


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	:		Tuple    =       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	:		List[Any]    =       {
    'b0': {
        'hidden_dim': 1_2_8_0,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_2_4,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1_2_8_0,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_4_0,
        'dropout_rate': 0.2,
        'dw_padding': [1_6],
    },
    'b2': {
        'hidden_dim': 1_4_0_8,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_6_0,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 1_6],
    },
    'b3': {
        'hidden_dim': 1_5_3_6,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_0_0,
        'dropout_rate': 0.3,
        'dw_padding': [5, 1_8],
    },
    'b4': {
        'hidden_dim': 1_7_9_2,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_8_0,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2_0_4_8,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_5_6,
        'dropout_rate': 0.4,
        'dw_padding': [1_3, 2_7],
    },
    'b6': {
        'hidden_dim': 2_3_0_4,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_2_8,
        'dropout_rate': 0.5,
        'dw_padding': [3_1],
    },
    'b7': {
        'hidden_dim': 2_5_6_0,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_0_0,
        'dropout_rate': 0.5,
        'dw_padding': [1_8],
    },
}






def 			UpperCamelCase (     __magic_name__					:							Optional[int]					)       ->  Tuple:
		"""simple docstring"""




		lowercase__					      =		EfficientNetConfig()
		lowercase__					      =		CONFIG_MAP[model_name]["""hidden_dim"""]
		lowercase__					      =		CONFIG_MAP[model_name]["""width_coef"""]
		lowercase__					      =		CONFIG_MAP[model_name]["""depth_coef"""]
		lowercase__					      =		CONFIG_MAP[model_name]["""image_size"""]
		lowercase__					      =		CONFIG_MAP[model_name]["""dropout_rate"""]
		lowercase__					      =		CONFIG_MAP[model_name]["""dw_padding"""]

		lowercase__					      =		"""huggingface/label-files"""
		lowercase__					      =		"""imagenet-1k-id2label.json"""
		lowercase__					      =		1000
		lowercase__					      =		json.load(open(hf_hub_download(__magic_name__   ,   __magic_name__   ,   repo_type="""dataset"""					)   ,   """r"""					)					)
		lowercase__					      =		{int(__magic_name__					): v for k, v in idalabel.items()}

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






def 			UpperCamelCase (     )       ->  str:
		"""simple docstring"""




		lowercase__					      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowercase__					      =		Image.open(requests.get(__magic_name__   ,   stream=__magic_name__					).raw					)
		return im






def 			UpperCamelCase (     __magic_name__					:							Union[str, Any]					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		CONFIG_MAP[model_name]["""image_size"""]
		lowercase__					      =		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=__magic_name__   ,   )
		return preprocessor






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]					)       ->  Dict:
		"""simple docstring"""




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

		lowercase__					      =		[]
		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:
				lowercase__					      =		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""")					)

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

		lowercase__					      =		"""classifier.weight"""
		lowercase__					      =		"""classifier.bias"""
		return key_mapping






def 			UpperCamelCase (     __magic_name__					:							List[Any]   ,   __magic_name__					:							List[Any]   ,   __magic_name__					:							Optional[Any]					)       ->  str:
		"""simple docstring"""




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

				lowercase__					      =		key_mapping[key]
				if "_conv" in key and "kernel" in key:
						lowercase__					      =		torch.from_numpy(__magic_name__					).permute(3   ,   2   ,   0   ,   1					)
				elif "depthwise_kernel" in key:
						lowercase__					      =		torch.from_numpy(__magic_name__					).permute(2   ,   3   ,   0   ,   1					)
				elif "kernel" in key:
						lowercase__					      =		torch.from_numpy(np.transpose(__magic_name__					)					)
				else:
						lowercase__					      =		torch.from_numpy(__magic_name__					)

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






@torch.no_grad()
def 			UpperCamelCase (     __magic_name__					:							List[str]   ,   __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							Any   ,   __magic_name__					:							str					)       ->  Dict:
		"""simple docstring"""




		lowercase__					      =		model_classes[model_name](
		    include_top=__magic_name__   ,   weights="""imagenet"""   ,   input_tensor=__magic_name__   ,   input_shape=__magic_name__   ,   pooling=__magic_name__   ,   classes=1000   ,   classifier_activation="""softmax"""   ,   )

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

		# Load HuggingFace model
		lowercase__					      =		get_efficientnet_config(__magic_name__					)
		lowercase__					      =		EfficientNetForImageClassification(__magic_name__					).eval()
		lowercase__					      =		hf_model.state_dict()

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

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

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

		# Original model inference
		lowercase__					      =		False
		lowercase__					      =		CONFIG_MAP[model_name]["""image_size"""]
		lowercase__					      =		prepare_img().resize((image_size, image_size)   ,   resample=PIL.Image.NEAREST					)
		lowercase__					      =		image.img_to_array(__magic_name__					)
		lowercase__					      =		np.expand_dims(__magic_name__   ,   axis=0					)
		lowercase__					      =		original_model.predict(__magic_name__					)

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

		if push_to_hub:
				# Push model and image processor to hub
				print(f'''Pushing converted {model_name} to the hub...'''					)
				lowercase__					      =		f'''efficientnet-{model_name}'''
				preprocessor.push_to_hub(__magic_name__					)
				hf_model.push_to_hub(__magic_name__					)


if __name__ == "__main__":
	A	:		List[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')

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A	:		Dict    =       {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Tuple    =       ['GLPNFeatureExtractor']
	A	:		List[str]    =       ['GLPNImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:		Optional[int]    =       [
	    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GLPNForDepthEstimation',
	    'GLPNLayer',
	    'GLPNModel',
	    'GLPNPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_glpn import GLPNFeatureExtractor
		from .image_processing_glpn import GLPNImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_glpn import (
		    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GLPNForDepthEstimation,
		    GLPNLayer,
		    GLPNModel,
		    GLPNPreTrainedModel,
		)


else:
	import sys

	A	:		Tuple    =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

