






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": 1_280,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 224,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1_280,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 240,
        "dropout_rate": 0.2,
        "dw_padding": [16],
    },
    "b2": {
        "hidden_dim": 1_408,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 260,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 16],
    },
    "b3": {
        "hidden_dim": 1_536,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 300,
        "dropout_rate": 0.3,
        "dw_padding": [5, 18],
    },
    "b4": {
        "hidden_dim": 1_792,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 380,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2_048,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 456,
        "dropout_rate": 0.4,
        "dw_padding": [13, 27],
    },
    "b6": {
        "hidden_dim": 2_304,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 528,
        "dropout_rate": 0.5,
        "dw_padding": [31],
    },
    "b7": {
        "hidden_dim": 2_560,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 600,
        "dropout_rate": 0.5,
        "dw_padding": [18],
    },
}




def 				A		(					lowercase__  :  Union[str, Any]  )		-> int:
	UpperCamelCase__ :Optional[int]						=      EfficientNetConfig()
	UpperCamelCase__ :Union[str, Any]						=      CONFIG_MAP[model_name]["""hidden_dim"""]
	UpperCamelCase__ :int						=      CONFIG_MAP[model_name]["""width_coef"""]
	UpperCamelCase__ :int						=      CONFIG_MAP[model_name]["""depth_coef"""]
	UpperCamelCase__ :List[Any]						=      CONFIG_MAP[model_name]["""image_size"""]
	UpperCamelCase__ :List[str]						=      CONFIG_MAP[model_name]["""dropout_rate"""]
	UpperCamelCase__ :Optional[int]						=      CONFIG_MAP[model_name]["""dw_padding"""]

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

	UpperCamelCase__ :Any						=      idalabel
	UpperCamelCase__ :Optional[Any]						=      {v: k for k, v in idalabel.items()}
	return config




def 				A		(					)		-> Optional[Any]:
	UpperCamelCase__ :Union[str, Any]						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCamelCase__ :int						=      Image.open(requests.get(lowercase__ ,		stream=lowercase__  ).raw  )
	return im




def 				A		(					lowercase__  :  int  )		-> List[str]:
	UpperCamelCase__ :Union[str, Any]						=      CONFIG_MAP[model_name]["""image_size"""]
	UpperCamelCase__ :Any						=      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=lowercase__ ,		)
	return preprocessor




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

	UpperCamelCase__ :List[Any]						=      []
	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__ :str						=      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__ :Tuple						=      {}
	for item in rename_keys:
		if item[0] in original_param_names:
			UpperCamelCase__ :List[str]						=      """efficientnet.""" + item[1]

	UpperCamelCase__ :Dict						=      """classifier.weight"""
	UpperCamelCase__ :int						=      """classifier.bias"""
	return key_mapping




def 				A		(					lowercase__  :  str ,		lowercase__  :  str ,		lowercase__  :  List[str]  )		-> List[str]:
	for key, value in tf_params.items():
		if "normalization" in key:
			continue

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

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




@torch.no_grad()
def 				A		(					lowercase__  :  Optional[Any] ,		lowercase__  :  Dict ,		lowercase__  :  Optional[int] ,		lowercase__  :  int  )		-> Dict:
	UpperCamelCase__ :str						=      model_classes[model_name](
	    include_top=lowercase__ ,		weights="""imagenet""" ,		input_tensor=lowercase__ ,		input_shape=lowercase__ ,		pooling=lowercase__ ,		classes=1000 ,		classifier_activation="""softmax""" ,		)

	UpperCamelCase__ :Optional[int]						=      original_model.trainable_variables
	UpperCamelCase__ :Optional[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__ :int						=      param.numpy()
	UpperCamelCase__ :Optional[int]						=      list(tf_params.keys()  )

	# Load HuggingFace model
	UpperCamelCase__ :str						=      get_efficientnet_config(lowercase__  )
	UpperCamelCase__ :Any						=      EfficientNetForImageClassification(lowercase__  ).eval()
	UpperCamelCase__ :List[Any]						=      hf_model.state_dict()

	# Create src-to-dst parameter name mapping dictionary
	print("""Converting parameters..."""  )
	UpperCamelCase__ :Union[str, Any]						=      rename_keys(lowercase__  )
	replace_params(lowercase__ ,		lowercase__ ,		lowercase__  )

	# Initialize preprocessor and preprocess input image
	UpperCamelCase__ :int						=      convert_image_processor(lowercase__  )
	UpperCamelCase__ :Any						=      preprocessor(images=prepare_img() ,		return_tensors="""pt"""  )

	# HF model inference
	hf_model.eval()
	with torch.no_grad():
		UpperCamelCase__ :Dict						=      hf_model(**lowercase__  )
	UpperCamelCase__ :Union[str, Any]						=      outputs.logits.detach().numpy()

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

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

	if push_to_hub:
		# Push model and image processor to hub
		print(f"""Pushing converted {model_name} to the hub..."""  )
		UpperCamelCase__ :Dict						=      f"""efficientnet-{model_name}"""
		preprocessor.push_to_hub(lowercase__  )
		hf_model.push_to_hub(lowercase__  )


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)

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> BatchFeature:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


