




'''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       :						Optional[Any]								=		logging.get_logger(__name__)

a       :						Union[str, Any]								=		{
    '''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       :						int								=		{
    '''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 __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]						)      ->      List[Any]:
			__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			=		10_00
			__snake_case			=		json.load(open(hf_hub_download(_UpperCAmelCase		,				_UpperCAmelCase		,				repo_type="dataset"						)		,				"r"						)						)
			__snake_case			=		{int(_UpperCAmelCase						): v for k, v in idalabel.items()}

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


def __UpperCAmelCase	(   )      ->      int:
			__snake_case			=		"http://images.cocodataset.org/val2017/000000039769.jpg"
			__snake_case			=		Image.open(requests.get(_UpperCAmelCase		,				stream=_UpperCAmelCase						).raw						)
			return im


def __UpperCAmelCase	(   _UpperCAmelCase		:   Union[str, Any]						)      ->      Tuple:
			__snake_case			=		CONFIG_MAP[model_name]["image_size"]
			__snake_case			=		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 __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]						)      ->      str:
			__snake_case			=		[v.split("_"						)[0].split("block"						)[1] for v in original_param_names if v.startswith("block"						)]
			__snake_case			=		sorted(set(_UpperCAmelCase						)						)
			__snake_case			=		len(_UpperCAmelCase						)
			__snake_case			=		{b: str(_UpperCAmelCase						) for b, i in zip(_UpperCAmelCase		,				range(_UpperCAmelCase						)						)}

			__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 __UpperCAmelCase	(   _UpperCAmelCase		:   List[Any]		,				_UpperCAmelCase		:   str		,				_UpperCAmelCase		:   Dict						)      ->      Dict:
			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(_UpperCAmelCase						).permute(3		,				2		,				0		,				1						)
						elif "depthwise_kernel" in key:
									__snake_case			=		torch.from_numpy(_UpperCAmelCase						).permute(2		,				3		,				0		,				1						)
						elif "kernel" in key:
									__snake_case			=		torch.from_numpy(np.transpose(_UpperCAmelCase						)						)
						else:
									__snake_case			=		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 __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   Any		,				_UpperCAmelCase		:   str		,				_UpperCAmelCase		:   Optional[Any]						)      ->      List[str]:
			__snake_case			=		model_classes[model_name](
			    include_top=_UpperCAmelCase		,				weights="imagenet"		,				input_tensor=_UpperCAmelCase		,				input_shape=_UpperCAmelCase		,				pooling=_UpperCAmelCase		,				classes=10_00		,				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(_UpperCAmelCase						)
			__snake_case			=		EfficientNetForImageClassification(_UpperCAmelCase						).eval()
			__snake_case			=		hf_model.state_dict()

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

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

			# HF model inference
			hf_model.eval()
			with torch.no_grad():
						__snake_case			=		hf_model(**_UpperCAmelCase						)
			__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(_UpperCAmelCase						)
			__snake_case			=		np.expand_dims(_UpperCAmelCase		,				axis=0						)
			__snake_case			=		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...'''						)
						__snake_case			=		F'''efficientnet-{model_name}'''
						preprocessor.push_to_hub(_UpperCAmelCase						)
						hf_model.push_to_hub(_UpperCAmelCase						)


if __name__ == "__main__":
						a       :						Tuple								=		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       :						Optional[int]								=		parser.parse_args()
						convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase:	List[Any]       			=		logging.get_logger(__name__)

_lowercase:	int       			=		{
    '''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="mgp-str"





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]=[32, 1_28]  ,  lowercase__	:	str=4  ,  lowercase__	:	List[str]=3  ,  lowercase__	:	Tuple=27  ,  lowercase__	:	Optional[Any]=38  ,  lowercase__	:	Optional[Any]=5_02_57  ,  lowercase__	:	Dict=3_05_22  ,  lowercase__	:	Dict=7_68  ,  lowercase__	:	Tuple=12  ,  lowercase__	:	int=12  ,  lowercase__	:	int=4.0  ,  lowercase__	:	Tuple=True  ,  lowercase__	:	List[Any]=False  ,  lowercase__	:	int=1e-5  ,  lowercase__	:	int=0.0  ,  lowercase__	:	Optional[Any]=0.0  ,  lowercase__	:	List[Any]=0.0  ,  lowercase__	:	Union[str, Any]=False  ,  lowercase__	:	str=0.0_2  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(**lowercase__						)

										_lowerCAmelCase							=							image_size
										_lowerCAmelCase							=							patch_size
										_lowerCAmelCase							=							num_channels
										_lowerCAmelCase							=							max_token_length
										_lowerCAmelCase							=							num_character_labels
										_lowerCAmelCase							=							num_bpe_labels
										_lowerCAmelCase							=							num_wordpiece_labels
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							mlp_ratio
										_lowerCAmelCase							=							distilled
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							drop_rate
										_lowerCAmelCase							=							qkv_bias
										_lowerCAmelCase							=							attn_drop_rate
										_lowerCAmelCase							=							drop_path_rate
										_lowerCAmelCase							=							output_aa_attentions
										_lowerCAmelCase							=							initializer_range






