





'''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()
snake_case_     :       List[str]						=  logging.get_logger(__name__)

snake_case_     :       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,
}

snake_case_     :       Optional[Any]						=  {
    "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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str   ) ->	Dict:
						UpperCAmelCase_			:      int     						=  EfficientNetConfig()
						UpperCAmelCase_			:      str     						=  CONFIG_MAP[model_name]['''hidden_dim''']
						UpperCAmelCase_			:      Any     						=  CONFIG_MAP[model_name]['''width_coef''']
						UpperCAmelCase_			:      Tuple     						=  CONFIG_MAP[model_name]['''depth_coef''']
						UpperCAmelCase_			:      List[Any]     						=  CONFIG_MAP[model_name]['''image_size''']
						UpperCAmelCase_			:      int     						=  CONFIG_MAP[model_name]['''dropout_rate''']
						UpperCAmelCase_			:      Optional[int]     						=  CONFIG_MAP[model_name]['''dw_padding''']

						UpperCAmelCase_			:      int     						=  '''huggingface/label-files'''
						UpperCAmelCase_			:      Dict     						=  '''imagenet-1k-id2label.json'''
						UpperCAmelCase_			:      Union[str, Any]     						=  1000
						UpperCAmelCase_			:      Tuple     						=  json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			repo_type='''dataset'''   ),			'''r'''   )   )
						UpperCAmelCase_			:      str     						=  {int(SCREAMING_SNAKE_CASE__   ): v for k, v in idalabel.items()}

						UpperCAmelCase_			:      Any     						=  idalabel
						UpperCAmelCase_			:      List[str]     						=  {v: k for k, v in idalabel.items()}
						return config





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





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	List[str]:
						UpperCAmelCase_			:      Optional[Any]     						=  CONFIG_MAP[model_name]['''image_size''']
						UpperCAmelCase_			:      str     						=  EfficientNetImageProcessor(
						    size={'''height''': size, '''width''': size},			image_mean=[0.4_85, 0.4_56, 0.4_06],			image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63],			do_center_crop=SCREAMING_SNAKE_CASE__,			)
						return preprocessor





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	Tuple:
						UpperCAmelCase_			:      Dict     						=  [v.split('''_'''   )[0].split('''block'''   )[1] for v in original_param_names if v.startswith('''block'''   )]
						UpperCAmelCase_			:      Optional[int]     						=  sorted(set(SCREAMING_SNAKE_CASE__   )   )
						UpperCAmelCase_			:      Union[str, Any]     						=  len(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      str     						=  {b: str(SCREAMING_SNAKE_CASE__   ) for b, i in zip(SCREAMING_SNAKE_CASE__,			range(SCREAMING_SNAKE_CASE__   )   )}

						UpperCAmelCase_			:      Optional[int]     						=  []
						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_			:      Union[str, Any]     						=  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_			:      Optional[int]     						=  {}
						for item in rename_keys:
												if item[0] in original_param_names:
																		UpperCAmelCase_			:      Optional[int]     						=  '''efficientnet.''' + item[1]

						UpperCAmelCase_			:      Any     						=  '''classifier.weight'''
						UpperCAmelCase_			:      Dict     						=  '''classifier.bias'''
						return key_mapping





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			int,			SCREAMING_SNAKE_CASE__      :			Optional[int]   ) ->	Optional[int]:
						for key, value in tf_params.items():
												if "normalization" in key:
																		continue

												UpperCAmelCase_			:      List[str]     						=  key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		UpperCAmelCase_			:      Union[str, Any]     						=  torch.from_numpy(SCREAMING_SNAKE_CASE__   ).permute(3,			2,			0,			1   )
												elif "depthwise_kernel" in key:
																		UpperCAmelCase_			:      Tuple     						=  torch.from_numpy(SCREAMING_SNAKE_CASE__   ).permute(2,			3,			0,			1   )
												elif "kernel" in key:
																		UpperCAmelCase_			:      str     						=  torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE__   )   )
												else:
																		UpperCAmelCase_			:      Tuple     						=  torch.from_numpy(SCREAMING_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_(SCREAMING_SNAKE_CASE__   )





