



'''simple docstring'''


import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=LxmertConfig.from_json_file(lowercase__       )
     print(F"""Building PyTorch model from configuration: {config}"""       )
     a_       		=LxmertForPreTraining(lowercase__       )

     # Load weights from tf checkpoint
     load_tf_weights_in_lxmert(lowercase__       ,	lowercase__       ,	lowercase__       )

     # Save pytorch-model
     print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
     torch.save(model.state_dict()       ,	lowercase__       )


if __name__ == "__main__":
  lowercase							       =			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(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase							       =			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







'''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()
lowercase							       =			logging.get_logger(__name__)

lowercase							       =			{
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

lowercase							       =			{
    '''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  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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

     a_       		="huggingface/label-files"
     a_       		="imagenet-1k-id2label.json"
     a_       		=1_0_0_0
     a_       		=json.load(open(hf_hub_download(lowercase__       ,	lowercase__       ,	repo_type="dataset"       )       ,	"r"       )       )
     a_       		={int(lowercase__       ): v for k, v in idalabel.items()}

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






def  UpperCAmelCase_       (		):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=CONFIG_MAP[model_name]["image_size"]
     a_       		=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  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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

     a_       		=[]
     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:
          a_       		=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")       )

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

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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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

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


     '''simple docstring'''







     a_       		=model_classes[model_name](
         include_top=lowercase__       ,	weights="imagenet"       ,	input_tensor=lowercase__       ,	input_shape=lowercase__       ,	pooling=lowercase__       ,	classes=1_0_0_0       ,	classifier_activation="softmax"       ,	)

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

     # Load HuggingFace model
     a_       		=get_efficientnet_config(lowercase__       )
     a_       		=EfficientNetForImageClassification(lowercase__       ).eval()
     a_       		=hf_model.state_dict()

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

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

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

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


if __name__ == "__main__":
  lowercase							       =			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''')

  lowercase							       =			parser.parse_args()
  convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


