




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''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}







def 					lowerCAmelCase    (      UpperCAmelCase			)    ->Tuple:






					"""simple docstring"""






					__magic_name__   :				Tuple						  =				EfficientNetConfig()
					__magic_name__   :				List[Any]						  =				CONFIG_MAP[model_name]['''hidden_dim''']
					__magic_name__   :				Dict						  =				CONFIG_MAP[model_name]['''width_coef''']
					__magic_name__   :				List[Any]						  =				CONFIG_MAP[model_name]['''depth_coef''']
					__magic_name__   :				Optional[int]						  =				CONFIG_MAP[model_name]['''image_size''']
					__magic_name__   :				str						  =				CONFIG_MAP[model_name]['''dropout_rate''']
					__magic_name__   :				List[Any]						  =				CONFIG_MAP[model_name]['''dw_padding''']

					__magic_name__   :				Dict						  =				'''huggingface/label-files'''
					__magic_name__   :				int						  =				'''imagenet-1k-id2label.json'''
					__magic_name__   :				int						  =				1000
					__magic_name__   :				List[str]						  =				json.load(open(hf_hub_download(__snake_case,      __snake_case,      repo_type='''dataset'''			),      '''r'''			)			)
					__magic_name__   :				Union[str, Any]						  =				{int(__snake_case			): v for k, v in idalabel.items()}

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







def 					lowerCAmelCase    (      )    ->List[str]:






					"""simple docstring"""






					__magic_name__   :				Dict						  =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__magic_name__   :				Optional[Any]						  =				Image.open(requests.get(__snake_case,      stream=__snake_case			).raw			)
					return im







def 					lowerCAmelCase    (      UpperCAmelCase			)    ->Tuple:






					"""simple docstring"""






					__magic_name__   :				List[str]						  =				CONFIG_MAP[model_name]['''image_size''']
					__magic_name__   :				Union[str, Any]						  =				EfficientNetImageProcessor(
					    size={'''height''': size, '''width''': size},      image_mean=[0.4_85, 0.4_56, 0.4_06],      image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63],      do_center_crop=__snake_case,      )
					return preprocessor







def 					lowerCAmelCase    (      UpperCAmelCase			)    ->str:






					"""simple docstring"""






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

					__magic_name__   :				Optional[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:
										__magic_name__   :				Optional[int]						  =				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''')			)

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

					__magic_name__   :				str						  =				'''classifier.weight'''
					__magic_name__   :				List[str]						  =				'''classifier.bias'''
					return key_mapping







def 					lowerCAmelCase    (      UpperCAmelCase,      UpperCAmelCase,      UpperCAmelCase			)    ->Optional[int]:






					"""simple docstring"""






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

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






					"""simple docstring"""






					__magic_name__   :				Optional[Any]						  =				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''',      )

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

					# Load HuggingFace model
					__magic_name__   :				Tuple						  =				get_efficientnet_config(__snake_case			)
					__magic_name__   :				int						  =				EfficientNetForImageClassification(__snake_case			).eval()
					__magic_name__   :				Any						  =				hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print('''Converting parameters...'''			)
					__magic_name__   :				Optional[int]						  =				rename_keys(__snake_case			)
					replace_params(__snake_case,      __snake_case,      __snake_case			)

					# Initialize preprocessor and preprocess input image
					__magic_name__   :				Union[str, Any]						  =				convert_image_processor(__snake_case			)
					__magic_name__   :				Optional[int]						  =				preprocessor(images=prepare_img(),      return_tensors='''pt'''			)

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										__magic_name__   :				Optional[int]						  =				hf_model(**__snake_case			)
					__magic_name__   :				Tuple						  =				outputs.logits.detach().numpy()

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


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)




'''simple docstring'''

def snake_case_      (					__snake_case  :							int	,   __snake_case  :							int	,   __snake_case  :							int)			->					float:
   lowerCAmelCase_			=      (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
   # formula for sum of series
   return total



def snake_case_      (					)			->					str:
   print(sum_of_series(1	,   1	,   10))


if __name__ == "__main__":
 import doctest

 doctest.testmod()





