



'''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


SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)

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


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Tuple     =       "layoutlmv3"



       def __init__(					self  ,UpperCAmelCase_=50_265  ,UpperCAmelCase_=768  ,UpperCAmelCase_=12  ,UpperCAmelCase_=12  ,UpperCAmelCase_=3_072  ,UpperCAmelCase_="gelu"  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=0.1  ,UpperCAmelCase_=512  ,UpperCAmelCase_=2  ,UpperCAmelCase_=0.02  ,UpperCAmelCase_=1E-5  ,UpperCAmelCase_=1  ,UpperCAmelCase_=0  ,UpperCAmelCase_=2  ,UpperCAmelCase_=1_024  ,UpperCAmelCase_=128  ,UpperCAmelCase_=128  ,UpperCAmelCase_=True  ,UpperCAmelCase_=32  ,UpperCAmelCase_=128  ,UpperCAmelCase_=64  ,UpperCAmelCase_=256  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=224  ,UpperCAmelCase_=3  ,UpperCAmelCase_=16  ,UpperCAmelCase_=None  ,**UpperCAmelCase_  ,)	->				int:
           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_  ,)
           lowercase__									=    max_ad_position_embeddings
           lowercase__									=    coordinate_size
           lowercase__									=    shape_size
           lowercase__									=    has_relative_attention_bias
           lowercase__									=    rel_pos_bins
           lowercase__									=    max_rel_pos
           lowercase__									=    has_spatial_attention_bias
           lowercase__									=    rel_ad_pos_bins
           lowercase__									=    max_rel_ad_pos
           lowercase__									=    text_embed
           lowercase__									=    visual_embed
           lowercase__									=    input_size
           lowercase__									=    num_channels
           lowercase__									=    patch_size
           lowercase__									=    classifier_dropout





class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Optional[Any]     =       version.parse("1.12" )



       @property
       def 				_a   (					self   )	->				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 				_a   (					self   )	->				float:
           return 1E-5



       @property
       def 				_a   (					self   )	->				int:
           return 12







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = -1  ,UpperCAmelCase_ = -1  ,UpperCAmelCase_ = False  ,UpperCAmelCase_ = None  ,UpperCAmelCase_ = 3  ,UpperCAmelCase_ = 40  ,UpperCAmelCase_ = 40  ,)	->				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
           lowercase__									=    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
           lowercase__									=    processor.tokenizer.num_special_tokens_to_add(UpperCAmelCase_   )
           lowercase__									=    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
           lowercase__									=    [[" ".join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

           # Generate dummy bounding boxes
           lowercase__									=    [[[48, 84, 73, 128]]] * 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__									=    self._generate_dummy_images(UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_   )

           lowercase__									=    dict(
               processor(
                   UpperCAmelCase_  ,text=UpperCAmelCase_  ,boxes=UpperCAmelCase_  ,return_tensors=UpperCAmelCase_  ,)   )

           return inputs









'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    int = 50_000_000							):





    '''simple docstring'''


    lowercase__									=    set()
    lowercase__									=    int((limit - 24) ** (1 / 2)							)

    lowercase__									=    set(range(3  ,prime_square_limit + 1  ,2							)							)
    primes.add(2							)
    for p in range(3  ,prime_square_limit + 1  ,2							):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p  ,prime_square_limit + 1  ,_snake_case							)							)							)

    for primea in primes:
        lowercase__									=    primea * primea
        for primea in primes:
            lowercase__									=    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                lowercase__									=    primea * primea * primea * primea
                lowercase__									=    square + cube + tetr
                if total >= limit:
                    break
                ret.add(_snake_case							)

    return len(_snake_case							)


if __name__ == "__main__":
 print(f'''{solution() = }''')




