

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()
snake_case_			  = logging.get_logger(__name__)

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

snake_case_			  = {
    '''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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Any			):



     '''simple docstring'''





     lowercase__    :   Tuple      =       EfficientNetConfig()
     lowercase__    :   Dict      =       CONFIG_MAP[model_name]['hidden_dim']
     lowercase__    :   List[str]      =       CONFIG_MAP[model_name]['width_coef']
     lowercase__    :   Tuple      =       CONFIG_MAP[model_name]['depth_coef']
     lowercase__    :   Union[str, Any]      =       CONFIG_MAP[model_name]['image_size']
     lowercase__    :   List[Any]      =       CONFIG_MAP[model_name]['dropout_rate']
     lowercase__    :   List[Any]      =       CONFIG_MAP[model_name]['dw_padding']

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

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

def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   Any      =       'http://images.cocodataset.org/val2017/000000039769.jpg'
     lowercase__    :   Tuple      =       Image.open(requests.get(SCREAMING_SNAKE_CASE_	,     stream=SCREAMING_SNAKE_CASE_			).raw			)
     return im

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





     lowercase__    :   Optional[int]      =       CONFIG_MAP[model_name]['image_size']
     lowercase__    :   int      =       EfficientNetImageProcessor(
         size={'height': size, 'width': size}	,     image_mean=[0.485, 0.456, 0.406]	,     image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]	,     do_center_crop=SCREAMING_SNAKE_CASE_	,     )
     return preprocessor

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[Any]			):



     '''simple docstring'''





     lowercase__    :   Dict      =       [v.split('_'			)[0].split('block'			)[1] for v in original_param_names if v.startswith('block'			)]
     lowercase__    :   Dict      =       sorted(set(SCREAMING_SNAKE_CASE_			)			)
     lowercase__    :   int      =       len(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Union[str, Any]      =       {b: str(SCREAMING_SNAKE_CASE_			) for b, i in zip(SCREAMING_SNAKE_CASE_	,     range(SCREAMING_SNAKE_CASE_			)			)}

     lowercase__    :   Any      =       []
     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__    :   Optional[Any]      =       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__    :   Optional[int]      =       {}
     for item in rename_keys:
          if item[0] in original_param_names:
               lowercase__    :   Union[str, Any]      =       'efficientnet.' + item[1]

     lowercase__    :   Union[str, Any]      =       'classifier.weight'
     lowercase__    :   List[str]      =       'classifier.bias'
     return key_mapping

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





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

          lowercase__    :   List[str]      =       key_mapping[key]
          if "_conv" in key and "kernel" in key:
               lowercase__    :   Optional[Any]      =       torch.from_numpy(SCREAMING_SNAKE_CASE_			).permute(3	,     2	,     0	,     1			)
          elif "depthwise_kernel" in key:
               lowercase__    :   Tuple      =       torch.from_numpy(SCREAMING_SNAKE_CASE_			).permute(2	,     3	,     0	,     1			)
          elif "kernel" in key:
               lowercase__    :   List[str]      =       torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE_			)			)
          else:
               lowercase__    :   str      =       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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int	,     SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]			):



     '''simple docstring'''





     lowercase__    :   Dict      =       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=1_000	,     classifier_activation='softmax'	,     )

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

     # Load HuggingFace model
     lowercase__    :   Any      =       get_efficientnet_config(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   str      =       EfficientNetForImageClassification(SCREAMING_SNAKE_CASE_			).eval()
     lowercase__    :   int      =       hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print('Converting parameters...'			)
     lowercase__    :   Union[str, Any]      =       rename_keys(SCREAMING_SNAKE_CASE_			)
     replace_params(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			)

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

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          lowercase__    :   Optional[Any]      =       hf_model(**SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Tuple      =       outputs.logits.detach().numpy()

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


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

       snake_case_			  = 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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    dict	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__      , lowercase__    :   List[str]      =       set(SCREAMING_SNAKE_CASE_			), [start]

     while stack:
          lowercase__    :   Union[str, Any]      =       stack.pop()
          explored.add(SCREAMING_SNAKE_CASE_			)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]			):
               if adj not in explored:
                    stack.append(SCREAMING_SNAKE_CASE_			)
     return explored


snake_case_			  = {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))

