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_     :   Optional[int] 				=				logging.get_logger(__name__)

lowercase_     :   Union[str, 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,
}

lowercase_     :   Dict 				=				{
    '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__      (	snake_case_ :		Optional[int]      ):
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      EfficientNetConfig()
 SCREAMING_SNAKE_CASE__:   str=      CONFIG_MAP[model_name]['''hidden_dim''']
 SCREAMING_SNAKE_CASE__:   Any=      CONFIG_MAP[model_name]['''width_coef''']
 SCREAMING_SNAKE_CASE__:   List[str]=      CONFIG_MAP[model_name]['''depth_coef''']
 SCREAMING_SNAKE_CASE__:   str=      CONFIG_MAP[model_name]['''image_size''']
 SCREAMING_SNAKE_CASE__:   int=      CONFIG_MAP[model_name]['''dropout_rate''']
 SCREAMING_SNAKE_CASE__:   Optional[int]=      CONFIG_MAP[model_name]['''dw_padding''']

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

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







def 			A__      (	):
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 SCREAMING_SNAKE_CASE__:   List[Any]=      Image.open(requests.get(snake_case_       ,				stream=snake_case_      ).raw      )
 return im







def 			A__      (	snake_case_ :		str      ):
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      CONFIG_MAP[model_name]['''image_size''']
 SCREAMING_SNAKE_CASE__:   int=      EfficientNetImageProcessor(
     size={'''height''': size, '''width''': size}       ,				image_mean=[0.4_85, 0.4_56, 0.4_06]       ,				image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]       ,				do_center_crop=snake_case_       ,				)
 return preprocessor







def 			A__      (	snake_case_ :		Optional[int]      ):
 SCREAMING_SNAKE_CASE__:   Any=      [v.split('''_'''      )[0].split('''block'''      )[1] for v in original_param_names if v.startswith('''block'''      )]
 SCREAMING_SNAKE_CASE__:   Any=      sorted(set(snake_case_      )      )
 SCREAMING_SNAKE_CASE__:   int=      len(snake_case_      )
 SCREAMING_SNAKE_CASE__:   Dict=      {b: str(snake_case_      ) for b, i in zip(snake_case_       ,				range(snake_case_      )      )}

 SCREAMING_SNAKE_CASE__:   Tuple=      []
 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:
  SCREAMING_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''')      )

 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      {}
 for item in rename_keys:
  if item[0] in original_param_names:
   SCREAMING_SNAKE_CASE__:   Optional[int]=      '''efficientnet.''' + item[1]

 SCREAMING_SNAKE_CASE__:   List[str]=      '''classifier.weight'''
 SCREAMING_SNAKE_CASE__:   Tuple=      '''classifier.bias'''
 return key_mapping







def 			A__      (	snake_case_ :		Optional[Any]       ,				snake_case_ :		int       ,				snake_case_ :		Optional[int]      ):
 for key, value in tf_params.items():
  if "normalization" in key:
   continue

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

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







@torch.no_grad()
def 			A__      (	snake_case_ :		Union[str, Any]       ,				snake_case_ :		Optional[int]       ,				snake_case_ :		List[Any]       ,				snake_case_ :		Union[str, Any]      ):
 SCREAMING_SNAKE_CASE__:   List[str]=      model_classes[model_name](
     include_top=snake_case_       ,				weights='''imagenet'''       ,				input_tensor=snake_case_       ,				input_shape=snake_case_       ,				pooling=snake_case_       ,				classes=1_000       ,				classifier_activation='''softmax'''       ,				)

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

 # Load HuggingFace model
 SCREAMING_SNAKE_CASE__:   int=      get_efficientnet_config(snake_case_      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      EfficientNetForImageClassification(snake_case_      ).eval()
 SCREAMING_SNAKE_CASE__:   Any=      hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print('''Converting parameters...'''      )
 SCREAMING_SNAKE_CASE__:   Tuple=      rename_keys(snake_case_      )
 replace_params(snake_case_       ,				snake_case_       ,				snake_case_      )

 # Initialize preprocessor and preprocess input image
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      convert_image_processor(snake_case_      )
 SCREAMING_SNAKE_CASE__:   int=      preprocessor(images=prepare_img()       ,				return_tensors='''pt'''      )

