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					:   str							=		logging.get_logger(__name__)

_lowerCamelCase					:   Optional[int]							=		{
    '''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					:   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 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]  ):
					SCREAMING_SNAKE_CASE    			=       EfficientNetConfig()
					SCREAMING_SNAKE_CASE    			=       CONFIG_MAP[model_name]["hidden_dim"]
					SCREAMING_SNAKE_CASE    			=       CONFIG_MAP[model_name]["width_coef"]
					SCREAMING_SNAKE_CASE    			=       CONFIG_MAP[model_name]["depth_coef"]
					SCREAMING_SNAKE_CASE    			=       CONFIG_MAP[model_name]["image_size"]
					SCREAMING_SNAKE_CASE    			=       CONFIG_MAP[model_name]["dropout_rate"]
					SCREAMING_SNAKE_CASE    			=       CONFIG_MAP[model_name]["dw_padding"]

					SCREAMING_SNAKE_CASE    			=       "huggingface/label-files"
					SCREAMING_SNAKE_CASE    			=       "imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE    			=       1_0_0_0
					SCREAMING_SNAKE_CASE    			=       json.load(open(hf_hub_download(_lowerCamelCase      , _lowerCamelCase      , repo_type="dataset"  )      , "r"  )  )
					SCREAMING_SNAKE_CASE    			=       {int(_lowerCamelCase  ): v for k, v in idalabel.items()}

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





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       "http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE    			=       Image.open(requests.get(_lowerCamelCase      , stream=_lowerCamelCase  ).raw  )
					return im





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple  ):
					SCREAMING_SNAKE_CASE    			=       CONFIG_MAP[model_name]["image_size"]
					SCREAMING_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=_lowerCamelCase      , )
					return preprocessor





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[str]  ):
					SCREAMING_SNAKE_CASE    			=       [v.split("_"  )[0].split("block"  )[1] for v in original_param_names if v.startswith("block"  )]
					SCREAMING_SNAKE_CASE    			=       sorted(set(_lowerCamelCase  )  )
					SCREAMING_SNAKE_CASE    			=       len(_lowerCamelCase  )
					SCREAMING_SNAKE_CASE    			=       {b: str(_lowerCamelCase  ) for b, i in zip(_lowerCamelCase      , range(_lowerCamelCase  )  )}

					SCREAMING_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:
										SCREAMING_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")  )

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

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





def 	__lowerCamelCase							(UpperCAmelCase__     :							int      , UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Tuple  ):
					for key, value in tf_params.items():
										if "normalization" in key:
															continue

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

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





@torch.no_grad()
def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Any  ):
					SCREAMING_SNAKE_CASE    			=       model_classes[model_name](
					    include_top=_lowerCamelCase      , weights="imagenet"      , input_tensor=_lowerCamelCase      , input_shape=_lowerCamelCase      , pooling=_lowerCamelCase      , classes=1_0_0_0      , classifier_activation="softmax"      , )

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

					# Load HuggingFace model
					SCREAMING_SNAKE_CASE    			=       get_efficientnet_config(_lowerCamelCase  )
					SCREAMING_SNAKE_CASE    			=       EfficientNetForImageClassification(_lowerCamelCase  ).eval()
					SCREAMING_SNAKE_CASE    			=       hf_model.state_dict()

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

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

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

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

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

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


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





import functools





def 	__lowerCamelCase							(UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							list[int]  ):

					# Validation
					if not isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for day in days  ):
										raise ValueError("The parameter days should be a list of integers"  )

					if len(UpperCAmelCase__  ) != 3 or not all(isinstance(UpperCAmelCase__      , UpperCAmelCase__  ) for cost in costs  ):
										raise ValueError("The parameter costs should be a list of three integers"  )

					if len(UpperCAmelCase__  ) == 0:
										return 0

					if min(UpperCAmelCase__  ) <= 0:
										raise ValueError("All days elements should be greater than 0"  )

					if max(UpperCAmelCase__  ) >= 3_6_6:
										raise ValueError("All days elements should be less than 366"  )

					SCREAMING_SNAKE_CASE    			=       set(UpperCAmelCase__  )

					@functools.cache
					def dynamic_programming(UpperCAmelCase__     :							int  ) -> int:
										if index > 3_6_5:
															return 0

										if index not in days_set:
															return dynamic_programming(index + 1  )

										return min(
										    costs[0] + dynamic_programming(index + 1  )      , costs[1] + dynamic_programming(index + 7  )      , costs[2] + dynamic_programming(index + 3_0  )      , )

					return dynamic_programming(1  )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




