



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 __future__ import annotations

import json

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

_lowerCAmelCase	:   List[str]    =   {"UserAgent": UserAgent().random}







def     UpperCAmelCase_		(					snake_case__					)	->     dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  script.contents[0]
				lowerCAmelCase__		  =  json.loads(data[data.find('{"config"'					) : -1]					)
				return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]


class 	__snake_case   :
		def __init__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  f'https://www.instagram.com/{username}/'
						lowerCAmelCase__		  =  self.get_json()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  requests.get(self.url    ,headers=a_						).text
						lowerCAmelCase__		  =  BeautifulSoup(a_    ,'html.parser'						).find_all('script'						)
						try:
										return extract_user_profile(scripts[4]						)
						except (json.decoder.JSONDecodeError, KeyError):
										return extract_user_profile(scripts[3]						)
		def __repr__(							self						):







						"""simple docstring"""
						return f'{self.__class__.__name__}(\'{self.username}\')'
		def __str__(							self						):







						"""simple docstring"""
						return f'{self.fullname} ({self.username}) is {self.biography}'
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["username"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["full_name"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["biography"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["business_email"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["external_url"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["edge_followed_by"]["count"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["edge_follow"]["count"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["edge_owner_to_timeline_media"]["count"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["profile_pic_url_hd"]
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["is_verified"]



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.user_data["is_private"]







def     UpperCAmelCase_		(					snake_case__ = "github"					)	->     None:



				"""simple docstring"""





				import os

				if os.environ.get('CI'					):
								return  # test failing on GitHub Actions
				lowerCAmelCase__		  =  InstagramUser(snake_case__					)
				assert instagram_user.user_data
				assert isinstance(instagram_user.user_data    ,			snake_case__					)
				assert instagram_user.username == username
				if username != "github":
								return
				assert instagram_user.fullname == "GitHub"
				assert instagram_user.biography == "Built for developers."
				assert instagram_user.number_of_posts > 150
				assert instagram_user.number_of_followers > 120000
				assert instagram_user.number_of_followings > 15
				assert instagram_user.email == "support@github.com"
				assert instagram_user.website == "https://github.com/readme"
				assert instagram_user.profile_picture_url.startswith('https://instagram.'					)
				assert instagram_user.is_verified is True
				assert instagram_user.is_private is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	_lowerCAmelCase	:   Optional[int]    =   InstagramUser("github")
	print(instagram_user)
	print(f"""{instagram_user.number_of_posts = }""")
	print(f"""{instagram_user.number_of_followers = }""")
	print(f"""{instagram_user.number_of_followings = }""")
	print(f"""{instagram_user.email = }""")
	print(f"""{instagram_user.website = }""")
	print(f"""{instagram_user.profile_picture_url = }""")
	print(f"""{instagram_user.is_verified = }""")
	print(f"""{instagram_user.is_private = }""")
