

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			     =       logging.get_logger(__name__)

__lowerCAmelCase			     =       {
    '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			     =       {
    '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    _lowercase			(	a__						:			Optional[Any]					)							->					Tuple:





	"""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    _lowercase			(	)							->					Union[str, Any]:





	"""simple docstring"""


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



def    _lowercase			(	a__						:			Tuple					)							->					int:





	"""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=[0.47_853_944, 0.4_732_864, 0.47_434_163]							,  do_center_crop=snake_case__							,  )
	return preprocessor



def    _lowercase			(	a__						:			Optional[Any]					)							->					Optional[int]:





	"""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    _lowercase			(	a__						:			Optional[int]							,  a__						:			int							,  a__						:			List[str]					)							->					Any:





	"""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    _lowercase			(	a__						:			str							,  a__						:			Dict							,  a__						:			int							,  a__						:			str					)							->					Dict:





	"""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__":
	__lowerCAmelCase			     =       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			     =       parser.parse_args()
	convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__					:     Optional[int]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     List[str]       =     {
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''camembert'''


     def __init__(  self							,					A_=3_05_22							,					A_=7_68							,					A_=12							,					A_=12							,					A_=30_72							,					A_="gelu"							,					A_=0.1							,					A_=0.1							,					A_=5_12							,					A_=2							,					A_=0.0_2							,					A_=1e-12							,					A_=1							,					A_=0							,					A_=2							,					A_="absolute"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					bos_token_id=A_							,					eos_token_id=A_							,					**A_    )

       _UpperCAmelCase		:							Optional[Any]							=     vocab_size
       _UpperCAmelCase		:							int							=     hidden_size
       _UpperCAmelCase		:							Any							=     num_hidden_layers
       _UpperCAmelCase		:							Optional[Any]							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     hidden_act
       _UpperCAmelCase		:							str							=     intermediate_size
       _UpperCAmelCase		:							str							=     hidden_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							List[Any]							=     max_position_embeddings
       _UpperCAmelCase		:							Union[str, Any]							=     type_vocab_size
       _UpperCAmelCase		:							List[Any]							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							int							=     position_embedding_type
       _UpperCAmelCase		:							Tuple							=     use_cache
       _UpperCAmelCase		:							Any							=     classifier_dropout


class 		_SCREAMING_SNAKE_CASE     (						A     ):


     @property
     def        __snake_case(  self    ):
       if self.task == "multiple-choice":
         _UpperCAmelCase		:							Any							=     {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         _UpperCAmelCase		:							List[Any]							=     {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]    )





