'''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()
A_     :						Dict    							=logging.get_logger(__name__)

A_     :						List[str]    							={
    '''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_     :						List[str]    							={
    '''b0''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_24,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_40,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 14_08,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_60,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 15_36,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_00,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 17_92,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_80,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 20_48,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_56,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 23_04,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_28,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 25_60,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_00,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}



def snake_case_      (					__snake_case  :							List[Any])			->					Tuple:
   lowerCAmelCase_			=      EfficientNetConfig()
   lowerCAmelCase_			=      CONFIG_MAP[model_name]['''hidden_dim''']
   lowerCAmelCase_			=      CONFIG_MAP[model_name]['''width_coef''']
   lowerCAmelCase_			=      CONFIG_MAP[model_name]['''depth_coef''']
   lowerCAmelCase_			=      CONFIG_MAP[model_name]['''image_size''']
   lowerCAmelCase_			=      CONFIG_MAP[model_name]['''dropout_rate''']
   lowerCAmelCase_			=      CONFIG_MAP[model_name]['''dw_padding''']

   lowerCAmelCase_			=      '''huggingface/label-files'''
   lowerCAmelCase_			=      '''imagenet-1k-id2label.json'''
   lowerCAmelCase_			=      1000
   lowerCAmelCase_			=      json.load(open(hf_hub_download(__snake_case	,   __snake_case	,   repo_type='''dataset''')	,   '''r'''))
   lowerCAmelCase_			=      {int(__snake_case): v for k, v in idalabel.items()}

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



def snake_case_      (					)			->					List[str]:
   lowerCAmelCase_			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCAmelCase_			=      Image.open(requests.get(__snake_case	,   stream=__snake_case).raw)
   return im



def snake_case_      (					__snake_case  :							Optional[Any])			->					Tuple:
   lowerCAmelCase_			=      CONFIG_MAP[model_name]['''image_size''']
   lowerCAmelCase_			=      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=__snake_case	,   )
   return preprocessor



def snake_case_      (					__snake_case  :							str)			->					str:
   lowerCAmelCase_			=      [v.split('''_''')[0].split('''block''')[1] for v in original_param_names if v.startswith('''block''')]
   lowerCAmelCase_			=      sorted(set(__snake_case))
   lowerCAmelCase_			=      len(__snake_case)
   lowerCAmelCase_			=      {b: str(__snake_case) for b, i in zip(__snake_case	,   range(__snake_case))}

   lowerCAmelCase_			=      []
   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:
      lowerCAmelCase_			=      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'''))

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

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



def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							List[Any]	,   __snake_case  :							str)			->					Optional[int]:
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      lowerCAmelCase_			=      key_mapping[key]
      if "_conv" in key and "kernel" in key:
         lowerCAmelCase_			=      torch.from_numpy(__snake_case).permute(3	,   2	,   0	,   1)
      elif "depthwise_kernel" in key:
         lowerCAmelCase_			=      torch.from_numpy(__snake_case).permute(2	,   3	,   0	,   1)
      elif "kernel" in key:
         lowerCAmelCase_			=      torch.from_numpy(np.transpose(__snake_case))
      else:
         lowerCAmelCase_			=      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 snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]	,   __snake_case  :							Dict	,   __snake_case  :							Union[str, Any])			->					Tuple:
   lowerCAmelCase_			=      model_classes[model_name](
       include_top=__snake_case	,   weights='''imagenet'''	,   input_tensor=__snake_case	,   input_shape=__snake_case	,   pooling=__snake_case	,   classes=1000	,   classifier_activation='''softmax'''	,   )

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

   # Load HuggingFace model
   lowerCAmelCase_			=      get_efficientnet_config(__snake_case)
   lowerCAmelCase_			=      EfficientNetForImageClassification(__snake_case).eval()
   lowerCAmelCase_			=      hf_model.state_dict()

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

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

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

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


if __name__ == "__main__":
 A_     :						Union[str, 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''')

 A_     :						Any    							=parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






'''simple docstring'''

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_     :						Tuple    							=logging.get_logger(__name__)

A_     :						Dict    							={
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}


class     __UpperCAmelCase       ( __a		):
  __A :				Union[str, Any]											= 'efficientnet'





  def __init__(	self    ,     _lowerCamelCase = 3    ,     _lowerCamelCase = 600    ,     _lowerCamelCase = 2.0    ,     _lowerCamelCase = 3.1    ,     _lowerCamelCase = 8    ,     _lowerCamelCase = [3, 3, 5, 3, 5, 5, 3]    ,     _lowerCamelCase = [32, 16, 24, 40, 80, 112, 192]    ,     _lowerCamelCase = [16, 24, 40, 80, 112, 192, 320]    ,     _lowerCamelCase = []    ,     _lowerCamelCase = [1, 2, 2, 2, 1, 2, 1]    ,     _lowerCamelCase = [1, 2, 2, 3, 3, 4, 1]    ,     _lowerCamelCase = [1, 6, 6, 6, 6, 6, 6]    ,     _lowerCamelCase = 0.25    ,     _lowerCamelCase = "swish"    ,     _lowerCamelCase = 2560    ,     _lowerCamelCase = "mean"    ,     _lowerCamelCase = 0.02    ,     _lowerCamelCase = 0.0_01    ,     _lowerCamelCase = 0.99    ,     _lowerCamelCase = 0.5    ,     _lowerCamelCase = 0.2    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )

     lowerCAmelCase_			=      num_channels
     lowerCAmelCase_			=      image_size
     lowerCAmelCase_			=      width_coefficient
     lowerCAmelCase_			=      depth_coefficient
     lowerCAmelCase_			=      depth_divisor
     lowerCAmelCase_			=      kernel_sizes
     lowerCAmelCase_			=      in_channels
     lowerCAmelCase_			=      out_channels
     lowerCAmelCase_			=      depthwise_padding
     lowerCAmelCase_			=      strides
     lowerCAmelCase_			=      num_block_repeats
     lowerCAmelCase_			=      expand_ratios
     lowerCAmelCase_			=      squeeze_expansion_ratio
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dim
     lowerCAmelCase_			=      pooling_type
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      batch_norm_eps
     lowerCAmelCase_			=      batch_norm_momentum
     lowerCAmelCase_			=      dropout_rate
     lowerCAmelCase_			=      drop_connect_rate
     lowerCAmelCase_			=      sum(_lowerCamelCase ) * 4






class     __UpperCAmelCase       ( __a		):
  __A :				str											= version.parse('1.11'		)





  @property
  def 						UpperCAmelCase_	(	self ):
     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ] )







  @property
  def 						UpperCAmelCase_	(	self ):
     return 1E-5





