
"""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								=						logging.get_logger(__name__)

_a								=						{
    """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								=						{
    """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__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase									=    EfficientNetConfig()
					_UpperCamelCase									=    CONFIG_MAP[model_name]['''hidden_dim''']
					_UpperCamelCase									=    CONFIG_MAP[model_name]['''width_coef''']
					_UpperCamelCase									=    CONFIG_MAP[model_name]['''depth_coef''']
					_UpperCamelCase									=    CONFIG_MAP[model_name]['''image_size''']
					_UpperCamelCase									=    CONFIG_MAP[model_name]['''dropout_rate''']
					_UpperCamelCase									=    CONFIG_MAP[model_name]['''dw_padding''']

					_UpperCamelCase									=    '''huggingface/label-files'''
					_UpperCamelCase									=    '''imagenet-1k-id2label.json'''
					_UpperCamelCase									=    10_00
					_UpperCamelCase									=    json.load(open(hf_hub_download(__snake_case,	__snake_case,	repo_type='''dataset'''      ),	'''r'''      )      )
					_UpperCamelCase									=    {int(__snake_case      ): v for k, v in idalabel.items()}

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



def  lowerCamelCase__						(     )				->						str:


					"""simple docstring"""





					_UpperCamelCase									=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase									=    Image.open(requests.get(__snake_case,	stream=__snake_case      ).raw      )
					return im



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    CONFIG_MAP[model_name]['''image_size''']
					_UpperCamelCase									=    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=__snake_case,	)
					return preprocessor



def  lowerCamelCase__						(     __snake_case      )				->						Union[str, Any]:


					"""simple docstring"""





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

					_UpperCamelCase									=    []
					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:
										_UpperCamelCase									=    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''')      )

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

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						str:


					"""simple docstring"""





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

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

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



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    model_classes[model_name](
					    include_top=__snake_case,	weights='''imagenet''',	input_tensor=__snake_case,	input_shape=__snake_case,	pooling=__snake_case,	classes=10_00,	classifier_activation='''softmax''',	)

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

					# Load HuggingFace model
					_UpperCamelCase									=    get_efficientnet_config(__snake_case      )
					_UpperCamelCase									=    EfficientNetForImageClassification(__snake_case      ).eval()
					_UpperCamelCase									=    hf_model.state_dict()

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

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

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

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

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

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


if __name__ == "__main__":
				_a								=						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								=						parser.parse_args()
				convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a								=						logging.get_logger(__name__)






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['pixel_values']



		def __init__(   self							,						__a = True							,						__a = None							,						__a = PILImageResampling.BICUBIC							,						__a = True							,						__a = True							,						__a = 1 / 2_55							,						__a = None							,						__a = True							,						__a = None							,						__a = None							,						**__a							,						)			->			None:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase									=    size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a)
							_UpperCamelCase									=    crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a							,						param_name='''crop_size''')

							_UpperCamelCase									=    do_resize
							_UpperCamelCase									=    do_rescale
							_UpperCamelCase									=    do_normalize
							_UpperCamelCase									=    do_center_crop
							_UpperCamelCase									=    crop_size
							_UpperCamelCase									=    size
							_UpperCamelCase									=    resample
							_UpperCamelCase									=    rescale_factor
							_UpperCamelCase									=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_UpperCamelCase									=    image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = PILImageResampling.BILINEAR							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)

							if "shortest_edge" in size:
												_UpperCamelCase									=    get_resize_output_image_size(__a							,						size=size['''shortest_edge''']							,						default_to_square=__a)
												# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
												_UpperCamelCase									=    (size['''height'''], size['''width'''])
							else:
												raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
							return resize(__a							,						size=__a							,						resample=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
							return center_crop(__a							,						size=(size['''height'''], size['''width'''])							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a)			->			np.ndarray:






							'''simple docstring'''
							return rescale(__a							,						scale=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							return normalize(__a							,						mean=__a							,						std=__a							,						data_format=__a							,						**__a)




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = ChannelDimension.FIRST							,						**__a							,						)			->			BatchFeature:






							'''simple docstring'''
							_UpperCamelCase									=    do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase									=    do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase									=    do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase									=    do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase									=    crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase									=    get_size_dict(__a							,						param_name='''crop_size'''							,						default_to_square=__a)
							_UpperCamelCase									=    resample if resample is not None else self.resample
							_UpperCamelCase									=    rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase									=    image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase									=    image_std if image_std is not None else self.image_std

							_UpperCamelCase									=    size if size is not None else self.size
							_UpperCamelCase									=    get_size_dict(__a)

							if not is_batched(__a):
												_UpperCamelCase									=    [images]

							if not valid_images(__a):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.''')

							if do_resize and size is None:
												raise ValueError('''Size must be specified if do_resize is True.''')

							if do_center_crop and crop_size is None:
												raise ValueError('''Crop size must be specified if do_center_crop is True.''')

							if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

							# All transformations expect numpy arrays.
							_UpperCamelCase									=    [to_numpy_array(__a) for image in images]

							if do_resize:
												_UpperCamelCase									=    [self.resize(image=__a							,						size=__a							,						resample=__a) for image in images]

							if do_center_crop:
												_UpperCamelCase									=    [self.center_crop(image=__a							,						size=__a) for image in images]

							if do_rescale:
												_UpperCamelCase									=    [self.rescale(image=__a							,						scale=__a) for image in images]

							if do_normalize:
												_UpperCamelCase									=    [self.normalize(image=__a							,						mean=__a							,						std=__a) for image in images]

							_UpperCamelCase									=    [to_channel_dimension_format(__a							,						__a) for image in images]

							_UpperCamelCase									=    {'''pixel_values''': images}
							return BatchFeature(data=__a							,						tensor_type=__a)






