


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()
SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE							    =							{
    '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  a   (lowerCAmelCase__   ):
   __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_000
   __a					=  json.load(open(hf_hub_download(lowerCAmelCase__		,				lowerCAmelCase__		,				repo_type="""dataset"""   )		,				"""r"""   )   )
   __a					=  {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

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




def  a   ():
   __a					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
   __a					=  Image.open(requests.get(lowerCAmelCase__		,				stream=lowerCAmelCase__   ).raw   )
   return im




def  a   (lowerCAmelCase__   ):
   __a					=  CONFIG_MAP[model_name]["""image_size"""]
   __a					=  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=lowerCAmelCase__		,				)
   return preprocessor




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

   __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  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   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(lowerCAmelCase__   ).permute(3		,				2		,				0		,				1   )
      elif "depthwise_kernel" in key:
         __a					=  torch.from_numpy(lowerCAmelCase__   ).permute(2		,				3		,				0		,				1   )
      elif "kernel" in key:
         __a					=  torch.from_numpy(np.transpose(lowerCAmelCase__   )   )
      else:
         __a					=  torch.from_numpy(lowerCAmelCase__   )

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




@torch.no_grad()
def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  model_classes[model_name](
       include_top=lowerCAmelCase__		,				weights="""imagenet"""		,				input_tensor=lowerCAmelCase__		,				input_shape=lowerCAmelCase__		,				pooling=lowerCAmelCase__		,				classes=1_000		,				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(lowerCAmelCase__   )
   __a					=  EfficientNetForImageClassification(lowerCAmelCase__   ).eval()
   __a					=  hf_model.state_dict()

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

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

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      __a					=  hf_model(**lowerCAmelCase__   )
   __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(lowerCAmelCase__   )
   __a					=  np.expand_dims(lowerCAmelCase__		,				axis=0   )
   __a					=  original_model.predict(lowerCAmelCase__   )

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

   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(lowerCAmelCase__   )
      hf_model.push_to_hub(lowerCAmelCase__   )


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

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







class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        __a					=  set_counts
        __a					=  max(__A    )
        __a					=  len(__A    )
        __a					=  [1] * num_sets
        __a					=  list(range(__A    )    )


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        __a					=  self.get_parent(__A    )
        __a					=  self.get_parent(__A    )

        if src_parent == dst_parent:
           return False

        if self.ranks[dst_parent] >= self.ranks[src_parent]:
           self.set_counts[dst_parent] += self.set_counts[src_parent]
           __a					=  0
           __a					=  dst_parent
           if self.ranks[dst_parent] == self.ranks[src_parent]:
              self.ranks[dst_parent] += 1
           __a					=  self.set_counts[dst_parent]
        else:
           self.set_counts[src_parent] += self.set_counts[dst_parent]
           __a					=  0
           __a					=  src_parent
           __a					=  self.set_counts[src_parent]

        __a					=  max(self.max_set  ,       __A    )
        return True


     def 	snake_case_  (	self  ,       __A    ):
        if self.parents[disj_set] == disj_set:
           return disj_set
        __a					=  self.get_parent(self.parents[disj_set]    )
        return self.parents[disj_set]



