


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			:					int         =	logging.get_logger(__name__)

lowerCamelCase			:					Union[str, 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""": 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       __lowerCAmelCase      (						__snake_case       ):
     __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(__snake_case					, __snake_case					, repo_type="dataset"       )					, "r"       )       )
     __lowerCAmelCase								=					{int(__snake_case       ): v for k, v in idalabel.items()}

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





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





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					CONFIG_MAP[model_name]["image_size"]
     __lowerCAmelCase								=					EfficientNetImageProcessor(
         size={"height": size, "width": size}					, image_mean=[0.485, 0.456, 0.406]					, image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163]					, do_center_crop=__snake_case					, )
     return preprocessor





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

     __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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     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(__snake_case       ).permute(3					, 2					, 0					, 1       )
          elif "depthwise_kernel" in key:
               __lowerCAmelCase								=					torch.from_numpy(__snake_case       ).permute(2					, 3					, 0					, 1       )
          elif "kernel" in key:
               __lowerCAmelCase								=					torch.from_numpy(np.transpose(__snake_case       )       )
          else:
               __lowerCAmelCase								=					torch.from_numpy(__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_(__snake_case       )





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					model_classes[model_name](
         include_top=__snake_case					, weights="imagenet"					, input_tensor=__snake_case					, input_shape=__snake_case					, pooling=__snake_case					, 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(__snake_case       )
     __lowerCAmelCase								=					EfficientNetForImageClassification(__snake_case       ).eval()
     __lowerCAmelCase								=					hf_model.state_dict()

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

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

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          __lowerCAmelCase								=					hf_model(**__snake_case       )
     __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(__snake_case       )
     __lowerCAmelCase								=					np.expand_dims(__snake_case					, axis=0       )
     __lowerCAmelCase								=					original_model.predict(__snake_case       )

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

     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(__snake_case       )
          hf_model.push_to_hub(__snake_case       )


if __name__ == "__main__":
 lowerCamelCase			:					str         =	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			:					int         =	parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)














lowerCamelCase			:					str         =	'''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase			:					Tuple         =	[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase			:					Dict         =	{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






