



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_    :  Any						  =		logging.get_logger(__name__)

a_    :  Optional[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,
}

a_    :  Optional[int]						  =		{
    'b0': {
        'hidden_dim': 1_2_8_0,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_2_4,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1_2_8_0,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_4_0,
        'dropout_rate': 0.2,
        'dw_padding': [1_6],
    },
    'b2': {
        'hidden_dim': 1_4_0_8,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_6_0,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 1_6],
    },
    'b3': {
        'hidden_dim': 1_5_3_6,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_0_0,
        'dropout_rate': 0.3,
        'dw_padding': [5, 1_8],
    },
    'b4': {
        'hidden_dim': 1_7_9_2,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_8_0,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2_0_4_8,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_5_6,
        'dropout_rate': 0.4,
        'dw_padding': [1_3, 2_7],
    },
    'b6': {
        'hidden_dim': 2_3_0_4,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_2_8,
        'dropout_rate': 0.5,
        'dw_padding': [3_1],
    },
    'b7': {
        'hidden_dim': 2_5_6_0,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_0_0,
        'dropout_rate': 0.5,
        'dw_padding': [1_8],
    },
}

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		EfficientNetConfig()
      lowerCamelCase             =		CONFIG_MAP[model_name]["hidden_dim"]
      lowerCamelCase             =		CONFIG_MAP[model_name]["width_coef"]
      lowerCamelCase             =		CONFIG_MAP[model_name]["depth_coef"]
      lowerCamelCase             =		CONFIG_MAP[model_name]["image_size"]
      lowerCamelCase             =		CONFIG_MAP[model_name]["dropout_rate"]
      lowerCamelCase             =		CONFIG_MAP[model_name]["dw_padding"]

      lowerCamelCase             =		"huggingface/label-files"
      lowerCamelCase             =		"imagenet-1k-id2label.json"
      lowerCamelCase             =		1000
      lowerCamelCase             =		json.load(open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type="dataset"			) , "r"			)			)
      lowerCamelCase             =		{int(UpperCAmelCase__			): v for k, v in idalabel.items()}

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

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		"http://images.cocodataset.org/val2017/000000039769.jpg"
      lowerCamelCase             =		Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__			).raw			)
      return im

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		CONFIG_MAP[model_name]["image_size"]
      lowerCamelCase             =		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      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		[v.split("_"			)[0].split("block"			)[1] for v in original_param_names if v.startswith("block"			)]
      lowerCamelCase             =		sorted(set(UpperCAmelCase__			)			)
      lowerCamelCase             =		len(UpperCAmelCase__			)
      lowerCamelCase             =		{b: str(UpperCAmelCase__			) for b, i in zip(UpperCAmelCase__ , range(UpperCAmelCase__			)			)}

      lowerCamelCase             =		[]
      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:
            lowerCamelCase             =		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")			)

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

      lowerCamelCase             =		"classifier.weight"
      lowerCamelCase             =		"classifier.bias"
      return key_mapping

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



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

            lowerCamelCase             =		key_mapping[key]
            if "_conv" in key and "kernel" in key:
                  lowerCamelCase             =		torch.from_numpy(UpperCAmelCase__			).permute(3 , 2 , 0 , 1			)
            elif "depthwise_kernel" in key:
                  lowerCamelCase             =		torch.from_numpy(UpperCAmelCase__			).permute(2 , 3 , 0 , 1			)
            elif "kernel" in key:
                  lowerCamelCase             =		torch.from_numpy(np.transpose(UpperCAmelCase__			)			)
            else:
                  lowerCamelCase             =		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      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		model_classes[model_name](
          include_top=UpperCAmelCase__ , weights="imagenet" , input_tensor=UpperCAmelCase__ , input_shape=UpperCAmelCase__ , pooling=UpperCAmelCase__ , classes=1000 , classifier_activation="softmax" , )

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

      # Load HuggingFace model
      lowerCamelCase             =		get_efficientnet_config(UpperCAmelCase__			)
      lowerCamelCase             =		EfficientNetForImageClassification(UpperCAmelCase__			).eval()
      lowerCamelCase             =		hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print("Converting parameters..."			)
      lowerCamelCase             =		rename_keys(UpperCAmelCase__			)
      replace_params(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)

      # Initialize preprocessor and preprocess input image
      lowerCamelCase             =		convert_image_processor(UpperCAmelCase__			)
      lowerCamelCase             =		preprocessor(images=prepare_img() , return_tensors="pt"			)

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

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


if __name__ == "__main__":
     a_    :  int						  =		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_    :  Dict						  =		parser.parse_args()
     convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileBertConfig.from_json_file(UpperCAmelCase__			)
      print(F"""Building PyTorch model from configuration: {config}"""			)
      lowerCamelCase             =		MobileBertForPreTraining(UpperCAmelCase__			)
      # Load weights from tf checkpoint
      lowerCamelCase             =		load_tf_weights_in_mobilebert(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      # Save pytorch-model
      print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
      torch.save(model.state_dict() , UpperCAmelCase__			)


if __name__ == "__main__":
     a_    :  Any						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--mobilebert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained MobileBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_    :  List[Any]						  =		parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)