





from math import factorial

lowercase_					     =	{str(d): factorial(d) for d in range(10)}




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     int:
  return sum(DIGIT_FACTORIAL[d] for d in str(_SCREAMING_SNAKE_CASE	)	)




def     __UpperCamelCase						()       ->     int:
  lowercase__			    =						7 * factorial(9	) + 1
  return sum(i for i in range(3	,		_SCREAMING_SNAKE_CASE	) if sum_of_digit_factorial(_SCREAMING_SNAKE_CASE	) == i	)


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









import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

lowercase_					     =	{
    """b0""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1_408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1_536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1_792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2_304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2_560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
  lowercase__			    =						EfficientNetConfig()
  lowercase__			    =						CONFIG_MAP[model_name]['hidden_dim']
  lowercase__			    =						CONFIG_MAP[model_name]['width_coef']
  lowercase__			    =						CONFIG_MAP[model_name]['depth_coef']
  lowercase__			    =						CONFIG_MAP[model_name]['image_size']
  lowercase__			    =						CONFIG_MAP[model_name]['dropout_rate']
  lowercase__			    =						CONFIG_MAP[model_name]['dw_padding']

  lowercase__			    =						'huggingface/label-files'
  lowercase__			    =						'imagenet-1k-id2label.json'
  lowercase__			    =						1000
  lowercase__			    =						json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		repo_type='dataset'	)	,		'r'	)	)
  lowercase__			    =						{int(_SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}

  lowercase__			    =						idalabel
  lowercase__			    =						{v: k for k, v in idalabel.items()}
  return config




def     __UpperCamelCase						()       ->     Tuple:
  lowercase__			    =						'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__			    =						Image.open(requests.get(_SCREAMING_SNAKE_CASE	,		stream=_SCREAMING_SNAKE_CASE	).raw	)
  return im




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Optional[Any]:
  lowercase__			    =						CONFIG_MAP[model_name]['image_size']
  lowercase__			    =						EfficientNetImageProcessor(
      size={'height': size, 'width': size}	,		image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	,		image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]	,		do_center_crop=_SCREAMING_SNAKE_CASE	,		)
  return preprocessor




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
  lowercase__			    =						sorted(set(_SCREAMING_SNAKE_CASE	)	)
  lowercase__			    =						len(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						{b: str(_SCREAMING_SNAKE_CASE	) for b, i in zip(_SCREAMING_SNAKE_CASE	,		range(_SCREAMING_SNAKE_CASE	)	)}

  lowercase__			    =						[]
  rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')	)
  rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')	)
  rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')	)
  rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')	)
  rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')	)

  for b in block_names:
    lowercase__			    =						block_name_mapping[b]
    rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")	)
    rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")	)
    rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")	)
    rename_keys.append(
        (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")	)
    rename_keys.append(
        (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")	)
    rename_keys.append(
        (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")	)
    rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")	)
    rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")	)
    rename_keys.append(
        (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")	)
    rename_keys.append(
        (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")	)

    rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")	)
    rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")	)
    rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")	)
    rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")	)
    rename_keys.append(
        (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")	)
    rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")	)
    rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")	)
    rename_keys.append(
        (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")	)
    rename_keys.append(
        (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")	)

  rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')	)
  rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')	)
  rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')	)
  rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')	)
  rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')	)

  lowercase__			    =						{}
  for item in rename_keys:
    if item[0] in original_param_names:
      lowercase__			    =						'efficientnet.' + item[1]

  lowercase__			    =						'classifier.weight'
  lowercase__			    =						'classifier.bias'
  return key_mapping




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  for key, value in tf_params.items():
    if "normalization" in key:
      continue

    lowercase__			    =						key_mapping[key]
    if "_conv" in key and "kernel" in key:
      lowercase__			    =						torch.from_numpy(_SCREAMING_SNAKE_CASE	).permute(3	,		2	,		0	,		1	)
    elif "depthwise_kernel" in key:
      lowercase__			    =						torch.from_numpy(_SCREAMING_SNAKE_CASE	).permute(2	,		3	,		0	,		1	)
    elif "kernel" in key:
      lowercase__			    =						torch.from_numpy(np.transpose(_SCREAMING_SNAKE_CASE	)	)
    else:
      lowercase__			    =						torch.from_numpy(_SCREAMING_SNAKE_CASE	)

    # Replace HF parameters with original TF model parameters
    assert hf_params[hf_key].shape == new_hf_value.shape
    hf_params[hf_key].copy_(_SCREAMING_SNAKE_CASE	)




@torch.no_grad()
def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						model_classes[model_name](
      include_top=_SCREAMING_SNAKE_CASE	,		weights='imagenet'	,		input_tensor=_SCREAMING_SNAKE_CASE	,		input_shape=_SCREAMING_SNAKE_CASE	,		pooling=_SCREAMING_SNAKE_CASE	,		classes=1000	,		classifier_activation='softmax'	,		)

  lowercase__			    =						original_model.trainable_variables
  lowercase__			    =						original_model.non_trainable_variables
  lowercase__			    =						{param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    lowercase__			    =						param.numpy()
  lowercase__			    =						list(tf_params.keys()	)

  # Load HuggingFace model
  lowercase__			    =						get_efficientnet_config(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						EfficientNetForImageClassification(_SCREAMING_SNAKE_CASE	).eval()
  lowercase__			    =						hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print('Converting parameters...'	)
  lowercase__			    =						rename_keys(_SCREAMING_SNAKE_CASE	)
  replace_params(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)

  # Initialize preprocessor and preprocess input image
  lowercase__			    =						convert_image_processor(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						preprocessor(images=prepare_img()	,		return_tensors='pt'	)

  # HF model inference
  hf_model.eval()
  with torch.no_grad():
    lowercase__			    =						hf_model(**_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						outputs.logits.detach().numpy()

  # Original model inference
  lowercase__			    =						False
  lowercase__			    =						CONFIG_MAP[model_name]['image_size']
  lowercase__			    =						prepare_img().resize((image_size, image_size)	,		resample=PIL.Image.NEAREST	)
  lowercase__			    =						image.img_to_array(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						np.expand_dims(_SCREAMING_SNAKE_CASE	,		axis=0	)
  lowercase__			    =						original_model.predict(_SCREAMING_SNAKE_CASE	)

  # Check whether original and HF model outputs match  -> np.allclose
  assert np.allclose(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		atol=1E-3	), "The predicted logits are not the same."
  print('Model outputs match!'	)

  if save_model:
    # Create folder to save model
    if not os.path.isdir(_SCREAMING_SNAKE_CASE	):
      os.mkdir(_SCREAMING_SNAKE_CASE	)
    # Save converted model and image processor
    hf_model.save_pretrained(_SCREAMING_SNAKE_CASE	)
    preprocessor.save_pretrained(_SCREAMING_SNAKE_CASE	)

  if push_to_hub:
    # Push model and image processor to hub
    print(F"""Pushing converted {model_name} to the hub..."""	)
    lowercase__			    =						F"""efficientnet-{model_name}"""
    preprocessor.push_to_hub(_SCREAMING_SNAKE_CASE	)
    hf_model.push_to_hub(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""b0""",
          type=str,
          help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default="""hf_model""",
          type=str,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
      parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

      lowercase_					     =	parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


