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()
UpperCAmelCase_			    =							logging.get_logger(__name__)

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

UpperCAmelCase_			    =							{
    '''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       lowerCAmelCase_     (		lowercase:							Optional[int]		)    ->				str:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= EfficientNetConfig()
 _UpperCamelCase:     Dict							= CONFIG_MAP[model_name]['''hidden_dim''']
 _UpperCamelCase:     Dict							= CONFIG_MAP[model_name]['''width_coef''']
 _UpperCamelCase:     Optional[Any]							= CONFIG_MAP[model_name]['''depth_coef''']
 _UpperCamelCase:     Optional[Any]							= CONFIG_MAP[model_name]['''image_size''']
 _UpperCamelCase:     Any							= CONFIG_MAP[model_name]['''dropout_rate''']
 _UpperCamelCase:     List[Any]							= CONFIG_MAP[model_name]['''dw_padding''']

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

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




def       lowerCAmelCase_     (		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _UpperCamelCase:     Optional[int]							= Image.open(requests.get(lowercase  ,					stream=lowercase		).raw		)
 return im




def       lowerCAmelCase_     (		lowercase:							str		)    ->				Tuple:







 '''simple docstring'''
 _UpperCamelCase:     List[str]							= CONFIG_MAP[model_name]['''image_size''']
 _UpperCamelCase:     List[Any]							= 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       lowerCAmelCase_     (		lowercase:							Optional[int]		)    ->				List[str]:







 '''simple docstring'''
 _UpperCamelCase:     Tuple							= [v.split('''_'''		)[0].split('''block'''		)[1] for v in original_param_names if v.startswith('''block'''		)]
 _UpperCamelCase:     Optional[Any]							= sorted(set(lowercase		)		)
 _UpperCamelCase:     Any							= len(lowercase		)
 _UpperCamelCase:     Any							= {b: str(lowercase		) for b, i in zip(lowercase  ,					range(lowercase		)		)}

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

 _UpperCamelCase:     str							= '''classifier.weight'''
 _UpperCamelCase:     Tuple							= '''classifier.bias'''
 return key_mapping




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							Tuple  ,					lowercase:							Any		)    ->				str:







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

  _UpperCamelCase:     List[Any]							= key_mapping[key]
  if "_conv" in key and "kernel" in key:
   _UpperCamelCase:     Union[str, Any]							= torch.from_numpy(lowercase		).permute(3  ,					2  ,					0  ,					1		)
  elif "depthwise_kernel" in key:
   _UpperCamelCase:     Tuple							= torch.from_numpy(lowercase		).permute(2  ,					3  ,					0  ,					1		)
  elif "kernel" in key:
   _UpperCamelCase:     Dict							= torch.from_numpy(np.transpose(lowercase		)		)
  else:
   _UpperCamelCase:     Optional[Any]							= 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       lowerCAmelCase_     (		lowercase:							Dict  ,					lowercase:							Optional[Any]  ,					lowercase:							Union[str, Any]  ,					lowercase:							Optional[int]		)    ->				Any:







 '''simple docstring'''
 _UpperCamelCase:     Optional[int]							= model_classes[model_name](
     include_top=lowercase  ,					weights='''imagenet'''  ,					input_tensor=lowercase  ,					input_shape=lowercase  ,					pooling=lowercase  ,					classes=1_000  ,					classifier_activation='''softmax'''  ,					)

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

 # Load HuggingFace model
 _UpperCamelCase:     Dict							= get_efficientnet_config(lowercase		)
 _UpperCamelCase:     Union[str, Any]							= EfficientNetForImageClassification(lowercase		).eval()
 _UpperCamelCase:     int							= hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print('''Converting parameters...'''		)
 _UpperCamelCase:     Any							= rename_keys(lowercase		)
 replace_params(lowercase  ,					lowercase  ,					lowercase		)

 # Initialize preprocessor and preprocess input image
 _UpperCamelCase:     Optional[int]							= convert_image_processor(lowercase		)
 _UpperCamelCase:     Optional[Any]							= preprocessor(images=prepare_img()  ,					return_tensors='''pt'''		)

 # HF model inference
 hf_model.eval()
 with torch.no_grad():
  _UpperCamelCase:     Tuple							= hf_model(**lowercase		)
 _UpperCamelCase:     int							= outputs.logits.detach().numpy()

 # Original model inference
 _UpperCamelCase:     List[str]							= False
 _UpperCamelCase:     Union[str, Any]							= CONFIG_MAP[model_name]['''image_size''']
 _UpperCamelCase:     Optional[Any]							= prepare_img().resize((image_size, image_size)  ,					resample=PIL.Image.NEAREST		)
 _UpperCamelCase:     Union[str, Any]							= image.img_to_array(lowercase		)
 _UpperCamelCase:     Union[str, Any]							= np.expand_dims(lowercase  ,					axis=0		)
 _UpperCamelCase:     str							= 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..."""		)
  _UpperCamelCase:     List[Any]							= F"""efficientnet-{model_name}"""
  preprocessor.push_to_hub(lowercase		)
  hf_model.push_to_hub(lowercase		)


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

  UpperCAmelCase_			    =							parser.parse_args()
  convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
from __future__ import annotations

UpperCAmelCase_			    =							1.6_0_2_1E-1_9  # units = C




def       lowerCAmelCase_     (		lowercase:							float  ,					lowercase:							float  ,					lowercase:							float  ,					)    ->				tuple[str, float]:







 '''simple docstring'''
 if (conductivity, electron_conc, mobility).count(0		) != 1:
  raise ValueError('''You cannot supply more or less than 2 values'''		)
 elif conductivity < 0:
  raise ValueError('''Conductivity cannot be negative'''		)
 elif electron_conc < 0:
  raise ValueError('''Electron concentration cannot be negative'''		)
 elif mobility < 0:
  raise ValueError('''mobility cannot be negative'''		)
 elif conductivity == 0:
  return (
      "conductivity",
      mobility * electron_conc * ELECTRON_CHARGE,
  )
 elif electron_conc == 0:
  return (
      "electron_conc",
      conductivity / (mobility * ELECTRON_CHARGE),
  )
 else:
  return (
      "mobility",
      conductivity / (electron_conc * ELECTRON_CHARGE),
  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()