




def SCREAMING_SNAKE_CASE       ( lowercase_ = 100		)     -> int:



       """simple docstring"""

       A__   				=					set()

       A__   				=					0

       A__   				=					n + 1  # maximum limit

       for a in range(2				,       lowercase_		):
              for b in range(2				,       lowercase_		):
                     A__   				=					a**b  # calculates the current power
                     collect_powers.add(lowercase_		)  # adds the result to the set
       return len(lowercase_		)


if __name__ == "__main__":
  print("""Number of terms """, solution(int(str(input()).strip())))








import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase			:	str							 =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCamelCase			:	int							 =     []
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					state_dict.pop(lowercase_		)
       A__   				=					val







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     A__   				=					key.replace('''backbone.0.body'''				,       '''backbone.conv_encoder.model'''		)
                     A__   				=					value
              else:
                     A__   				=					value

       return new_state_dict







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[Any]:



       """simple docstring"""

       A__   				=					''''''

       # first: transformer encoder
       for i in range(6		):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              A__   				=					state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
              A__   				=					state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""		)
              # next, add query, keys and values (in that order) to the state dict
              A__   				=					in_proj_weight[:256, :]
              A__   				=					in_proj_bias[:256]
              A__   				=					in_proj_weight[256:512, :]
              A__   				=					in_proj_bias[256:512]
              A__   				=					in_proj_weight[-256:, :]
              A__   				=					in_proj_bias[-256:]
       # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
       for i in range(6		):
              # read in weights + bias of input projection layer of self-attention
              A__   				=					state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
              A__   				=					state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""		)
              # next, add query, keys and values (in that order) to the state dict
              A__   				=					in_proj_weight[:256, :]
              A__   				=					in_proj_bias[:256]
              A__   				=					in_proj_weight[256:512, :]
              A__   				=					in_proj_bias[256:512]
              A__   				=					in_proj_weight[-256:, :]
              A__   				=					in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              A__   				=					state_dict.pop(
                  f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
              A__   				=					state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""		)
              # next, add query, keys and values (in that order) of cross-attention to the state dict
              A__   				=					in_proj_weight_cross_attn[:256, :]
              A__   				=					in_proj_bias_cross_attn[:256]
              A__   				=					in_proj_weight_cross_attn[256:512, :]
              A__   				=					in_proj_bias_cross_attn[256:512]
              A__   				=					in_proj_weight_cross_attn[-256:, :]
              A__   				=					in_proj_bias_cross_attn[-256:]







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Dict:



       """simple docstring"""

       A__				,							A__   				=					image.size
       A__   				=					max(lowercase_				,       lowercase_		)
       A__   				=					800 if '''detection''' in checkpoint_url else 1_000
       A__   				=					target_max_size / current_max_size
       A__   				=					image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

       return resized_image







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Tuple:



       """simple docstring"""

       A__   				=					F.to_tensor(lowercase_		)
       A__   				=					F.normalize(lowercase_				,       mean=[0.4_85, 0.4_56, 0.4_06]				,       std=[0.2_29, 0.2_24, 0.2_25]		)
       return image







@torch.no_grad()
def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       logger.info('''Converting model...'''		)

       # load original state dict
       A__   				=					torch.hub.load_state_dict_from_url(lowercase_				,       map_location='''cpu'''		)
       # rename keys
       for src, dest in rename_keys:
              rename_key(lowercase_				,       lowercase_				,       lowercase_		)
       A__   				=					rename_backbone_keys(lowercase_		)
       # query, key and value matrices need special treatment
       read_in_q_k_v(lowercase_		)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       A__   				=					'''model.'''
       for key in state_dict.copy().keys():
              if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
                     A__   				=					state_dict.pop(lowercase_		)
                     A__   				=					val
    # create HuggingFace model and load state dict
       A__   				=					TableTransformerConfig(
           backbone='''resnet18'''				,       mask_loss_coefficient=1				,       dice_loss_coefficient=1				,       ce_loss_coefficient=1				,       bbox_loss_coefficient=5				,       giou_loss_coefficient=2				,       eos_coefficient=0.4				,       class_cost=1				,       bbox_cost=5				,       giou_cost=2				,       )

       if "detection" in checkpoint_url:
              A__   				=					15
              A__   				=					2
              A__   				=					{0: '''table''', 1: '''table rotated'''}
              A__   				=					idalabel
              A__   				=					{v: k for k, v in idalabel.items()}
       else:
              A__   				=					125
              A__   				=					6
              A__   				=					{
                  0: '''table''',
                  1: '''table column''',
                  2: '''table row''',
                  3: '''table column header''',
                  4: '''table projected row header''',
                  5: '''table spanning cell''',
              }
              A__   				=					idalabel
              A__   				=					{v: k for k, v in idalabel.items()}

       A__   				=					DetrImageProcessor(
           format='''coco_detection'''				,       max_size=800 if '''detection''' in checkpoint_url else 1_000		)
       A__   				=					TableTransformerForObjectDetection(lowercase_		)
       model.load_state_dict(lowercase_		)
       model.eval()

       # verify our conversion
       A__   				=					'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
       A__   				=					hf_hub_download(repo_id='''nielsr/example-pdf'''				,       repo_type='''dataset'''				,       filename=lowercase_		)
       A__   				=					Image.open(lowercase_		).convert('''RGB'''		)
       A__   				=					normalize(resize(lowercase_				,       lowercase_		)		).unsqueeze(0		)

       A__   				=					model(lowercase_		)

       if "detection" in checkpoint_url:
              A__   				=					(1, 15, 3)
              A__   				=					torch.tensor(
                  [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]		)
              A__   				=					torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]		)

       else:
              A__   				=					(1, 125, 7)
              A__   				=					torch.tensor(
                  [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]		)
              A__   				=					torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]		)

       assert outputs.logits.shape == expected_shape
       assert torch.allclose(outputs.logits[0, :3, :3]				,       lowercase_				,       atol=1E-4		)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,       lowercase_				,       atol=1E-4		)
       print('''Looks ok!'''		)

       if pytorch_dump_folder_path is not None:
              # Save model and image processor
              logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""		)
              Path(lowercase_		).mkdir(exist_ok=lowercase_		)
              model.save_pretrained(lowercase_		)
              image_processor.save_pretrained(lowercase_		)

       if push_to_hub:
              # Push model to HF hub
              logger.info('''Pushing model to the hub...'''		)
              A__   				=					(
                  '''microsoft/table-transformer-detection'''
                  if '''detection''' in checkpoint_url
                  else '''microsoft/table-transformer-structure-recognition'''
              )
              model.push_to_hub(lowercase_		)
              image_processor.push_to_hub(lowercase_		)


if __name__ == "__main__":
  _lowerCamelCase			:	str							 =     argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_url""",
      default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
      type=str,
      choices=[
          """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
          """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
      ],
      help="""URL of the Table Transformer checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )
  _lowerCamelCase			:	str							 =     parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


