






"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase            =	logging.get_logger(__name__)

_lowerCAmelCase            =	{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
_lowerCAmelCase            =	[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A )     ->       Any:
				for attribute in key.split(""".""" ):
								lowercase    :      Dict       = getattr(_A  ,				_A )

				if weight_type is not None:
								lowercase    :      Union[str, Any]       = getattr(_A  ,				_A ).shape
				else:
								lowercase    :      Union[str, Any]       = hf_pointer.shape

				assert hf_shape == value.shape, (
				    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    F""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								lowercase    :      str       = value
				elif weight_type == "weight_g":
								lowercase    :      Optional[int]       = value
				elif weight_type == "weight_v":
								lowercase    :      List[Any]       = value
				elif weight_type == "bias":
								lowercase    :      Union[str, Any]       = value
				else:
								lowercase    :      Optional[int]       = value

				logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )


def      UpperCamelCase       ( _A  ,				_A )     ->       str:
				lowercase    :      List[str]       = []
				lowercase    :      Union[str, Any]       = fairseq_model.state_dict()

				lowercase    :      Dict       = hf_model.feature_extractor

				# if encoder has different dim to decoder -> use proj_weight
				lowercase    :      Tuple       = None

				for name, value in fairseq_dict.items():
								lowercase    :      Dict       = False
								if "conv_layers" in name:
												load_conv_layer(
												    _A  ,				_A  ,				_A  ,				_A  ,				hf_model.config.feat_extract_norm == """group"""  ,				)
												lowercase    :      List[Any]       = True
								elif name.split(""".""" )[0] == "proj":
												lowercase    :      List[Any]       = fairseq_model.proj
												lowercase    :      int       = True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
																				lowercase    :      Any       = True
																				if "*" in mapped_key:
																								lowercase    :      Optional[int]       = name.split(_A )[0].split(""".""" )[-2]
																								lowercase    :      int       = mapped_key.replace("""*"""  ,				_A )
																				if "weight_g" in name:
																								lowercase    :      Union[str, Any]       = """weight_g"""
																				elif "weight_v" in name:
																								lowercase    :      Dict       = """weight_v"""
																				elif "bias" in name:
																								lowercase    :      Optional[Any]       = """bias"""
																				elif "weight" in name:
																								lowercase    :      Any       = """weight"""
																				else:
																								lowercase    :      List[str]       = None
																				set_recursively(_A  ,				_A  ,				_A  ,				_A  ,				_A )
																continue
								if not is_used:
												unused_weights.append(_A )

				logger.warning(F"""Unused weights: {unused_weights}""" )

				return proj_weight


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A )     ->       str:
				lowercase    :      Any       = full_name.split("""conv_layers.""" )[-1]
				lowercase    :      List[str]       = name.split(""".""" )
				lowercase    :      List[Any]       = int(items[0] )
				lowercase    :      Optional[int]       = int(items[1] )

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												lowercase    :      Tuple       = value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												lowercase    :      str       = value
												logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												lowercase    :      Optional[int]       = value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    F"""{full_name} has size {value.shape}, but"""
												    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												lowercase    :      Tuple       = value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
				else:
								unused_weights.append(_A )


def      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase      ,      lowercase    :      List[str]       = emb.weight.shape
				lowercase    :      List[str]       = nn.Linear(_A  ,				_A  ,				bias=_A )
				lowercase    :      List[Any]       = emb.weight.data
				return lin_layer


def      UpperCamelCase       ( _A )     ->       str:
				with open(_A  ,				"""r"""  ,				encoding="""utf-8""" ) as f:
								lowercase    :      Optional[int]       = f.readlines()
								lowercase    :      Any       = [line.split(""" """ )[0] for line in lines]

				lowercase    :      Union[str, Any]       = len(_A )

				lowercase    :      Optional[Any]       = {
				    """<s>""": 0,
				    """<pad>""": 1,
				    """</s>""": 2,
				    """<unk>""": 3,
				}

				vocab_dict.update(dict(zip(_A  ,				range(4  ,				num_words + 4 ) ) ) )
				return vocab_dict


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A  ,				)     ->       List[str]:
				lowercase    :      List[Any]       = WavaVecaConfig.from_pretrained(_A )
				lowercase    :      Tuple       = SpeechaTextaConfig.from_pretrained(
				    _A  ,				vocab_size=_A  ,				decoder_layers=_A  ,				do_stable_layer_norm=_A )

				lowercase    :      Dict       = WavaVecaFeatureExtractor(
				    feature_size=1  ,				sampling_rate=16_000  ,				padding_value=0  ,				do_normalize=_A  ,				return_attention_mask=_A  ,				)

				lowercase      ,      lowercase      ,      lowercase    :      Optional[int]       = fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]  ,				arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
				lowercase    :      Optional[Any]       = model[0].eval()

				# set weights for wav2vec2 encoder
				lowercase    :      Union[str, Any]       = WavaVecaModel(_A )
				lowercase    :      Dict       = recursively_load_weights_wavaveca(model.encoder  ,				_A )

				lowercase    :      Tuple       = SpeechaTextaForCausalLM(_A )
				lowercase      ,      lowercase    :      Dict       = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()  ,				strict=_A )