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Any:
						UpperCAmelCase_			:      Optional[Any]     						=  model_classes[model_name](
						    include_top=SCREAMING_SNAKE_CASE__,			weights='''imagenet''',			input_tensor=SCREAMING_SNAKE_CASE__,			input_shape=SCREAMING_SNAKE_CASE__,			pooling=SCREAMING_SNAKE_CASE__,			classes=1000,			classifier_activation='''softmax''',			)

						UpperCAmelCase_			:      int     						=  original_model.trainable_variables
						UpperCAmelCase_			:      List[Any]     						=  original_model.non_trainable_variables
						UpperCAmelCase_			:      Tuple     						=  {param.name: param.numpy() for param in tf_params}
						for param in tf_non_train_params:
												UpperCAmelCase_			:      Dict     						=  param.numpy()
						UpperCAmelCase_			:      Dict     						=  list(tf_params.keys()   )

						# Load HuggingFace model
						UpperCAmelCase_			:      str     						=  get_efficientnet_config(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[int]     						=  EfficientNetForImageClassification(SCREAMING_SNAKE_CASE__   ).eval()
						UpperCAmelCase_			:      Optional[int]     						=  hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print('''Converting parameters...'''   )
						UpperCAmelCase_			:      List[Any]     						=  rename_keys(SCREAMING_SNAKE_CASE__   )
						replace_params(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )

						# Initialize preprocessor and preprocess input image
						UpperCAmelCase_			:      Optional[Any]     						=  convert_image_processor(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Union[str, Any]     						=  preprocessor(images=prepare_img(),			return_tensors='''pt'''   )

						# HF model inference
						hf_model.eval()
						with torch.no_grad():
												UpperCAmelCase_			:      str     						=  hf_model(**SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Dict     						=  outputs.logits.detach().numpy()

						# Original model inference
						UpperCAmelCase_			:      List[str]     						=  False
						UpperCAmelCase_			:      Union[str, Any]     						=  CONFIG_MAP[model_name]['''image_size''']
						UpperCAmelCase_			:      str     						=  prepare_img().resize((image_size, image_size),			resample=PIL.Image.NEAREST   )
						UpperCAmelCase_			:      Union[str, Any]     						=  image.img_to_array(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Optional[Any]     						=  np.expand_dims(SCREAMING_SNAKE_CASE__,			axis=0   )
						UpperCAmelCase_			:      Optional[int]     						=  original_model.predict(SCREAMING_SNAKE_CASE__   )

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

						if push_to_hub:
												# Push model and image processor to hub
												print(F"""Pushing converted {model_name} to the hub..."""   )
												UpperCAmelCase_			:      List[str]     						=  F"""efficientnet-{model_name}"""
												preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__   )
												hf_model.push_to_hub(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       List[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")

					snake_case_     :       List[Any]						=  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_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	str:
						if number > 0:
												raise ValueError('''input must be a negative integer'''   )
						UpperCAmelCase_			:      Union[str, Any]     						=  len(bin(SCREAMING_SNAKE_CASE__   )[3:]   )
						UpperCAmelCase_			:      Union[str, Any]     						=  bin(abs(SCREAMING_SNAKE_CASE__   ) - (1 << binary_number_length)   )[3:]
						UpperCAmelCase_			:      Optional[Any]     						=  (
						    (
						        '''1'''
						        + '''0''' * (binary_number_length - len(SCREAMING_SNAKE_CASE__   ))
						        + twos_complement_number
						    )
						    if number < 0
						    else '''0'''
						)
						return "0b" + twos_complement_number


if __name__ == "__main__":
					import doctest

					doctest.testmod()





