"""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()
__lowerCAmelCase       :			List[str]   	=logging.get_logger(__name__)

__lowerCAmelCase       :			List[Any]   	={
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

__lowerCAmelCase       :			Optional[Any]   	={
    """b0""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 2_2_4,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 2_4_0,
        """dropout_rate""": 0.2,
        """dw_padding""": [1_6],
    },
    """b2""": {
        """hidden_dim""": 1_4_0_8,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 2_6_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 1_6],
    },
    """b3""": {
        """hidden_dim""": 1_5_3_6,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 3_0_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 1_8],
    },
    """b4""": {
        """hidden_dim""": 1_7_9_2,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 3_8_0,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_0_4_8,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 4_5_6,
        """dropout_rate""": 0.4,
        """dw_padding""": [1_3, 2_7],
    },
    """b6""": {
        """hidden_dim""": 2_3_0_4,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 5_2_8,
        """dropout_rate""": 0.5,
        """dw_padding""": [3_1],
    },
    """b7""": {
        """hidden_dim""": 2_5_6_0,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 6_0_0,
        """dropout_rate""": 0.5,
        """dw_padding""": [1_8],
    },
}


def      UpperCAmelCase__			(							lowerCAmelCase__	:Any						)					->       Tuple:
      '''simple docstring'''


      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								=      1_0_0_0
      lowercase								=      json.load(open(hf_hub_download(lowerCAmelCase__   ,						lowerCAmelCase__   ,						repo_type="""dataset"""						)   ,						"""r"""						)						)
      lowercase								=      {int(lowerCAmelCase__						): v for k, v in idalabel.items()}

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


def      UpperCAmelCase__			(							)					->       List[str]:
      '''simple docstring'''


      lowercase								=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowercase								=      Image.open(requests.get(lowerCAmelCase__   ,						stream=lowerCAmelCase__						).raw						)
      return im


def      UpperCAmelCase__			(							lowerCAmelCase__	:int						)					->       Tuple:
      '''simple docstring'''


      lowercase								=      CONFIG_MAP[model_name]["""image_size"""]
      lowercase								=      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__	:List[str]						)					->       str:
      '''simple docstring'''


      lowercase								=      [v.split("""_"""						)[0].split("""block"""						)[1] for v in original_param_names if v.startswith("""block"""						)]
      lowercase								=      sorted(set(lowerCAmelCase__						)						)
      lowercase								=      len(lowerCAmelCase__						)
      lowercase								=      {b: str(lowerCAmelCase__						) for b, i in zip(lowerCAmelCase__   ,						range(lowerCAmelCase__						)						)}

      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__			(							lowerCAmelCase__	:Optional[int]   ,						lowerCAmelCase__	:List[str]   ,						lowerCAmelCase__	:str						)					->       Optional[int]:
      '''simple docstring'''


      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(lowerCAmelCase__						).permute(3   ,						2   ,						0   ,						1						)
            elif "depthwise_kernel" in key:
                  lowercase								=      torch.from_numpy(lowerCAmelCase__						).permute(2   ,						3   ,						0   ,						1						)
            elif "kernel" in key:
                  lowercase								=      torch.from_numpy(np.transpose(lowerCAmelCase__						)						)
            else:
                  lowercase								=      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__	:Union[str, Any]   ,						lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:Optional[int]						)					->       List[str]:
      '''simple docstring'''


      lowercase								=      model_classes[model_name](
          include_top=lowerCAmelCase__   ,						weights="""imagenet"""   ,						input_tensor=lowerCAmelCase__   ,						input_shape=lowerCAmelCase__   ,						pooling=lowerCAmelCase__   ,						classes=1_0_0_0   ,						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(lowerCAmelCase__						)
      lowercase								=      EfficientNetForImageClassification(lowerCAmelCase__						).eval()
      lowercase								=      hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print("""Converting parameters..."""						)
      lowercase								=      rename_keys(lowerCAmelCase__						)
      replace_params(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)

      # Initialize preprocessor and preprocess input image
      lowercase								=      convert_image_processor(lowerCAmelCase__						)
      lowercase								=      preprocessor(images=prepare_img()   ,						return_tensors="""pt"""						)

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            lowercase								=      hf_model(**lowerCAmelCase__						)
      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(lowerCAmelCase__						)
      lowercase								=      np.expand_dims(lowerCAmelCase__   ,						axis=0						)
      lowercase								=      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...'						)
            lowercase								=      f'efficientnet-{model_name}'
            preprocessor.push_to_hub(lowerCAmelCase__						)
            hf_model.push_to_hub(lowerCAmelCase__						)


if __name__ == "__main__":
       __lowerCAmelCase       :			List[Any]   	=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""")

       __lowerCAmelCase       :			Tuple   	=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__	:int = 1_0**9						)					->       int:
      '''simple docstring'''


      lowercase								=      1
      lowercase								=      2

      lowercase								=      0
      lowercase								=      0
      lowercase								=      0
      while perimeter <= max_perimeter:
            perimeters_sum += perimeter

            prev_value += 2 * value
            value += prev_value

            lowercase								=      2 * value + 2 if i % 2 == 0 else 2 * value - 2
            i += 1

      return perimeters_sum


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



