


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		       =						logging.get_logger(__name__)

_A		       =						{
    '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		       =						{
    'b0': {
        'hidden_dim': 1280,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 224,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1280,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 240,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 1408,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 260,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 1536,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 300,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 1792,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 380,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2048,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 456,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 2304,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 528,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 2560,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 600,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=EfficientNetConfig()
     __UpperCamelCase    							=CONFIG_MAP[model_name]['hidden_dim']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['width_coef']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['depth_coef']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['image_size']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['dropout_rate']
     __UpperCamelCase    							=CONFIG_MAP[model_name]['dw_padding']

     __UpperCamelCase    							='huggingface/label-files'
     __UpperCamelCase    							='imagenet-1k-id2label.json'
     __UpperCamelCase    							=10_00
     __UpperCamelCase    							=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     repo_type='dataset'				)			,     'r'				)				)
     __UpperCamelCase    							={int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}

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

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							='http://images.cocodataset.org/val2017/000000039769.jpg'
     __UpperCamelCase    							=Image.open(requests.get(SCREAMING_SNAKE_CASE__			,     stream=SCREAMING_SNAKE_CASE__				).raw				)
     return im

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]				):
     __UpperCamelCase    							=CONFIG_MAP[model_name]['image_size']
     __UpperCamelCase    							=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=SCREAMING_SNAKE_CASE__			,     )
     return preprocessor

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

     __UpperCamelCase    							=[]
     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:
          __UpperCamelCase    							=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')				)

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

     __UpperCamelCase    							='classifier.weight'
     __UpperCamelCase    							='classifier.bias'
     return key_mapping

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Tuple				):
     for key, value in tf_params.items():
          if "normalization" in key:
               continue

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

@torch.no_grad()
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]				):
     __UpperCamelCase    							=model_classes[model_name](
         include_top=SCREAMING_SNAKE_CASE__			,     weights='imagenet'			,     input_tensor=SCREAMING_SNAKE_CASE__			,     input_shape=SCREAMING_SNAKE_CASE__			,     pooling=SCREAMING_SNAKE_CASE__			,     classes=10_00			,     classifier_activation='softmax'			,     )

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

     # Load HuggingFace model
     __UpperCamelCase    							=get_efficientnet_config(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=EfficientNetForImageClassification(SCREAMING_SNAKE_CASE__				).eval()
     __UpperCamelCase    							=hf_model.state_dict()

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

     # Initialize preprocessor and preprocess input image
     __UpperCamelCase    							=convert_image_processor(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=preprocessor(images=prepare_img()			,     return_tensors='pt'				)

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

     # Original model inference
     __UpperCamelCase    							=False
     __UpperCamelCase    							=CONFIG_MAP[model_name]['image_size']
     __UpperCamelCase    							=prepare_img().resize((image_size, image_size)			,     resample=PIL.Image.NEAREST				)
     __UpperCamelCase    							=image.img_to_array(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=np.expand_dims(SCREAMING_SNAKE_CASE__			,     axis=0				)
     __UpperCamelCase    							=original_model.predict(SCREAMING_SNAKE_CASE__				)

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

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


if __name__ == "__main__":
   _A		       =						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		       =						parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
   import onnxruntime as ort


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}






class   UpperCAmelCase		:



   def __init__(self		:					Optional[Any]    ,						snake_case__		:					Optional[Any]=None    ,						**snake_case__		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future." )
          snake_case       :   Optional[Any]							=      model
          snake_case       :   Dict							=      kwargs.get("model_save_dir"    ,						snake_case__ )
          snake_case       :   int							=      kwargs.get("latest_model_name"    ,						snake_case__ )



   def __call__(self		:					Tuple    ,						**snake_case__		:					str )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      {k: np.array(snake_case__ ) for k, v in kwargs.items()}
          return self.model.run(snake_case__    ,						snake_case__ )



   @staticmethod
   def 				_SCREAMING_SNAKE_CASE   (snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[int]=None    ,						snake_case__		:					Optional[int]=None )		->	Any:


          '''simple docstring'''


          if provider is None:
                 logger.info("No onnxruntime provider specified, using CPUExecutionProvider" )
                 snake_case       :   Optional[int]							=      "CPUExecutionProvider"

          return ort.InferenceSession(snake_case__    ,						providers=[provider]    ,						sess_options=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Any )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

          snake_case       :   Any							=      self.model_save_dir.joinpath(self.latest_model_name )
          snake_case       :   str							=      Path(snake_case__ ).joinpath(snake_case__ )
          try:
                 shutil.copyfile(snake_case__    ,						snake_case__ )
          except shutil.SameFileError:
                 pass

          # copy external weights (for models >2GB)
          snake_case       :   List[str]							=      self.model_save_dir.joinpath(snake_case__ )
          if src_path.exists():
                 snake_case       :   Tuple							=      Path(snake_case__ ).joinpath(snake_case__ )
                 try:
                        shutil.copyfile(snake_case__    ,						snake_case__ )
                 except shutil.SameFileError:
                        pass



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, os.PathLike]    ,						**snake_case__		:					Optional[int]    ,						)		->	str:


          '''simple docstring'''


          if os.path.isfile(snake_case__ ):
                 logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" )
                 return

          os.makedirs(snake_case__    ,						exist_ok=snake_case__ )

          # saving model weights/files
          self._save_pretrained(snake_case__    ,						**snake_case__ )



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Tuple    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					Optional[Union[bool, str, None]] = None    ,						snake_case__		:					Optional[Union[str, None]] = None    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional["ort.SessionOptions"] = None    ,						**snake_case__		:					Tuple    ,						)		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      file_name if file_name is not None else ONNX_WEIGHTS_NAME
          # load model from local directory
          if os.path.isdir(snake_case__ ):
                 snake_case       :   Any							=      OnnxRuntimeModel.load_model(
                     os.path.join(snake_case__    ,						snake_case__ )    ,						provider=snake_case__    ,						sess_options=snake_case__ )
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ )
          # load model from hub
          else:
                 # download model
                 snake_case       :   Dict							=      hf_hub_download(
                     repo_id=snake_case__    ,						filename=snake_case__    ,						use_auth_token=snake_case__    ,						revision=snake_case__    ,						cache_dir=snake_case__    ,						force_download=snake_case__    ,						)
                 snake_case       :   List[Any]							=      Path(snake_case__ ).parent
                 snake_case       :   Union[str, Any]							=      Path(snake_case__ ).name
                 snake_case       :   Dict							=      OnnxRuntimeModel.load_model(snake_case__    ,						provider=snake_case__    ,						sess_options=snake_case__ )
          return cls(model=snake_case__    ,						**snake_case__ )



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Optional[Any]    ,						snake_case__		:					Union[str, Path]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Optional[str] = None    ,						snake_case__		:					Optional[str] = None    ,						**snake_case__		:					Dict    ,						)		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      None
          if len(str(snake_case__ ).split("@" ) ) == 2:
                 snake_case				,					snake_case       :   int							=      model_id.split("@" )

          return cls._from_pretrained(
              model_id=snake_case__    ,						revision=snake_case__    ,						cache_dir=snake_case__    ,						force_download=snake_case__    ,						use_auth_token=snake_case__    ,						**snake_case__    ,						)





