
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 							_A       (						lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     lowerCAmelCase__          =			EfficientNetConfig()
     lowerCAmelCase__          =			CONFIG_MAP[model_name]["hidden_dim"]
     lowerCAmelCase__          =			CONFIG_MAP[model_name]["width_coef"]
     lowerCAmelCase__          =			CONFIG_MAP[model_name]["depth_coef"]
     lowerCAmelCase__          =			CONFIG_MAP[model_name]["image_size"]
     lowerCAmelCase__          =			CONFIG_MAP[model_name]["dropout_rate"]
     lowerCAmelCase__          =			CONFIG_MAP[model_name]["dw_padding"]

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

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



def 							_A       (						):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__          =			CONFIG_MAP[model_name]["image_size"]
     lowerCAmelCase__          =			EfficientNetImageProcessor(
         size={"height": size, "width": size}       ,    image_mean=[0.485, 0.456, 0.406]       ,    image_std=[0.47853944, 0.4732864, 0.47434163]       ,    do_center_crop=_A       ,    )
     return preprocessor



def 							_A       (						lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

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

     lowerCAmelCase__          =			[]
     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:
          lowerCAmelCase__          =			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")      )

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

     lowerCAmelCase__          =			"classifier.weight"
     lowerCAmelCase__          =			"classifier.bias"
     return key_mapping



def 							_A       (						lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: str      ):
     """simple docstring"""

     for key, value in tf_params.items():
          if "normalization" in key:
               continue

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

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



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     lowerCAmelCase__          =			model_classes[model_name](
         include_top=_A       ,    weights="imagenet"       ,    input_tensor=_A       ,    input_shape=_A       ,    pooling=_A       ,    classes=1000       ,    classifier_activation="softmax"       ,    )

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

     # Load HuggingFace model
     lowerCAmelCase__          =			get_efficientnet_config(_A      )
     lowerCAmelCase__          =			EfficientNetForImageClassification(_A      ).eval()
     lowerCAmelCase__          =			hf_model.state_dict()

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

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

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

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

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

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


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)






import random



def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			[], [], []
     for element in data:
          if element < pivot:
               less.append(lowerCAmelCase_      )
          elif element > pivot:
               greater.append(lowerCAmelCase_      )
          else:
               equal.append(lowerCAmelCase_      )
     return less, equal, greater






def 							_A       (						lowerCAmelCase_					: list       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     if index >= len(lowerCAmelCase_      ) or index < 0:
          return None

     lowerCAmelCase__          =			items[random.randint(0       ,    len(lowerCAmelCase_      ) - 1      )]
     lowerCAmelCase__          =			0
     lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			_partition(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )
     lowerCAmelCase__          =			len(lowerCAmelCase_      )

     # index is the pivot
     if m <= index < m + count:
          return pivot
     # must be in smaller
     elif m > index:
          return quick_select(lowerCAmelCase_       ,    lowerCAmelCase_      )
     # must be in larger
     else:
          return quick_select(lowerCAmelCase_       ,    index - (m + count)      )
