





__lowerCAmelCase :List[str]     =    [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages






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 :str     =    logging.get_logger(__name__)

__lowerCAmelCase :int     =    {
    '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 :List[str]     =    {
    '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    A				(							UpperCAmelCase				):
					_snake_case       :					str				       =   EfficientNetConfig()
					_snake_case       :					Optional[int]				       =   CONFIG_MAP[model_name]["hidden_dim"]
					_snake_case       :					Tuple				       =   CONFIG_MAP[model_name]["width_coef"]
					_snake_case       :					Dict				       =   CONFIG_MAP[model_name]["depth_coef"]
					_snake_case       :					List[Any]				       =   CONFIG_MAP[model_name]["image_size"]
					_snake_case       :					Tuple				       =   CONFIG_MAP[model_name]["dropout_rate"]
					_snake_case       :					str				       =   CONFIG_MAP[model_name]["dw_padding"]

					_snake_case       :					Union[str, Any]				       =   "huggingface/label-files"
					_snake_case       :					Any				       =   "imagenet-1k-id2label.json"
					_snake_case       :					Optional[Any]				       =   1_000
					_snake_case       :					Optional[Any]				       =   json.load(open(hf_hub_download(UpperCAmelCase  ,  UpperCAmelCase  ,  repo_type="dataset"				)  ,  "r"				)				)
					_snake_case       :					int				       =   {int(UpperCAmelCase				): v for k, v in idalabel.items()}

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







def    A				(							):
					_snake_case       :					List[Any]				       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					_snake_case       :					Optional[int]				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				)
					return im







def    A				(							UpperCAmelCase				):
					_snake_case       :					Optional[Any]				       =   CONFIG_MAP[model_name]["image_size"]
					_snake_case       :					int				       =   EfficientNetImageProcessor(
					    size={"height": size, "width": size}  ,  image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,  image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]  ,  do_center_crop=UpperCAmelCase  ,  )
					return preprocessor







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

					_snake_case       :					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:
										_snake_case       :					Optional[int]				       =   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")				)

					_snake_case       :					Union[str, Any]				       =   {}
					for item in rename_keys:
										if item[0] in original_param_names:
															_snake_case       :					str				       =   "efficientnet." + item[1]

					_snake_case       :					List[str]				       =   "classifier.weight"
					_snake_case       :					int				       =   "classifier.bias"
					return key_mapping







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					for key, value in tf_params.items():
										if "normalization" in key:
															continue

										_snake_case       :					Union[str, Any]				       =   key_mapping[key]
										if "_conv" in key and "kernel" in key:
															_snake_case       :					Union[str, Any]				       =   torch.from_numpy(UpperCAmelCase				).permute(3  ,  2  ,  0  ,  1				)
										elif "depthwise_kernel" in key:
															_snake_case       :					str				       =   torch.from_numpy(UpperCAmelCase				).permute(2  ,  3  ,  0  ,  1				)
										elif "kernel" in key:
															_snake_case       :					Optional[Any]				       =   torch.from_numpy(np.transpose(UpperCAmelCase				)				)
										else:
															_snake_case       :					int				       =   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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   model_classes[model_name](
					    include_top=UpperCAmelCase  ,  weights="imagenet"  ,  input_tensor=UpperCAmelCase  ,  input_shape=UpperCAmelCase  ,  pooling=UpperCAmelCase  ,  classes=1_000  ,  classifier_activation="softmax"  ,  )

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

					# Load HuggingFace model
					_snake_case       :					Optional[int]				       =   get_efficientnet_config(UpperCAmelCase				)
					_snake_case       :					Dict				       =   EfficientNetForImageClassification(UpperCAmelCase				).eval()
					_snake_case       :					int				       =   hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print("Converting parameters..."				)
					_snake_case       :					Optional[int]				       =   rename_keys(UpperCAmelCase				)
					replace_params(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					# Initialize preprocessor and preprocess input image
					_snake_case       :					Optional[Any]				       =   convert_image_processor(UpperCAmelCase				)
					_snake_case       :					List[str]				       =   preprocessor(images=prepare_img()  ,  return_tensors="pt"				)

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

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


if __name__ == "__main__":
							__lowerCAmelCase :Any     =    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 :List[Any]     =    parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)