



"""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()
lowercase__					:						int												=			logging.get_logger(__name__)

lowercase__					:						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,
}

lowercase__					:						List[str]												=			{
    '''b0''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_24,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_40,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 14_08,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_60,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 15_36,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_00,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 17_92,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_80,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 20_48,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_56,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 23_04,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_28,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 25_60,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_00,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}




def __lowercase    (							_a					):
    snake_case_				:  Union[str, Any]       =			EfficientNetConfig()
    snake_case_				:  List[str]       =			CONFIG_MAP[model_name]['''hidden_dim''']
    snake_case_				:  Tuple       =			CONFIG_MAP[model_name]['''width_coef''']
    snake_case_				:  str       =			CONFIG_MAP[model_name]['''depth_coef''']
    snake_case_				:  Dict       =			CONFIG_MAP[model_name]['''image_size''']
    snake_case_				:  Tuple       =			CONFIG_MAP[model_name]['''dropout_rate''']
    snake_case_				:  Any       =			CONFIG_MAP[model_name]['''dw_padding''']

    snake_case_				:  int       =			'''huggingface/label-files'''
    snake_case_				:  List[str]       =			'''imagenet-1k-id2label.json'''
    snake_case_				:  Union[str, Any]       =			1_000
    snake_case_				:  str       =			json.load(open(hf_hub_download(_a	,    _a	,    repo_type='''dataset'''					)	,    '''r'''					)					)
    snake_case_				:  Dict       =			{int(_a					): v for k, v in idalabel.items()}

    snake_case_				:  List[str]       =			idalabel
    snake_case_				:  Tuple       =			{v: k for k, v in idalabel.items()}
    return config




def __lowercase    (							):
    snake_case_				:  Optional[int]       =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_				:  Tuple       =			Image.open(requests.get(_a	,    stream=_a					).raw					)
    return im




def __lowercase    (							_a					):
    snake_case_				:  Optional[Any]       =			CONFIG_MAP[model_name]['''image_size''']
    snake_case_				:  List[str]       =			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=_a	,    )
    return preprocessor




def __lowercase    (							_a					):
    snake_case_				:  Union[str, Any]       =			[v.split('''_'''					)[0].split('''block'''					)[1] for v in original_param_names if v.startswith('''block'''					)]
    snake_case_				:  int       =			sorted(set(_a					)					)
    snake_case_				:  Tuple       =			len(_a					)
    snake_case_				:  Union[str, Any]       =			{b: str(_a					) for b, i in zip(_a	,    range(_a					)					)}

    snake_case_				:  str       =			[]
    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_				:  Any       =			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_				:  Dict       =			{}
    for item in rename_keys:
        if item[0] in original_param_names:
            snake_case_				:  str       =			'''efficientnet.''' + item[1]

    snake_case_				:  List[str]       =			'''classifier.weight'''
    snake_case_				:  List[str]       =			'''classifier.bias'''
    return key_mapping




def __lowercase    (							_a	,    _a	,    _a					):
    for key, value in tf_params.items():
        if "normalization" in key:
            continue

        snake_case_				:  Tuple       =			key_mapping[key]
        if "_conv" in key and "kernel" in key:
            snake_case_				:  str       =			torch.from_numpy(_a					).permute(3	,    2	,    0	,    1					)
        elif "depthwise_kernel" in key:
            snake_case_				:  Union[str, Any]       =			torch.from_numpy(_a					).permute(2	,    3	,    0	,    1					)
        elif "kernel" in key:
            snake_case_				:  List[Any]       =			torch.from_numpy(np.transpose(_a					)					)
        else:
            snake_case_				:  List[str]       =			torch.from_numpy(_a					)

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




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Dict       =			model_classes[model_name](
        include_top=_a	,    weights='''imagenet'''	,    input_tensor=_a	,    input_shape=_a	,    pooling=_a	,    classes=1_000	,    classifier_activation='''softmax'''	,    )

    snake_case_				:  Optional[int]       =			original_model.trainable_variables
    snake_case_				:  List[Any]       =			original_model.non_trainable_variables
    snake_case_				:  Optional[int]       =			{param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        snake_case_				:  Any       =			param.numpy()
    snake_case_				:  List[Any]       =			list(tf_params.keys()					)

