




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()
lowerCamelCase__ :   Dict					=							logging.get_logger(__name__)

lowerCamelCase__ :   List[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,
}

lowerCamelCase__ :   int					=							{
    'b0': {
        'hidden_dim': 1_280,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 224,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1_280,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 240,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 1_408,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 260,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 1_536,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 300,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 1_792,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 380,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2_048,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 456,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 2_304,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 528,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 2_560,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 600,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> Any:
      SCREAMING_SNAKE_CASE_							=			EfficientNetConfig()
      SCREAMING_SNAKE_CASE_							=			CONFIG_MAP[model_name]['hidden_dim']
      SCREAMING_SNAKE_CASE_							=			CONFIG_MAP[model_name]['width_coef']
      SCREAMING_SNAKE_CASE_							=			CONFIG_MAP[model_name]['depth_coef']
      SCREAMING_SNAKE_CASE_							=			CONFIG_MAP[model_name]['image_size']
      SCREAMING_SNAKE_CASE_							=			CONFIG_MAP[model_name]['dropout_rate']
      SCREAMING_SNAKE_CASE_							=			CONFIG_MAP[model_name]['dw_padding']

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

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

def 					UpperCAmelCase_							(      )					-> List[Any]:
      SCREAMING_SNAKE_CASE_							=			'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_							=			Image.open(requests.get(__UpperCAmelCase			,			stream=__UpperCAmelCase							).raw							)
      return im

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Tuple							)					-> Any:
      SCREAMING_SNAKE_CASE_							=			CONFIG_MAP[model_name]['image_size']
      SCREAMING_SNAKE_CASE_							=			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=__UpperCAmelCase			,			)
      return preprocessor

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int							)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE_							=			[v.split('_'							)[0].split('block'							)[1] for v in original_param_names if v.startswith('block'							)]
      SCREAMING_SNAKE_CASE_							=			sorted(set(__UpperCAmelCase							)							)
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			{b: str(__UpperCAmelCase							) for b, i in zip(__UpperCAmelCase			,			range(__UpperCAmelCase							)							)}

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

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

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	str							)					-> Any:
      for key, value in tf_params.items():
            if "normalization" in key:
                  continue

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

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

@torch.no_grad()
def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Any			,			__UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	Tuple			,			__UpperCAmelCase						:	int							)					-> int:
      SCREAMING_SNAKE_CASE_							=			model_classes[model_name](
          include_top=__UpperCAmelCase			,			weights='imagenet'			,			input_tensor=__UpperCAmelCase			,			input_shape=__UpperCAmelCase			,			pooling=__UpperCAmelCase			,			classes=10_00			,			classifier_activation='softmax'			,			)

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

      # Load HuggingFace model
      SCREAMING_SNAKE_CASE_							=			get_efficientnet_config(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			EfficientNetForImageClassification(__UpperCAmelCase							).eval()
      SCREAMING_SNAKE_CASE_							=			hf_model.state_dict()

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

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

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

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

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

      if push_to_hub:
            # Push model and image processor to hub
            print(f"Pushing converted {model_name} to the hub..."							)
            SCREAMING_SNAKE_CASE_							=			f"efficientnet-{model_name}"
            preprocessor.push_to_hub(__UpperCAmelCase							)
            hf_model.push_to_hub(__UpperCAmelCase							)


if __name__ == "__main__":
   lowerCamelCase__ :   Dict					=							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')

   lowerCamelCase__ :   Optional[Any]					=							parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			{}



 def       lowerCAmelCase_  (							self  :		List[str]						):
       print(self.vertex						)
       for i in self.vertex:
             print(_lowerCAmelCase				,				' -> '				,				' -> '.join([str(_lowerCAmelCase						) for j in self.vertex[i]]						)						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase						)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_							=			[to_vertex]



 def       lowerCAmelCase_  (							self  :		Dict						):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_							=			[False] * len(self.vertex						)

       # call the recursive helper function
       for i in range(len(self.vertex						)						):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		list						):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_							=			True

       print(_lowerCAmelCase				,				end=' '						)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)


if __name__ == "__main__":
   lowerCamelCase__ :   List[Any]					=							Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3