





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 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  EfficientNetConfig()
				snake_case_						:   List[str]           =  CONFIG_MAP[model_name]['''hidden_dim''']
				snake_case_						:   List[str]           =  CONFIG_MAP[model_name]['''width_coef''']
				snake_case_						:   Dict           =  CONFIG_MAP[model_name]['''depth_coef''']
				snake_case_						:   int           =  CONFIG_MAP[model_name]['''image_size''']
				snake_case_						:   Any           =  CONFIG_MAP[model_name]['''dropout_rate''']
				snake_case_						:   List[Any]           =  CONFIG_MAP[model_name]['''dw_padding''']

				snake_case_						:   Any           =  '''huggingface/label-files'''
				snake_case_						:   Tuple           =  '''imagenet-1k-id2label.json'''
				snake_case_						:   List[Any]           =  1_000
				snake_case_						:   Dict           =  json.load(open(hf_hub_download(_UpperCamelCase			,    _UpperCamelCase			,    repo_type='''dataset'''			)			,    '''r'''			)			)
				snake_case_						:   Optional[int]           =  {int(_UpperCamelCase			): v for k, v in idalabel.items()}

				snake_case_						:   Optional[int]           =  idalabel
				snake_case_						:   List[Any]           =  {v: k for k, v in idalabel.items()}
				return config






def 					lowerCamelCase_						(    )		->						int:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:   Tuple           =  Image.open(requests.get(_UpperCamelCase			,    stream=_UpperCamelCase			).raw			)
				return im






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  CONFIG_MAP[model_name]['''image_size''']
				snake_case_						:   Any           =  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=_UpperCamelCase			,    )
				return preprocessor






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


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

				snake_case_						:   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:
								snake_case_						:   Optional[Any]           =  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''')			)

				snake_case_						:   Optional[int]           =  {}
				for item in rename_keys:
								if item[0] in original_param_names:
												snake_case_						:   List[str]           =  '''efficientnet.''' + item[1]

				snake_case_						:   Tuple           =  '''classifier.weight'''
				snake_case_						:   List[Any]           =  '''classifier.bias'''
				return key_mapping






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


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

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

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






@torch.no_grad()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   int           =  model_classes[model_name](
				    include_top=_UpperCamelCase			,    weights='''imagenet'''			,    input_tensor=_UpperCamelCase			,    input_shape=_UpperCamelCase			,    pooling=_UpperCamelCase			,    classes=1_000			,    classifier_activation='''softmax'''			,    )

				snake_case_						:   Dict           =  original_model.trainable_variables
				snake_case_						:   Union[str, Any]           =  original_model.non_trainable_variables
				snake_case_						:   Optional[int]           =  {param.name: param.numpy() for param in tf_params}
				for param in tf_non_train_params:
								snake_case_						:   Dict           =  param.numpy()
				snake_case_						:   Any           =  list(tf_params.keys()			)

				# Load HuggingFace model
				snake_case_						:   int           =  get_efficientnet_config(_UpperCamelCase			)
				snake_case_						:   List[str]           =  EfficientNetForImageClassification(_UpperCamelCase			).eval()
				snake_case_						:   List[Any]           =  hf_model.state_dict()

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

				# Initialize preprocessor and preprocess input image
				snake_case_						:   List[Any]           =  convert_image_processor(_UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  preprocessor(images=prepare_img()			,    return_tensors='''pt'''			)

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								snake_case_						:   Optional[int]           =  hf_model(**_UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  outputs.logits.detach().numpy()

				# Original model inference
				snake_case_						:   Any           =  False
				snake_case_						:   Optional[int]           =  CONFIG_MAP[model_name]['''image_size''']
				snake_case_						:   Tuple           =  prepare_img().resize((image_size, image_size)			,    resample=PIL.Image.NEAREST			)
				snake_case_						:   Dict           =  image.img_to_array(_UpperCamelCase			)
				snake_case_						:   str           =  np.expand_dims(_UpperCamelCase			,    axis=0			)
				snake_case_						:   List[Any]           =  original_model.predict(_UpperCamelCase			)

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

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


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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_    =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    ['''XGLMTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    ['''XGLMTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''XGLMForCausalLM''',
	    '''XGLMModel''',
	    '''XGLMPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''FlaxXGLMForCausalLM''',
	    '''FlaxXGLMModel''',
	    '''FlaxXGLMPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFXGLMForCausalLM''',
	    '''TFXGLMModel''',
	    '''TFXGLMPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm import XGLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm_fast import XGLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xglm import (
		    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXGLMForCausalLM,
		    TFXGLMModel,
		    TFXGLMPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase_    =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
