamelCase					:							int = -1				,  _UpperCamelCase					:							int = -1				,  _UpperCamelCase					:							bool = False				,  _UpperCamelCase					:							Optional["TensorType"] = None				,  _UpperCamelCase					:							int = 3				,  _UpperCamelCase					:							int = 4_0				,  _UpperCamelCase					:							int = 4_0				,  ) ->Mapping[str, Any]:
									setattr(processor.image_processor				,  '''apply_ocr'''				,  _UpperCamelCase				)

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									snake_case_  =      compute_effective_axis_dimension(
									    _UpperCamelCase				,  fixed_dimension=OnnxConfig.default_fixed_batch				,  num_token_to_add=0				)
									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									snake_case_  =      processor.tokenizer.num_special_tokens_to_add(_UpperCamelCase				)
									snake_case_  =      compute_effective_axis_dimension(
									    _UpperCamelCase				,  fixed_dimension=OnnxConfig.default_fixed_sequence				,  num_token_to_add=_UpperCamelCase				)
									# Generate dummy inputs according to compute batch and sequence
									snake_case_  =      [[''' '''.join([processor.tokenizer.unk_token]				) * seq_length]] * batch_size

									# Generate dummy bounding boxes
									snake_case_  =      [[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
									snake_case_  =      self._generate_dummy_images(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)

									snake_case_  =      dict(
									    processor(
									        _UpperCamelCase				,  text=_UpperCamelCase				,  boxes=_UpperCamelCase				,  return_tensors=_UpperCamelCase				,  )				)

									return inputs




"""simple docstring"""

def  __snake_case							(	__A      :						int							,		__A      :						int     )     ->       float:






				'''simple docstring'''
				return base * power(__A							,		(exponent - 1)     ) if exponent else 1


if __name__ == "__main__":
							print('Raise base to the power of exponent using recursion...')
							A_						:    str				=      int(input('Enter the base: ').strip())
							A_						:    Dict				=      int(input('Enter the exponent: ').strip())
							A_						:    List[str]				=      power(base, abs(exponent))
							if exponent < 0:  # power() does not properly deal w/ negative exponents
														A_						:    Any				=      1 / result
							print(f'''{base} to the power of {exponent} is {result}''')





