



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     :str			       =logging.get_logger(__name__)

__snake_case     :List[str]			       ={
    '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     :Union[str, Any]			       ={
    '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_	(      lowerCAmelCase__	:	Optional[int]				)       ->							List[str]:

							'''simple docstring'''






							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      						=    1000
							A      						=    json.load(open(hf_hub_download(lowerCAmelCase__			,	lowerCAmelCase__			,	repo_type='dataset'				)			,	'r'				)				)
							A      						=    {int(lowerCAmelCase__				): v for k, v in idalabel.items()}

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






def  lowerCamelCase_	(      )       ->							Optional[Any]:

							'''simple docstring'''






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






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict				)       ->							Any:

							'''simple docstring'''






							A      						=    CONFIG_MAP[model_name]['image_size']
							A      						=    EfficientNetImageProcessor(
							    size={'height': size, 'width': size}			,	image_mean=[0.485, 0.456, 0.406]			,	image_std=[0.47853944, 0.4732864, 0.47434163]			,	do_center_crop=lowerCAmelCase__			,	)
							return preprocessor






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							Dict:

							'''simple docstring'''






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

							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  lowerCamelCase_	(      lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Dict				)       ->							int:

							'''simple docstring'''






							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(lowerCAmelCase__				).permute(3			,	2			,	0			,	1				)
														elif "depthwise_kernel" in key:
																					A      						=    torch.from_numpy(lowerCAmelCase__				).permute(2			,	3			,	0			,	1				)
														elif "kernel" in key:
																					A      						=    torch.from_numpy(np.transpose(lowerCAmelCase__				)				)
														else:
																					A      						=    torch.from_numpy(lowerCAmelCase__				)

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






@torch.no_grad()
def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	int				)       ->							str:

							'''simple docstring'''






							A      						=    model_classes[model_name](
							    include_top=lowerCAmelCase__			,	weights='imagenet'			,	input_tensor=lowerCAmelCase__			,	input_shape=lowerCAmelCase__			,	pooling=lowerCAmelCase__			,	classes=1000			,	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(lowerCAmelCase__				)
							A      						=    EfficientNetForImageClassification(lowerCAmelCase__				).eval()
							A      						=    hf_model.state_dict()

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

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

							# HF model inference
							hf_model.eval()
							with torch.no_grad():
														A      						=    hf_model(**lowerCAmelCase__				)
							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(lowerCAmelCase__				)
							A      						=    np.expand_dims(lowerCAmelCase__			,	axis=0				)
							A      						=    original_model.predict(lowerCAmelCase__				)

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

							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(lowerCAmelCase__				)
														hf_model.push_to_hub(lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :int			       =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     :List[Any]			       =parser.parse_args()
				convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


SCREAMING_SNAKE_CASE__     :     int		    =       logging.get_logger(__name__)



@dataclass
class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= [
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]





	def __init__(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				for deprecated_arg in self.deprecated_args:
							if deprecated_arg in kwargs:
										SCREAMING_SNAKE_CASE_								=						deprecated_arg[3:]
										setattr(self      ,     SCREAMING_SNAKE_CASE      ,     not kwargs.pop(SCREAMING_SNAKE_CASE      )      )
										logger.warning(
										    f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
										    f' {positive_arg}={kwargs[positive_arg]}'      )

				SCREAMING_SNAKE_CASE_								=						kwargs.pop('torchscript'      ,     self.torchscript      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('torch_xla_tpu_print_metrics'      ,     self.torch_xla_tpu_print_metrics      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('fp16_opt_level'      ,     self.fpaa_opt_level      )
				super().__init__(**SCREAMING_SNAKE_CASE      )

	A    							= field(default=SCREAMING_SNAKE_CASE__  ,    metadata={'''help''': '''Trace the models using torchscript'''}     )
	A    							= field(default=SCREAMING_SNAKE_CASE__  ,    metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}     )
	A    							= field(
	    default='''O1'''  ,    metadata={
	        '''help''': (
	            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
	            '''See details at https://nvidia.github.io/apex/amp.html'''
	        )
	    }  ,    )





	@cached_property
	def 						A_(							self      )  ->					Tuple["torch.device", int]:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				logger.info('PyTorch: setting up devices'      )
				if not self.cuda:
							SCREAMING_SNAKE_CASE_								=						torch.device('cpu'      )
							SCREAMING_SNAKE_CASE_								=						0
				elif is_torch_tpu_available():
							SCREAMING_SNAKE_CASE_								=						xm.xla_device()
							SCREAMING_SNAKE_CASE_								=						0
				else:
							SCREAMING_SNAKE_CASE_								=						torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
							SCREAMING_SNAKE_CASE_								=						torch.cuda.device_count()
				return device, n_gpu





	@property
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				return is_torch_tpu_available() and self.tpu





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				# TODO(PVP): currently only single GPU is supported
				return torch.cuda.current_device()





	@property
	def 						A_(							self      )  ->					"torch.device":




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				return self._setup_devices[0]





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				return self._setup_devices[1]





	@property
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				return self.n_gpu > 0
