
"""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()
a					:	str							=		logging.get_logger(__name__)

a					:	Union[str, Any]							=		{
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

a					:	Dict							=		{
    '''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  _UpperCamelCase	(						_A      )    ->  Union[str, Any]:





    """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          =   1_0_0_0
    _UpperCAmelCase          =   json.load(open(hf_hub_download(_A					,      _A					,      repo_type="""dataset"""      )					,      """r"""      )      )
    _UpperCAmelCase          =   {int(_A      ): v for k, v in idalabel.items()}

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


def  _UpperCamelCase	(						)    ->  Optional[Any]:





    """simple docstring"""

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


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





    """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=_A					,      )
    return preprocessor


def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase          =   [v.split("""_"""      )[0].split("""block"""      )[1] for v in original_param_names if v.startswith("""block"""      )]
    _UpperCAmelCase          =   sorted(set(_A      )      )
    _UpperCAmelCase          =   len(_A      )
    _UpperCAmelCase          =   {b: str(_A      ) for b, i in zip(_A					,      range(_A      )      )}

    _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  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[str]:





    """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(_A      ).permute(3					,      2					,      0					,      1      )
        elif "depthwise_kernel" in key:
            _UpperCAmelCase          =   torch.from_numpy(_A      ).permute(2					,      3					,      0					,      1      )
        elif "kernel" in key:
            _UpperCAmelCase          =   torch.from_numpy(np.transpose(_A      )      )
        else:
            _UpperCAmelCase          =   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      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase          =   model_classes[model_name](
        include_top=_A					,      weights="""imagenet"""					,      input_tensor=_A					,      input_shape=_A					,      pooling=_A					,      classes=1_0_0_0					,      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(_A      )
    _UpperCAmelCase          =   EfficientNetForImageClassification(_A      ).eval()
    _UpperCAmelCase          =   hf_model.state_dict()

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

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

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        _UpperCAmelCase          =   hf_model(**_A      )
    _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(_A      )
    _UpperCAmelCase          =   np.expand_dims(_A					,      axis=0      )
    _UpperCAmelCase          =   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..."""      )
        _UpperCAmelCase          =   F"""efficientnet-{model_name}"""
        preprocessor.push_to_hub(_A      )
        hf_model.push_to_hub(_A      )


if __name__ == "__main__":
 a					:	Union[str, Any]							=		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''')

 a					:	Any							=		parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)

"""simple docstring"""



import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
    torch.save(model.state_dict()					,      _A      )


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)