




'''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()
A     =logging.get_logger(__name__)

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

A     ={
    'b0': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_24,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_40,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 14_08,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_60,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 15_36,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_00,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 17_92,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_80,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 20_48,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_56,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 23_04,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_28,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 25_60,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_00,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}





def 						snake_case_							(_a	:							List[str]     ):
    UpperCAmelCase			       =		EfficientNetConfig()
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''hidden_dim''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''width_coef''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''depth_coef''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''image_size''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''dropout_rate''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''dw_padding''']

    UpperCAmelCase			       =		'''huggingface/label-files'''
    UpperCAmelCase			       =		'''imagenet-1k-id2label.json'''
    UpperCAmelCase			       =		1_0_0_0
    UpperCAmelCase			       =		json.load(open(hf_hub_download(_a    ,   _a    ,   repo_type='''dataset'''     )    ,   '''r'''     )     )
    UpperCAmelCase			       =		{int(_a     ): v for k, v in idalabel.items()}

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





def 						snake_case_							():
    UpperCAmelCase			       =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase			       =		Image.open(requests.get(_a    ,   stream=_a     ).raw     )
    return im





def 						snake_case_							(_a	:							str     ):
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''image_size''']
    UpperCAmelCase			       =		EfficientNetImageProcessor(
        size={'''height''': size, '''width''': size}    ,   image_mean=[0.485, 0.456, 0.406]    ,   image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]    ,   do_center_crop=_a    ,   )
    return preprocessor





def 						snake_case_							(_a	:							Optional[Any]     ):
    UpperCAmelCase			       =		[v.split('''_'''     )[0].split('''block'''     )[1] for v in original_param_names if v.startswith('''block'''     )]
    UpperCAmelCase			       =		sorted(set(_a     )     )
    UpperCAmelCase			       =		len(_a     )
    UpperCAmelCase			       =		{b: str(_a     ) for b, i in zip(_a    ,   range(_a     )     )}

    UpperCAmelCase			       =		[]
    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:
        UpperCAmelCase			       =		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''')     )

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

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





def 						snake_case_							(_a	:							Dict    ,   _a	:							List[str]    ,   _a	:							Dict     ):
    for key, value in tf_params.items():
        if "normalization" in key:
            continue

        UpperCAmelCase			       =		key_mapping[key]
        if "_conv" in key and "kernel" in key:
            UpperCAmelCase			       =		torch.from_numpy(_a     ).permute(3    ,   2    ,   0    ,   1     )
        elif "depthwise_kernel" in key:
            UpperCAmelCase			       =		torch.from_numpy(_a     ).permute(2    ,   3    ,   0    ,   1     )
        elif "kernel" in key:
            UpperCAmelCase			       =		torch.from_numpy(np.transpose(_a     )     )
        else:
            UpperCAmelCase			       =		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 						snake_case_							(_a	:							Optional[Any]    ,   _a	:							List[str]    ,   _a	:							Optional[int]    ,   _a	:							Dict     ):
    UpperCAmelCase			       =		model_classes[model_name](
        include_top=_a    ,   weights='''imagenet'''    ,   input_tensor=_a    ,   input_shape=_a    ,   pooling=_a    ,   classes=1_0_0_0    ,   classifier_activation='''softmax'''    ,   )

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

    # Load HuggingFace model
    UpperCAmelCase			       =		get_efficientnet_config(_a     )
    UpperCAmelCase			       =		EfficientNetForImageClassification(_a     ).eval()
    UpperCAmelCase			       =		hf_model.state_dict()

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

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

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

    # Original model inference
    UpperCAmelCase			       =		False
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''image_size''']
    UpperCAmelCase			       =		prepare_img().resize((image_size, image_size)    ,   resample=PIL.Image.NEAREST     )
    UpperCAmelCase			       =		image.img_to_array(_a     )
    UpperCAmelCase			       =		np.expand_dims(_a    ,   axis=0     )
    UpperCAmelCase			       =		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..."     )
        UpperCAmelCase			       =		F"efficientnet-{model_name}"
        preprocessor.push_to_hub(_a     )
        hf_model.push_to_hub(_a     )


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

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






"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_				):

		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'float' object cannot be interpreted as an integer"""				)
		if isinstance(UpperCamelCase_  ,				UpperCamelCase_				):
				raise TypeError("""'str' object cannot be interpreted as an integer"""				)

		if num == 0:
				return "0b0"

		__SCREAMING_SNAKE_CASE            = False

		if num < 0:
				__SCREAMING_SNAKE_CASE            = True
				__SCREAMING_SNAKE_CASE            = -num

		__SCREAMING_SNAKE_CASE            = []
		while num > 0:
				binary.insert(0  ,				num % 2				)
				num >>= 1

		if negative:
				return "-0b" + "".join(str(UpperCamelCase_				) for e in binary				)

		return "0b" + "".join(str(UpperCamelCase_				) for e in binary				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




