




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      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						{
    '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      			=						{
    '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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]				)  ->							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_										=					1000
    UpperCAmelCase_										=					json.load(open(hf_hub_download(__UpperCamelCase	,					__UpperCamelCase	,					repo_type='''dataset'''				)	,					'''r'''				)				)
    UpperCAmelCase_										=					{int(__UpperCamelCase				): v for k, v in idalabel.items()}

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




def  SCREAMING_SNAKE_CASE     (	)  ->							List[Any]:
    UpperCAmelCase_										=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase_										=					Image.open(requests.get(__UpperCamelCase	,					stream=__UpperCamelCase				).raw				)
    return im




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					CONFIG_MAP[model_name]['''image_size''']
    UpperCAmelCase_										=					EfficientNetImageProcessor(
        size={'''height''': size, '''width''': size}	,					image_mean=[0.485, 0.456, 0.406]	,					image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163]	,					do_center_crop=__UpperCamelCase	,					)
    return preprocessor




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							Any:
    UpperCAmelCase_										=					[v.split('''_'''				)[0].split('''block'''				)[1] for v in original_param_names if v.startswith('''block'''				)]
    UpperCAmelCase_										=					sorted(set(__UpperCamelCase				)				)
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    UpperCAmelCase_										=					{b: str(__UpperCamelCase				) for b, i in zip(__UpperCamelCase	,					range(__UpperCamelCase				)				)}

    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						Union[str, Any]				)  ->							Any:
    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(__UpperCamelCase				).permute(3	,					2	,					0	,					1				)
        elif "depthwise_kernel" in key:
            UpperCAmelCase_										=					torch.from_numpy(__UpperCamelCase				).permute(2	,					3	,					0	,					1				)
        elif "kernel" in key:
            UpperCAmelCase_										=					torch.from_numpy(np.transpose(__UpperCamelCase				)				)
        else:
            UpperCAmelCase_										=					torch.from_numpy(__UpperCamelCase				)

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




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						Union[str, Any]				)  ->							Optional[int]:
    UpperCAmelCase_										=					model_classes[model_name](
        include_top=__UpperCamelCase	,					weights='''imagenet'''	,					input_tensor=__UpperCamelCase	,					input_shape=__UpperCamelCase	,					pooling=__UpperCamelCase	,					classes=1000	,					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(__UpperCamelCase				)
    UpperCAmelCase_										=					EfficientNetForImageClassification(__UpperCamelCase				).eval()
    UpperCAmelCase_										=					hf_model.state_dict()

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

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

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

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

    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(__UpperCamelCase				)
        hf_model.push_to_hub(__UpperCamelCase				)


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











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						float	,					__UpperCamelCase      :						float				)  ->							float:

    if density <= 0:
        raise ValueError('''Impossible fluid density'''				)
    if bulk_modulus <= 0:
        raise ValueError('''Impossible bulk modulus'''				)

    return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()





