



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


_lowercase		:				List[Any]      		=logging.get_logger(__name__)

_lowercase		:				Dict      		={
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					"layoutlmv3"

						def __init__(       self ,       __lowercase=5_0_2_6_5 ,       __lowercase=7_6_8 ,       __lowercase=1_2 ,       __lowercase=1_2 ,       __lowercase=3_0_7_2 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=5_1_2 ,       __lowercase=2 ,       __lowercase=0.0_2 ,       __lowercase=1E-5 ,       __lowercase=1 ,       __lowercase=0 ,       __lowercase=2 ,       __lowercase=1_0_2_4 ,       __lowercase=1_2_8 ,       __lowercase=1_2_8 ,       __lowercase=True ,       __lowercase=3_2 ,       __lowercase=1_2_8 ,       __lowercase=6_4 ,       __lowercase=2_5_6 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=2_2_4 ,       __lowercase=3 ,       __lowercase=1_6 ,       __lowercase=None ,       **__lowercase ,       ) ->				Optional[int]:
									"""simple docstring"""




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




class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Union[str, Any]      =					version.parse("1.12"		)

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Mapping[str, Mapping[int, str]]:
									"""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 							SCREAMING_SNAKE_CASE__(       self  ) ->				float:
									"""simple docstring"""




									return 1E-5

						@property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									return 1_2

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase = -1 ,       __lowercase = -1 ,       __lowercase = False ,       __lowercase = None ,       __lowercase = 3 ,       __lowercase = 4_0 ,       __lowercase = 4_0 ,       ) ->				Mapping[str, Any]:
									"""simple docstring"""




									setattr(processor.image_processor ,       """apply_ocr""" ,       __lowercase  )

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

									# Generate dummy bounding boxes
									a__       :						Union[str, Any]							=  [[[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)
									a__       :						Any							=  self._generate_dummy_images(__lowercase ,       __lowercase ,       __lowercase ,       __lowercase  )

									a__       :						Optional[int]							=  dict(
									    processor(
									        __lowercase ,       text=__lowercase ,       boxes=__lowercase ,       return_tensors=__lowercase ,       )  )

									return inputs









from __future__ import annotations

import math

def      lowerCAmelCase_					(  _lowercase    :       int)							->	list[int]:







			"""simple docstring"""

			if num <= 0:
						a__       :						Tuple							=  F'''{num}: Invalid input, please enter a positive integer.'''
						raise ValueError(_lowercase)

			a__       :						List[Any]							=  [True] * (num + 1)
			a__       :						List[str]							=  []
			a__       :						List[Any]							=  2
			a__       :						Optional[int]							=  int(math.sqrt(_lowercase))

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(_lowercase)

									# Set multiples of start be False
									for i in range(start * start						,       num + 1						,       _lowercase):
												if sieve[i] is True:
															a__       :						Optional[int]							=  False

						start += 1

			for j in range(end + 1						,       num + 1):
						if sieve[j] is True:
									prime.append(_lowercase)

			return prime


if __name__ == "__main__":
		print(prime_sieve(int(input("Enter a positive integer: ").strip())))




