
def 	a_		(	__lowercase       :     Dict	, __lowercase       :     Any			)			->  Dict:
			_snake_case          =      0
			_snake_case          =      len(__lowercase			) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						_snake_case          =      left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(__lowercase			):
									return None

						_snake_case          =      sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												_snake_case          =      left
												_snake_case          =      point
									elif point > right:
												_snake_case          =      right
												_snake_case          =      point
									else:
												if item < current_item:
															_snake_case          =      point - 1
												else:
															_snake_case          =      point + 1
			return None






def 	a_		(	__lowercase       :     Any	, __lowercase       :     str	, __lowercase       :     List[Any]	, __lowercase       :     Optional[Any]			)			->  Dict:

			# avoid divided by 0 during interpolation
			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			_snake_case          =      left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(__lowercase			):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(__lowercase	, __lowercase	, __lowercase	, __lowercase			)
			elif point > right:
						return interpolation_search_by_recursion(__lowercase	, __lowercase	, __lowercase	, __lowercase			)
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    __lowercase	, __lowercase	, __lowercase	, point - 1			)
						else:
									return interpolation_search_by_recursion(
									    __lowercase	, __lowercase	, point + 1	, __lowercase			)






def 	a_		(	__lowercase       :     Tuple			)			->  Optional[int]:
			if collection != sorted(__lowercase			):
						raise ValueError('Collection must be ascending sorted'			)
			return True


if __name__ == "__main__":
			import sys
			_lowerCamelCase	:    int          =      0
			if debug == 1:
						_lowerCamelCase	:    Optional[int]          =      [10, 30, 40, 45, 50, 66, 77, 93]
						try:
									__assert_sorted(collection)
						except ValueError:
									sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
						_lowerCamelCase	:    Any          =      67

			_lowerCamelCase	:    Any          =      interpolation_search(collection, target)
			if result is not None:
						print(F'{target} found at positions: {result}')
			else:
						print('''Not found''')

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	:    int          =      logging.get_logger(__name__)

_lowerCamelCase	:    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,
}

_lowerCamelCase	:    List[str]          =      {
    '''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 	a_		(	__lowercase       :     int			)			->  Optional[int]:
			_snake_case          =      EfficientNetConfig()
			_snake_case          =      CONFIG_MAP[model_name]['hidden_dim']
			_snake_case          =      CONFIG_MAP[model_name]['width_coef']
			_snake_case          =      CONFIG_MAP[model_name]['depth_coef']
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      CONFIG_MAP[model_name]['dropout_rate']
			_snake_case          =      CONFIG_MAP[model_name]['dw_padding']

			_snake_case          =      'huggingface/label-files'
			_snake_case          =      'imagenet-1k-id2label.json'
			_snake_case          =      1_000
			_snake_case          =      json.load(open(hf_hub_download(__lowercase	, __lowercase	, repo_type='dataset'			)	, 'r'			)			)
			_snake_case          =      {int(__lowercase			): v for k, v in idalabel.items()}

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






def 	a_		(	)			->  Any:
			_snake_case          =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case          =      Image.open(requests.get(__lowercase	, stream=__lowercase			).raw			)
			return im






def 	a_		(	__lowercase       :     Union[str, Any]			)			->  Tuple:
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      EfficientNetImageProcessor(
			    size={'height': size, 'width': size}	, image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	, image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]	, do_center_crop=__lowercase	, )
			return preprocessor






def 	a_		(	__lowercase       :     str			)			->  List[Any]:
			_snake_case          =      [v.split('_'			)[0].split('block'			)[1] for v in original_param_names if v.startswith('block'			)]
			_snake_case          =      sorted(set(__lowercase			)			)
			_snake_case          =      len(__lowercase			)
			_snake_case          =      {b: str(__lowercase			) for b, i in zip(__lowercase	, range(__lowercase			)			)}

			_snake_case          =      []
			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:
						_snake_case          =      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')			)

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

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






def 	a_		(	__lowercase       :     Any	, __lowercase       :     Any	, __lowercase       :     Any			)			->  Optional[Any]:
			for key, value in tf_params.items():
						if "normalization" in key:
									continue

						_snake_case          =      key_mapping[key]
						if "_conv" in key and "kernel" in key:
									_snake_case          =      torch.from_numpy(__lowercase			).permute(3	, 2	, 0	, 1			)
						elif "depthwise_kernel" in key:
									_snake_case          =      torch.from_numpy(__lowercase			).permute(2	, 3	, 0	, 1			)
						elif "kernel" in key:
									_snake_case          =      torch.from_numpy(np.transpose(__lowercase			)			)
						else:
									_snake_case          =      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 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Any	, __lowercase       :     int	, __lowercase       :     str			)			->  Dict:
			_snake_case          =      model_classes[model_name](
			    include_top=__lowercase	, weights='imagenet'	, input_tensor=__lowercase	, input_shape=__lowercase	, pooling=__lowercase	, classes=1_000	, classifier_activation='softmax'	, )

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

			# Load HuggingFace model
			_snake_case          =      get_efficientnet_config(__lowercase			)
			_snake_case          =      EfficientNetForImageClassification(__lowercase			).eval()
			_snake_case          =      hf_model.state_dict()

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

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

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

			# Original model inference
			_snake_case          =      False
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      prepare_img().resize((image_size, image_size)	, resample=PIL.Image.NEAREST			)
			_snake_case          =      image.img_to_array(__lowercase			)
			_snake_case          =      np.expand_dims(__lowercase	, axis=0			)
			_snake_case          =      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...'''			)
						_snake_case          =      f'''efficientnet-{model_name}'''
						preprocessor.push_to_hub(__lowercase			)
						hf_model.push_to_hub(__lowercase			)


if __name__ == "__main__":
			_lowerCamelCase	:    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''')

			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)