


'''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()
__SCREAMING_SNAKE_CASE					:				Dict									=		logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{
    '''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       a_				(	UpperCamelCase_ ):
							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_			    =		1_0_0_0
							A_			    =		json.load(open(hf_hub_download(UpperCamelCase_						,  UpperCamelCase_						,  repo_type="dataset" )						,  "r" ) )
							A_			    =		{int(UpperCamelCase_ ): v for k, v in idalabel.items()}

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







def       a_				(	):
							A_			    =		"http://images.cocodataset.org/val2017/000000039769.jpg"
							A_			    =		Image.open(requests.get(UpperCamelCase_						,  stream=UpperCamelCase_ ).raw )
							return im







def       a_				(	UpperCamelCase_ ):
							A_			    =		CONFIG_MAP[model_name]["image_size"]
							A_			    =		EfficientNetImageProcessor(
							    size={"height": size, "width": size}						,  image_mean=[0.485, 0.456, 0.406]						,  image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]						,  do_center_crop=UpperCamelCase_						,  )
							return preprocessor







def       a_				(	UpperCamelCase_ ):
							A_			    =		[v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )]
							A_			    =		sorted(set(UpperCamelCase_ ) )
							A_			    =		len(UpperCamelCase_ )
							A_			    =		{b: str(UpperCamelCase_ ) for b, i in zip(UpperCamelCase_						,  range(UpperCamelCase_ ) )}

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

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







@torch.no_grad()
def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		model_classes[model_name](
							    include_top=UpperCamelCase_						,  weights="imagenet"						,  input_tensor=UpperCamelCase_						,  input_shape=UpperCamelCase_						,  pooling=UpperCamelCase_						,  classes=1_0_0_0						,  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(UpperCamelCase_ )
							A_			    =		EfficientNetForImageClassification(UpperCamelCase_ ).eval()
							A_			    =		hf_model.state_dict()

							# Create src-to-dst parameter name mapping dictionary
							print("Converting parameters..." )
							A_			    =		rename_keys(UpperCamelCase_ )
							replace_params(UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ )

							# Initialize preprocessor and preprocess input image
							A_			    =		convert_image_processor(UpperCamelCase_ )
							A_			    =		preprocessor(images=prepare_img()						,  return_tensors="pt" )

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

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

							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(UpperCamelCase_ )
														hf_model.push_to_hub(UpperCamelCase_ )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				str									=		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''')

							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)









'''simple docstring'''
__SCREAMING_SNAKE_CASE					:				Optional[int]									=		{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


__SCREAMING_SNAKE_CASE					:				Tuple									=		{value: key for key, value in encode_dict.items()}







def       a_				(	UpperCamelCase_ ):
							A_			    =		""
							for letter in word.lower():
														if letter.isalpha() or letter == " ":
																					encoded += encode_dict[letter]
														else:
																					raise Exception("encode() accepts only letters of the alphabet and spaces" )
							return encoded







def       a_				(	UpperCamelCase_ ):
							if set(UpperCamelCase_ ) - {"A", "B", " "} != set():
														raise Exception("decode() accepts only 'A', 'B' and spaces" )
							A_			    =		""
							for word in coded.split():
														while len(UpperCamelCase_ ) != 0:
																					decoded += decode_dict[word[:5]]
																					A_			    =		word[5:]
														decoded += " "
							return decoded.strip()


if __name__ == "__main__":
							from doctest import testmod

							testmod()





