

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_   							=  logging.get_logger(__name__)

lowercase_   							=  {
    '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_   							=  {
    '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 (     A__      :  List[Any]	)       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			=EfficientNetConfig()
      _lowercase      			=CONFIG_MAP[model_name]['hidden_dim']
      _lowercase      			=CONFIG_MAP[model_name]['width_coef']
      _lowercase      			=CONFIG_MAP[model_name]['depth_coef']
      _lowercase      			=CONFIG_MAP[model_name]['image_size']
      _lowercase      			=CONFIG_MAP[model_name]['dropout_rate']
      _lowercase      			=CONFIG_MAP[model_name]['dw_padding']

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

      _lowercase      			=idalabel
      _lowercase      			={v: k for k, v in idalabel.items()}
      return config
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



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





      """simple docstring"""



      _lowercase      			=CONFIG_MAP[model_name]['image_size']
      _lowercase      			=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=A__ ,  )
      return preprocessor
def 	a (     A__      :  Tuple	)       -> Any:





      """simple docstring"""



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

      _lowercase      			=[]
      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:
            _lowercase      			=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')	)

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

      _lowercase      			='classifier.weight'
      _lowercase      			='classifier.bias'
      return key_mapping
def 	a (     A__      :  Dict ,  A__      :  str ,  A__      :  List[Any]	)       -> Dict:





      """simple docstring"""



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

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

            # Replace HF parameters with original TF model parameters
            assert hf_params[hf_key].shape == new_hf_value.shape
            hf_params[hf_key].copy_(A__	)
@torch.no_grad()
def 	a (     A__      :  Optional[int] ,  A__      :  str ,  A__      :  str ,  A__      :  Tuple	)       -> Optional[int]:





      """simple docstring"""



      _lowercase      			=model_classes[model_name](
          include_top=A__ ,  weights='imagenet' ,  input_tensor=A__ ,  input_shape=A__ ,  pooling=A__ ,  classes=1000 ,  classifier_activation='softmax' ,  )

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

      # Load HuggingFace model
      _lowercase      			=get_efficientnet_config(A__	)
      _lowercase      			=EfficientNetForImageClassification(A__	).eval()
      _lowercase      			=hf_model.state_dict()

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

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

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

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

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

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


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





from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase_   							=  HfArgumentParser(InitializationArguments)
lowercase_   							=  parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase_   							=  AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase_   							=  {
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowercase_   							=  AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase_   							=  AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)


