



"""simple docstring"""







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()
A    =			logging.get_logger(__name__)

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

A    =			{
    'b0': {
        'hidden_dim': 1_2_8_0,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_2_4,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1_2_8_0,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_4_0,
        'dropout_rate': 0.2,
        'dw_padding': [1_6],
    },
    'b2': {
        'hidden_dim': 1_4_0_8,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_6_0,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 1_6],
    },
    'b3': {
        'hidden_dim': 1_5_3_6,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_0_0,
        'dropout_rate': 0.3,
        'dw_padding': [5, 1_8],
    },
    'b4': {
        'hidden_dim': 1_7_9_2,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_8_0,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2_0_4_8,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_5_6,
        'dropout_rate': 0.4,
        'dw_padding': [1_3, 2_7],
    },
    'b6': {
        'hidden_dim': 2_3_0_4,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_2_8,
        'dropout_rate': 0.5,
        'dw_padding': [3_1],
    },
    'b7': {
        'hidden_dim': 2_5_6_0,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_0_0,
        'dropout_rate': 0.5,
        'dw_padding': [1_8],
    },
}

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							str:
							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__			(     ) ->							Union[str, Any]:
							A         =					'http://images.cocodataset.org/val2017/000000039769.jpg'
							A         =					Image.open(requests.get(lowerCamelCase__				,      stream=lowerCamelCase__			).raw			)
							return im

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Optional[Any]:
							A         =					CONFIG_MAP[model_name]['image_size']
							A         =					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=lowerCamelCase__				,      )
							return preprocessor

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Any:
							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__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							int:
							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__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			) ->							List[Any]:
							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__":
							A    =			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')

							A    =			parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








"""simple docstring"""







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__=None				,      lowerCamelCase__=None			) ->							Dict:
							return field(default_factory=lambda: default				,      metadata=lowerCamelCase__			)






@dataclass
class      UpperCAmelCase__		:
		lowerCAmelCase_       :	List[str] 							=     list_field(
		    default=[]   ,metadata={
		        """help""": (
		            """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
		            """ of all available models"""
		        )
		    }   ,)

		lowerCAmelCase_       :	List[int] 							=     list_field(
		    default=[8]   ,metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}     )

		lowerCAmelCase_       :	List[int] 							=     list_field(
		    default=[8, 32, 1_28, 5_12]   ,metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}   ,)

		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}   ,)
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}   ,)
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Use FP16 to accelerate inference."""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Benchmark training of model"""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Verbose memory tracing"""}     )
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}   ,)
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={
		        """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
		    }   ,)
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Trace memory line by line"""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Save result to a CSV file"""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Save all print statements in a log file"""}     )
		lowerCAmelCase_       :	bool 							=     field(default=UpperCamelCase   ,metadata={"""help""": """Whether to print environment information"""}     )
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={
		        """help""": (
		            """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
		            """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
		            """ for debugging / testing and on TPU."""
		        )
		    }   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""inference_time_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving time results to csv."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""inference_memory_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving memory results to csv."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""train_time_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving time results to csv for training."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""train_memory_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""env_info_{round(time()     )}.csv"""   ,metadata={"""help""": """CSV filename used if saving environment information."""}   ,)
		lowerCAmelCase_       :	str 							=     field(
		    default=f"""log_{round(time()     )}.csv"""   ,metadata={"""help""": """Log filename used if print statements are saved in log."""}   ,)
		lowerCAmelCase_       :	int 							=     field(default=3   ,metadata={"""help""": """Times an experiment will be run."""}     )
		lowerCAmelCase_       :	bool 							=     field(
		    default=UpperCamelCase   ,metadata={
		        """help""": (
		            """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
		            """ model weights."""
		        )
		    }   ,)



		def 					A_		( self  :    Dict				)			->						Optional[int]:






									'''simple docstring'''

									warnings.warn(
									    f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
									    ' are deprecated in general and it is advised to use external Benchmarking libraries '
									    ' to benchmark Transformer models.'      ,							snake_case      ,							)



		def 					A_		( self  :    str				)			->						List[Any]:






									'''simple docstring'''

									return json.dumps(dataclasses.asdict(self				)      ,							indent=2				)



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






									'''simple docstring'''

									if len(self.models				) <= 0:
																raise ValueError(
																    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
																    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'				)
									return self.models



		@property
		def 					A_		( self  :    Tuple				)			->						List[str]:






									'''simple docstring'''

									if not self.multi_process:
																return False
									elif self.is_tpu:
																logger.info('Multiprocessing is currently not possible on TPU.'				)
																return False
									else:
																return True