    # Load HuggingFace model
    snake_case_				:  Tuple       =			get_efficientnet_config(_a					)
    snake_case_				:  Tuple       =			EfficientNetForImageClassification(_a					).eval()
    snake_case_				:  Dict       =			hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print('''Converting parameters...'''					)
    snake_case_				:  List[str]       =			rename_keys(_a					)
    replace_params(_a	,    _a	,    _a					)

    # Initialize preprocessor and preprocess input image
    snake_case_				:  Optional[Any]       =			convert_image_processor(_a					)
    snake_case_				:  List[Any]       =			preprocessor(images=prepare_img()	,    return_tensors='''pt'''					)

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        snake_case_				:  Union[str, Any]       =			hf_model(**_a					)
    snake_case_				:  List[str]       =			outputs.logits.detach().numpy()

    # Original model inference
    snake_case_				:  Union[str, Any]       =			False
    snake_case_				:  List[str]       =			CONFIG_MAP[model_name]['''image_size''']
    snake_case_				:  str       =			prepare_img().resize((image_size, image_size)	,    resample=PIL.Image.NEAREST					)
    snake_case_				:  Tuple       =			image.img_to_array(_a					)
    snake_case_				:  int       =			np.expand_dims(_a	,    axis=0					)
    snake_case_				:  Union[str, Any]       =			original_model.predict(_a					)

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

    if push_to_hub:
        # Push model and image processor to hub
        print(f"Pushing converted {model_name} to the hub..."					)
        snake_case_				:  Union[str, Any]       =			f"efficientnet-{model_name}"
        preprocessor.push_to_hub(_a					)
        hf_model.push_to_hub(_a					)


if __name__ == "__main__":
      lowercase__					:						Optional[Any]												=			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''')

      lowercase__					:						int												=			parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










"""simple docstring"""

import math
import sys

import cva
import numpy as np




def __lowercase    (							_a	,    _a					):
    # For applying gaussian function for each element in matrix.
    snake_case_				:  Dict       =			math.sqrt(_a					)
    snake_case_				:  Tuple       =			1 / (sigma * math.sqrt(2 * math.pi					))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5					)




def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Optional[int]       =			kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]




def __lowercase    (							_a	,    _a					):
    # Creates a gaussian kernel of given dimension.
    snake_case_				:  Union[str, Any]       =			np.zeros((kernel_size, kernel_size)					)
    for i in range(0	,    _a					):
        for j in range(0	,    _a					):
            snake_case_				:  Union[str, Any]       =			math.sqrt(
                abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
    return vec_gaussian(_a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    ):
    snake_case_				:  int       =			np.zeros(img.shape					)
    snake_case_				:  str       =			get_gauss_kernel(_a	,    _a					)
    snake_case_,			snake_case_				:  Any       =			img.shape
    for i in range(kernel_size // 2	,    size_x - kernel_size // 2					):
        for j in range(kernel_size // 2	,    size_y - kernel_size // 2					):
            snake_case_				:  List[Any]       =			get_slice(_a	,    _a	,    _a	,    _a					)
            snake_case_				:  Union[str, Any]       =			img_s - img_s[kernel_size // 2, kernel_size // 2]
            snake_case_				:  Optional[Any]       =			vec_gaussian(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  List[Any]       =			np.sum(_a					) / np.sum(_a					)
            snake_case_				:  Dict       =			val
    return imga




def __lowercase    (							_a					):
    snake_case_				:  Optional[int]       =			args[1] if args[1:] else '''../image_data/lena.jpg'''
    snake_case_				:  int       =			float(args[2]					) if args[2:] else 1.0
    snake_case_				:  Dict       =			float(args[3]					) if args[3:] else 1.0
    if args[4:]:
        snake_case_				:  Optional[Any]       =			int(args[4]					)
        snake_case_				:  Union[str, Any]       =			kernel_size + abs(kernel_size % 2 - 1					)
    else:
        snake_case_				:  Union[str, Any]       =			5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
     lowercase__					,lowercase__					,lowercase__					,lowercase__					:						Tuple												=			parse_args(sys.argv)
     lowercase__					:						str												=			cva.imread(filename, 0)
     cva.imshow('''input image''', img)

     lowercase__					:						Tuple												=			img / 2_55
     lowercase__					:						Optional[int]												=			out.astype('''float32''')
     lowercase__					:						Union[str, Any]												=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     lowercase__					:						List[str]												=			out * 2_55
     lowercase__					:						List[str]												=			np.uinta(out)
     cva.imshow('''output image''', out)
     cva.waitKey(0)
     cva.destroyAllWindows()