 # HF model inference
 hf_model.eval()
 with torch.no_grad():
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      hf_model(**snake_case_      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      outputs.logits.detach().numpy()

 # Original model inference
 SCREAMING_SNAKE_CASE__:   int=      False
 SCREAMING_SNAKE_CASE__:   int=      CONFIG_MAP[model_name]['''image_size''']
 SCREAMING_SNAKE_CASE__:   Dict=      prepare_img().resize((image_size, image_size)       ,				resample=PIL.Image.NEAREST      )
 SCREAMING_SNAKE_CASE__:   List[str]=      image.img_to_array(snake_case_      )
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      np.expand_dims(snake_case_       ,				axis=0      )
 SCREAMING_SNAKE_CASE__:   int=      original_model.predict(snake_case_      )

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

 if push_to_hub:
  # Push model and image processor to hub
  print(F'Pushing converted {model_name} to the hub...'      )
  SCREAMING_SNAKE_CASE__:   Dict=      F'efficientnet-{model_name}'
  preprocessor.push_to_hub(snake_case_      )
  hf_model.push_to_hub(snake_case_      )


if __name__ == "__main__":
      lowercase_     :   Optional[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')

      lowercase_     :   str 				=				parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowercase_     :   List[str] 				=				{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class 		_lowerCamelCase		(   nn.Module    ):
    def __init__(  self , lowerCAmelCase   )       ->					str:
     super().__init__()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torchvision.models.resnetaaa(pretrained=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      list(model.children()   )[:-2]
     SCREAMING_SNAKE_CASE__:   Tuple=      nn.Sequential(*lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
     SCREAMING_SNAKE_CASE__:   str=      self.pool(self.model(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   str=      torch.flatten(lowerCAmelCase , start_dim=2   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      out.transpose(1 , 2   ).contiguous()
     return out  # BxNx2048






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [json.loads(lowerCAmelCase   ) for l in open(lowerCAmelCase   )]
     SCREAMING_SNAKE_CASE__:   List[str]=      os.path.dirname(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      tokenizer
     SCREAMING_SNAKE_CASE__:   Any=      labels
     SCREAMING_SNAKE_CASE__:   List[Any]=      len(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      max_seq_length

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      transforms
    def __len__(  self   )       ->					Optional[Any]:
     return len(self.data   )
    def __getitem__(  self , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      sentence[0], sentence[1:-1], sentence[-1]
     SCREAMING_SNAKE_CASE__:   Tuple=      sentence[: self.max_seq_length]

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.zeros(self.n_classes   )
     SCREAMING_SNAKE_CASE__:   Dict=      1

     SCREAMING_SNAKE_CASE__:   Optional[int]=      Image.open(os.path.join(self.data_dir , self.data[index]['''img''']   )   ).convert('''RGB'''   )
     SCREAMING_SNAKE_CASE__:   int=      self.transforms(lowerCAmelCase   )

     return {
         "image_start_token": start_token,
         "image_end_token": end_token,
         "sentence": sentence,
         "image": image,
         "label": label,
     }



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      Counter()
     for row in self.data:
      label_freqs.update(row['''label''']   )
     return label_freqs







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Any=      [len(row['''sentence''']      ) for row in batch]
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      len(snake_case_      ), max(snake_case_      )

 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )

 for i_batch, (input_row, length) in enumerate(zip(snake_case_       ,				snake_case_      )      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      input_row['''sentence''']
  SCREAMING_SNAKE_CASE__:   Dict=      1

 SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.stack([row['''image'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   List[str]=      torch.stack([row['''label'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Any=      torch.stack([row['''image_start_token'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.stack([row['''image_end_token'''] for row in batch]      )

 return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def 			A__      (	):
 return [
     "Crime",
     "Drama",
     "Thriller",
     "Action",
     "Comedy",
     "Romance",
     "Documentary",
     "Short",
     "Mystery",
     "History",
     "Family",
     "Adventure",
     "Fantasy",
     "Sci-Fi",
     "Western",
     "Horror",
     "Sport",
     "War",
     "Music",
     "Musical",
     "Animation",
     "Biography",
     "Film-Noir",
 ]







def 			A__      (	):
 return transforms.Compose(
     [
         transforms.Resize(256      ),
         transforms.CenterCrop(224      ),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]       ,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]       ,				),
     ]      )

