



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()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

__SCREAMING_SNAKE_CASE           =  {
    """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 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Dict              =						EfficientNetConfig()
			A							:					List[str]              =						CONFIG_MAP[model_name]["hidden_dim"]
			A							:					str              =						CONFIG_MAP[model_name]["width_coef"]
			A							:					Tuple              =						CONFIG_MAP[model_name]["depth_coef"]
			A							:					Tuple              =						CONFIG_MAP[model_name]["image_size"]
			A							:					List[Any]              =						CONFIG_MAP[model_name]["dropout_rate"]
			A							:					str              =						CONFIG_MAP[model_name]["dw_padding"]

			A							:					Optional[int]              =						"huggingface/label-files"
			A							:					Optional[int]              =						"imagenet-1k-id2label.json"
			A							:					int              =						1000
			A							:					List[str]              =						json.load(open(hf_hub_download(_lowerCamelCase ,    _lowerCamelCase ,    repo_type="dataset"    ) ,    "r"    )    )
			A							:					Any              =						{int(_lowerCamelCase    ): v for k, v in idalabel.items()}

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



def 	UpperCAmelCase   (						):
			A							:					List[str]              =						"http://images.cocodataset.org/val2017/000000039769.jpg"
			A							:					Union[str, Any]              =						Image.open(requests.get(_lowerCamelCase ,    stream=_lowerCamelCase    ).raw    )
			return im



def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					List[str]              =						CONFIG_MAP[model_name]["image_size"]
			A							:					str              =						EfficientNetImageProcessor(
			    size={"height": size, "width": size} ,    image_mean=[0.4_85, 0.4_56, 0.4_06] ,    image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] ,    do_center_crop=_lowerCamelCase ,    )
			return preprocessor



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

			A							:					Union[str, Any]              =						[]
			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:
						A							:					Optional[int]              =						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")    )

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

			A							:					Optional[int]              =						"classifier.weight"
			A							:					List[Any]              =						"classifier.bias"
			return key_mapping



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

						A							:					Dict              =						key_mapping[key]
						if "_conv" in key and "kernel" in key:
									A							:					List[Any]              =						torch.from_numpy(_lowerCamelCase    ).permute(3 ,    2 ,    0 ,    1    )
						elif "depthwise_kernel" in key:
									A							:					int              =						torch.from_numpy(_lowerCamelCase    ).permute(2 ,    3 ,    0 ,    1    )
						elif "kernel" in key:
									A							:					Dict              =						torch.from_numpy(np.transpose(_lowerCamelCase    )    )
						else:
									A							:					str              =						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 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Tuple              =						model_classes[model_name](
			    include_top=_lowerCamelCase ,    weights="imagenet" ,    input_tensor=_lowerCamelCase ,    input_shape=_lowerCamelCase ,    pooling=_lowerCamelCase ,    classes=1000 ,    classifier_activation="softmax" ,    )

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

			# Load HuggingFace model
			A							:					List[str]              =						get_efficientnet_config(_lowerCamelCase    )
			A							:					List[str]              =						EfficientNetForImageClassification(_lowerCamelCase    ).eval()
			A							:					Union[str, Any]              =						hf_model.state_dict()

			# Create src-to-dst parameter name mapping dictionary
			print("Converting parameters..."    )
			A							:					List[Any]              =						rename_keys(_lowerCamelCase    )
			replace_params(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )

			# Initialize preprocessor and preprocess input image
			A							:					Tuple              =						convert_image_processor(_lowerCamelCase    )
			A							:					List[str]              =						preprocessor(images=prepare_img() ,    return_tensors="pt"    )

			# HF model inference
			hf_model.eval()
			with torch.no_grad():
						A							:					Dict              =						hf_model(**_lowerCamelCase    )
			A							:					List[Any]              =						outputs.logits.detach().numpy()

			# Original model inference
			A							:					int              =						False
			A							:					Optional[int]              =						CONFIG_MAP[model_name]["image_size"]
			A							:					Any              =						prepare_img().resize((image_size, image_size) ,    resample=PIL.Image.NEAREST    )
			A							:					str              =						image.img_to_array(_lowerCamelCase    )
			A							:					Dict              =						np.expand_dims(_lowerCamelCase ,    axis=0    )
			A							:					List[Any]              =						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..."""    )
						A							:					Optional[int]              =						f"""efficientnet-{model_name}"""
						preprocessor.push_to_hub(_lowerCamelCase    )
						hf_model.push_to_hub(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  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""")

							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




from collections import deque

from .hash_table import HashTable

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			Dict	,	**__lowerCamelCase      :			int )      ->				Optional[int]:
									super().__init__(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						deque([] ) if self.values[key] is None else self.values[key]
									self.values[key].appendleft(__lowerCamelCase )
									A							:					Dict              =						self.values[key]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									return (
									    sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
									    / self.size_table
									    * self.charge_factor
									)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			List[Any]=None )      ->				Optional[int]:
									if not (
									    len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
									):
												return key
									return super()._collision_resolution(__lowerCamelCase	,	__lowerCamelCase )