
"""simple docstring"""



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()
UpperCAmelCase__          = logging.get_logger(__name__)

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

UpperCAmelCase__          = {
    'b0': {
        'hidden_dim': 1280,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 224,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1280,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 240,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 1408,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 260,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 1536,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 300,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 1792,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 380,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2048,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 456,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 2304,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 528,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 2560,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 600,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any]		)     ->					List[Any]:
  _snake_case					     =       EfficientNetConfig()
  _snake_case					     =       CONFIG_MAP[model_name]['''hidden_dim''']
  _snake_case					     =       CONFIG_MAP[model_name]['''width_coef''']
  _snake_case					     =       CONFIG_MAP[model_name]['''depth_coef''']
  _snake_case					     =       CONFIG_MAP[model_name]['''image_size''']
  _snake_case					     =       CONFIG_MAP[model_name]['''dropout_rate''']
  _snake_case					     =       CONFIG_MAP[model_name]['''dw_padding''']

  _snake_case					     =       '''huggingface/label-files'''
  _snake_case					     =       '''imagenet-1k-id2label.json'''
  _snake_case					     =       10_00
  _snake_case					     =       json.load(open(hf_hub_download(__lowerCamelCase ,					__lowerCamelCase ,					repo_type='''dataset'''		) ,					'''r'''		)		)
  _snake_case					     =       {int(__lowerCamelCase		): v for k, v in idalabel.items()}

  _snake_case					     =       idalabel
  _snake_case					     =       {v: k for k, v in idalabel.items()}
  return config
def 					_UpperCAmelCase      ( )     ->					List[Any]:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str]		)     ->					Any:
  _snake_case					     =       CONFIG_MAP[model_name]['''image_size''']
  _snake_case					     =       EfficientNetImageProcessor(
      size={'''height''': size, '''width''': size} ,					image_mean=[0.485, 0.456, 0.406] ,					image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163] ,					do_center_crop=__lowerCamelCase ,					)
  return preprocessor
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any		)     ->					List[str]:
  _snake_case					     =       [v.split('''_'''		)[0].split('''block'''		)[1] for v in original_param_names if v.startswith('''block'''		)]
  _snake_case					     =       sorted(set(__lowerCamelCase		)		)
  _snake_case					     =       len(__lowerCamelCase		)
  _snake_case					     =       {b: str(__lowerCamelCase		) for b, i in zip(__lowerCamelCase ,					range(__lowerCamelCase		)		)}

  _snake_case					     =       []
  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:
    _snake_case					     =       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''')		)

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

  _snake_case					     =       '''classifier.weight'''
  _snake_case					     =       '''classifier.bias'''
  return key_mapping
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       Optional[int]		)     ->					Optional[int]:
  for key, value in tf_params.items():
    if "normalization" in key:
      continue

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

    # Replace HF parameters with original TF model parameters
    assert hf_params[hf_key].shape == new_hf_value.shape
    hf_params[hf_key].copy_(__lowerCamelCase		)
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Union[str, Any]		)     ->					List[Any]:
  _snake_case					     =       model_classes[model_name](
      include_top=__lowerCamelCase ,					weights='''imagenet''' ,					input_tensor=__lowerCamelCase ,					input_shape=__lowerCamelCase ,					pooling=__lowerCamelCase ,					classes=10_00 ,					classifier_activation='''softmax''' ,					)

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

  # Load HuggingFace model
  _snake_case					     =       get_efficientnet_config(__lowerCamelCase		)
  _snake_case					     =       EfficientNetForImageClassification(__lowerCamelCase		).eval()
  _snake_case					     =       hf_model.state_dict()

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

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

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

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

  # Check whether original and HF model outputs match  -> np.allclose
  assert np.allclose(__lowerCamelCase ,					__lowerCamelCase ,					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(__lowerCamelCase		):
      os.mkdir(__lowerCamelCase		)
    # Save converted model and image processor
    hf_model.save_pretrained(__lowerCamelCase		)
    preprocessor.save_pretrained(__lowerCamelCase		)

  if push_to_hub:
    # Push model and image processor to hub
    print(f'''Pushing converted {model_name} to the hub...'''		)
    _snake_case					     =       f'''efficientnet-{model_name}'''
    preprocessor.push_to_hub(__lowerCamelCase		)
    hf_model.push_to_hub(__lowerCamelCase		)


if __name__ == "__main__":
 UpperCAmelCase__          = 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')

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





"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Any:
  stooge(__lowerCamelCase ,					0 ,					len(__lowerCamelCase		) - 1		)
  return arr
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       str		)     ->					int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case     ,					_snake_case					     =       arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case					     =       (int)((h - i + 1) / 3		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)

    # Recursively sort last 2/3 elements
    stooge(__lowerCamelCase ,					i + t ,					(__lowerCamelCase)		)

    # Recursively sort first 2/3 elements
    stooge(__lowerCamelCase ,					__lowerCamelCase ,					(h - t)		)


if __name__ == "__main__":
 UpperCAmelCase__          = input('Enter numbers separated by a comma:\n').strip()
 UpperCAmelCase__          = [int(item) for item in user_input.split(',')]
 print(stooge_sort(unsorted))



