






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_           = logging.get_logger(__name__)

lowerCAmelCase_           = {
    '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_           = {
    '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		(							UpperCAmelCase	:       Optional[int]   ):
    A	 =      EfficientNetConfig()
    A	 =      CONFIG_MAP[model_name]['hidden_dim']
    A	 =      CONFIG_MAP[model_name]['width_coef']
    A	 =      CONFIG_MAP[model_name]['depth_coef']
    A	 =      CONFIG_MAP[model_name]['image_size']
    A	 =      CONFIG_MAP[model_name]['dropout_rate']
    A	 =      CONFIG_MAP[model_name]['dw_padding']

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

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







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







def      snake_case		(							UpperCAmelCase	:       Optional[Any]   ):
    A	 =      CONFIG_MAP[model_name]['image_size']
    A	 =      EfficientNetImageProcessor(
        size={'height': size, 'width': size},						image_mean=[0.485, 0.456, 0.406],						image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163],						do_center_crop=lowercase__,						)
    return preprocessor







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

    A	 =      []
    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:
        A	 =      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')   )

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

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







def      snake_case		(							UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       str,						UpperCAmelCase	:       List[Any]   ):







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

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

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







@torch.no_grad()
def      snake_case		(							UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       int,						UpperCAmelCase	:       List[str],						UpperCAmelCase	:       List[str]   ):
    A	 =      model_classes[model_name](
        include_top=lowercase__,						weights='imagenet',						input_tensor=lowercase__,						input_shape=lowercase__,						pooling=lowercase__,						classes=10_00,						classifier_activation='softmax',						)

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

    # Load HuggingFace model
    A	 =      get_efficientnet_config(lowercase__   )
    A	 =      EfficientNetForImageClassification(lowercase__   ).eval()
    A	 =      hf_model.state_dict()

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

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

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

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

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

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


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













import copy
import re
class      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   "hp"
		snake_case    =   {}
		snake_case    =   None
		@classmethod
		def       A(  cls		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   int:


						'''simple docstring'''
						A	 =      prefix
						A	 =      defaults
						cls.build_naming_info()
		@staticmethod
		def       A(  _SCREAMING_SNAKE_CASE		:       List[str]						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						if len(_SCREAMING_SNAKE_CASE		) == 0:
										return ""
						A	 =      None
						if any(char.isdigit() for char in word		):
										raise Exception(f'Parameters should not contain numbers: \'{word}\' contains a number'		)
						if word in info["short_word"]:
										return info["short_word"][word]
						for prefix_len in range(1						,len(_SCREAMING_SNAKE_CASE		) + 1		):
										A	 =      word[:prefix_len]
										if prefix in info["reverse_short_word"]:
														continue
										else:
														A	 =      prefix
														break

						if short_word is None:
										# Paranoid fallback
										def int_to_alphabetic(_SCREAMING_SNAKE_CASE		:       Optional[int]		):
														A	 =      ''
														while integer != 0:
																		A	 =      chr(ord('A'		) + integer % 1_0		) + s
																		integer //= 1_0
														return s

										A	 =      0
										while True:
														A	 =      word + '#' + int_to_alphabetic(_SCREAMING_SNAKE_CASE		)
														if sword in info["reverse_short_word"]:
																		continue
														else:
																		A	 =      sword
																		break

						A	 =      short_word
						A	 =      word
						return short_word
		@staticmethod
		def       A(  _SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   int:


						'''simple docstring'''
						A	 =      param_name.split('_'		)

						A	 =      [TrialShortNamer.shortname_for_word(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		) for word in words]

						# We try to create a separatorless short name, but if there is a collision we have to fallback
						# to a separated short name
						A	 =      ['', '_']

						for separator in separators:
										A	 =      separator.join(_SCREAMING_SNAKE_CASE		)
										if shortname not in info["reverse_short_param"]:
														A	 =      shortname
														A	 =      param_name
														return shortname

						return param_name
		@staticmethod
		def       A(  _SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      TrialShortNamer.shortname_for_key(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						A	 =      short_name
						A	 =      param_name
		@classmethod
		def       A(  cls		:       Optional[int]		)    ->   str:


						'''simple docstring'''
						if cls.NAMING_INFO is not None:
										return

						A	 =      {
						    'short_word': {},
						    'reverse_short_word': {},
						    'short_param': {},
						    'reverse_short_param': {},
						}

						A	 =      list(cls.DEFAULTS.keys()		)

						for k in field_keys:
										cls.add_new_param_name(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)

						A	 =      info
		@classmethod
		def       A(  cls		:       List[str]						,_SCREAMING_SNAKE_CASE		:       str		)    ->   List[Any]:


						'''simple docstring'''
						cls.build_naming_info()
						assert cls.PREFIX is not None
						A	 =      [copy.copy(cls.PREFIX		)]

						for k, v in params.items():
										if k not in cls.DEFAULTS:
														raise Exception(f'You should provide a default value for the param name {k} with value {v}'		)
										if v == cls.DEFAULTS[k]:
														# The default value is not added to the name
														continue

										A	 =      cls.NAMING_INFO['short_param'][k]

										if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
														A	 =      1 if v else 0

										A	 =      '' if isinstance(_SCREAMING_SNAKE_CASE						,(int, float)		) else '-'
										A	 =      f'{key}{sep}{v}'
										name.append(_SCREAMING_SNAKE_CASE		)

						return "_".join(_SCREAMING_SNAKE_CASE		)







		@classmethod
		def       A(  cls		:       int						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   str:


						'''simple docstring'''
						A	 =      repr[len(cls.PREFIX		) + 1 :]
						if repr == "":
										A	 =      []
						else:
										A	 =      repr.split('_'		)

						A	 =      {}

						for value in values:
										if "-" in value:
														A ,		A	 =      value.split('-'		)
										else:
														A	 =      re.sub('[0-9.]'						,''						,_SCREAMING_SNAKE_CASE		)
														A	 =      float(re.sub('[^0-9.]'						,''						,_SCREAMING_SNAKE_CASE		)		)

										A	 =      cls.NAMING_INFO['reverse_short_param'][p_k]

										A	 =      p_v

						for k in cls.DEFAULTS:
										if k not in parameters:
														A	 =      cls.DEFAULTS[k]

						return parameters





