





"""simple docstring"""



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


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Dict        =   "layoutlmv3"






       def __init__(   self  :    Tuple  ,     snake_case__  :    Tuple=5_0_2_6_5  ,     snake_case__  :    List[Any]=7_6_8  ,     snake_case__  :    Optional[int]=1_2  ,     snake_case__  :    Any=1_2  ,     snake_case__  :    int=3_0_7_2  ,     snake_case__  :    Dict="gelu"  ,     snake_case__  :    int=0.1  ,     snake_case__  :    Any=0.1  ,     snake_case__  :    Tuple=5_1_2  ,     snake_case__  :    List[Any]=2  ,     snake_case__  :    int=0.02  ,     snake_case__  :    Optional[int]=1e-5  ,     snake_case__  :    Tuple=1  ,     snake_case__  :    Dict=0  ,     snake_case__  :    Union[str, Any]=2  ,     snake_case__  :    Dict=1_0_2_4  ,     snake_case__  :    Optional[Any]=1_2_8  ,     snake_case__  :    Dict=1_2_8  ,     snake_case__  :    List[str]=True  ,     snake_case__  :    Dict=3_2  ,     snake_case__  :    Union[str, Any]=1_2_8  ,     snake_case__  :    Optional[Any]=6_4  ,     snake_case__  :    int=2_5_6  ,     snake_case__  :    Union[str, Any]=True  ,     snake_case__  :    List[str]=True  ,     snake_case__  :    str=True  ,     snake_case__  :    Any=2_2_4  ,     snake_case__  :    Tuple=3  ,     snake_case__  :    str=1_6  ,     snake_case__  :    Dict=None  ,     **snake_case__  :    int  ,     ):

              '''simple docstring'''






              super().__init__(
                  vocab_size=snake_case__  ,     hidden_size=snake_case__  ,     num_hidden_layers=snake_case__  ,     num_attention_heads=snake_case__  ,     intermediate_size=snake_case__  ,     hidden_act=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__  ,     pad_token_id=snake_case__  ,     bos_token_id=snake_case__  ,     eos_token_id=snake_case__  ,     **snake_case__  ,     )
              lowercase		:int       							=				max_ad_position_embeddings
              lowercase		:int       							=				coordinate_size
              lowercase		:Union[str, Any]       							=				shape_size
              lowercase		:Dict       							=				has_relative_attention_bias
              lowercase		:int       							=				rel_pos_bins
              lowercase		:int       							=				max_rel_pos
              lowercase		:str       							=				has_spatial_attention_bias
              lowercase		:str       							=				rel_ad_pos_bins
              lowercase		:Union[str, Any]       							=				max_rel_ad_pos
              lowercase		:int       							=				text_embed
              lowercase		:List[Any]       							=				visual_embed
              lowercase		:Dict       							=				input_size
              lowercase		:List[Any]       							=				num_channels
              lowercase		:Dict       							=				patch_size
              lowercase		:Any       							=				classifier_dropout







class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      List[str]        =   version.parse("1.12"     )






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              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  :    Optional[int]				):

              '''simple docstring'''






              return 1e-5






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              return 1_2







       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    "ProcessorMixin"  ,     snake_case__  :    int = -1  ,     snake_case__  :    int = -1  ,     snake_case__  :    bool = False  ,     snake_case__  :    Optional["TensorType"] = None  ,     snake_case__  :    int = 3  ,     snake_case__  :    int = 4_0  ,     snake_case__  :    int = 4_0  ,     ):

              '''simple docstring'''






              setattr(processor.image_processor  ,     '''apply_ocr'''  ,     snake_case__				)

              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              lowercase		:List[Any]       							=				compute_effective_axis_dimension(
                  snake_case__  ,     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
              lowercase		:List[str]       							=				processor.tokenizer.num_special_tokens_to_add(snake_case__				)
              lowercase		:str       							=				compute_effective_axis_dimension(
                  snake_case__  ,     fixed_dimension=OnnxConfig.default_fixed_sequence  ,     num_token_to_add=snake_case__				)
              # Generate dummy inputs according to compute batch and sequence
              lowercase		:Optional[Any]       							=				[[''' '''.join([processor.tokenizer.unk_token]				) * seq_length]] * batch_size

              # Generate dummy bounding boxes
              lowercase		:Optional[int]       							=				[[[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)
              lowercase		:Tuple       							=				self._generate_dummy_images(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)

              lowercase		:List[str]       							=				dict(
                  processor(
                      snake_case__  ,     text=snake_case__  ,     boxes=snake_case__  ,     return_tensors=snake_case__  ,     )				)

              return inputs





"""simple docstring"""



from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def       lowerCamelCase					()     ->							tuple[list[int], int]:
       lowercase		:Any       							=				[randint(-1000				,				1000) for i in range(10)]
       lowercase		:Any       							=				randint(-5000				,				5000)
       return (arr, r)


UpperCAmelCase      		=			make_dataset()






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, ...]:
       for triplet in permutations(a_				,				3):
              if sum(a_) == target:
                     return tuple(sorted(a_))
       return (0, 0, 0)






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, int, int]:
       arr.sort()
       lowercase		:Union[str, Any]       							=				len(a_)
       for i in range(n - 1):
              lowercase							,  lowercase		:Union[str, Any]       							=				i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def       lowerCamelCase					()     ->							tuple[float, float]:
       lowercase		:int       							=				'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       lowercase		:Optional[Any]       							=				'''
triplet_sum1(*dataset)
'''
       lowercase		:Union[str, Any]       							=				'''
triplet_sum2(*dataset)
'''
       lowercase		:Dict       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       lowercase		:Optional[int]       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       return (min(a_), min(a_))


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 UpperCAmelCase      		=			solution_times()
 print(F"""The time for naive implementation is {times[0]}.""")
 print(F"""The time for optimized implementation is {times[1]}.""")