				# set output linear layer
				unexpected_keys.remove("""embed_out""" )
				lowercase    :      Union[str, Any]       = nn.Parameter(model.decoder.embed_out.detach() )

				# layer norm is init to identity matrix so leaving it is fine
				logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
				logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )

				lowercase    :      Optional[int]       = SpeechEncoderDecoderModel(encoder=_A  ,				decoder=_A )
				lowercase    :      List[str]       = False

				# add projection layer
				lowercase    :      int       = nn.Parameter(projection_layer.weight )
				lowercase    :      Union[str, Any]       = nn.Parameter(projection_layer.bias )

				lowercase    :      Union[str, Any]       = create_vocab_dict(_A )

				with open(os.path.join(_A  ,				"""vocab.json""" )  ,				"""w""" ) as fp:
								json.dump(_A  ,				_A )

				lowercase    :      Optional[int]       = SpeechaTextaTokenizer(os.path.join(_A  ,				"""vocab.json""" ) )
				tokenizer.save_pretrained(_A )

				lowercase    :      Union[str, Any]       = hf_wavavec.config.to_dict()
				lowercase    :      Optional[int]       = tokenizer.pad_token_id
				lowercase    :      Tuple       = tokenizer.bos_token_id
				lowercase    :      Dict       = tokenizer.eos_token_id
				lowercase    :      Optional[Any]       = """speech_to_text_2"""
				lowercase    :      Optional[int]       = """wav2vec2"""

				lowercase    :      Tuple       = SpeechEncoderDecoderConfig.from_dict(_A )

				hf_wavavec.save_pretrained(_A )
				feature_extractor.save_pretrained(_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
				parser.add_argument(
				    '--encoder_config_path',
				    default='facebook/wav2vec2-large-lv60',
				    type=str,
				    help='Path to hf encoder wav2vec2 checkpoint config',
				)
				parser.add_argument(
				    '--decoder_config_path',
				    default='facebook/s2t-small-mustc-en-fr-st',
				    type=str,
				    help='Path to hf decoder s2t checkpoint config',
				)
				parser.add_argument('--vocab_size', default=1_02_24, type=int, help='Vocab size of decoder')
				parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

				_lowerCAmelCase            =	parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    vocab_size=args.vocab_size,
				    num_decoder_layers=args.num_decoder_layers,
				)








"""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()
_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': 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      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      List[str]       = EfficientNetConfig()
				lowercase    :      Any       = CONFIG_MAP[model_name]["""hidden_dim"""]
				lowercase    :      List[str]       = CONFIG_MAP[model_name]["""width_coef"""]
				lowercase    :      str       = CONFIG_MAP[model_name]["""depth_coef"""]
				lowercase    :      int       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      List[Any]       = CONFIG_MAP[model_name]["""dropout_rate"""]
				lowercase    :      int       = CONFIG_MAP[model_name]["""dw_padding"""]

				lowercase    :      Optional[int]       = """huggingface/label-files"""
				lowercase    :      int       = """imagenet-1k-id2label.json"""
				lowercase    :      Any       = 1_000
				lowercase    :      Any       = json.load(open(hf_hub_download(_A  ,				_A  ,				repo_type="""dataset""" )  ,				"""r""" ) )
				lowercase    :      Optional[int]       = {int(_A ): v for k, v in idalabel.items()}

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


def      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      Any       = """http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase    :      Optional[int]       = Image.open(requests.get(_A  ,				stream=_A ).raw )
				return im


def      UpperCamelCase       ( _A )     ->       Optional[Any]:
				lowercase    :      str       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      Optional[int]       = 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=_A  ,				)
				return preprocessor


def      UpperCamelCase       ( _A )     ->       Optional[int]:
				lowercase    :      Union[str, Any]       = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
				lowercase    :      Optional[Any]       = sorted(set(_A ) )
				lowercase    :      Dict       = len(_A )
				lowercase    :      List[str]       = {b: str(_A ) for b, i in zip(_A  ,				range(_A ) )}

				lowercase    :      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:
								lowercase    :      str       = 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""") )

				lowercase    :      Union[str, Any]       = {}
				for item in rename_keys:
								if item[0] in original_param_names:
												lowercase    :      Optional[int]       = """efficientnet.""" + item[1]

				lowercase    :      Any       = """classifier.weight"""
				lowercase    :      Tuple       = """classifier.bias"""
				return key_mapping


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[Any]:
				for key, value in tf_params.items():
								if "normalization" in key:
												continue

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

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


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A )     ->       str:
				lowercase    :      Any       = model_classes[model_name](
				    include_top=_A  ,				weights="""imagenet"""  ,				input_tensor=_A  ,				input_shape=_A  ,				pooling=_A  ,				classes=1_000  ,				classifier_activation="""softmax"""  ,				)

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

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

				# Create src-to-dst parameter name mapping dictionary
				print("""Converting parameters...""" )
				lowercase    :      int       = rename_keys(_A )
				replace_params(_A  ,				_A  ,				_A )

				# Initialize preprocessor and preprocess input image
				lowercase    :      Optional[int]       = convert_image_processor(_A )
				lowercase    :      Any       = preprocessor(images=prepare_img()  ,				return_tensors="""pt""" )

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								lowercase    :      Union[str, Any]       = hf_model(**_A )
				lowercase    :      List[Any]       = outputs.logits.detach().numpy()

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

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

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


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)
