





'''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()
lowercase_					= logging.get_logger(__name__)

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

lowercase_					= {
    """b0""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1_408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1_536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1_792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2_304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2_560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}





def lowerCamelCase						(     __lowerCamelCase :    Tuple    )						->int:
    _SCREAMING_SNAKE_CASE									=			EfficientNetConfig()
    _SCREAMING_SNAKE_CASE									=			CONFIG_MAP[model_name]["""hidden_dim"""]
    _SCREAMING_SNAKE_CASE									=			CONFIG_MAP[model_name]["""width_coef"""]
    _SCREAMING_SNAKE_CASE									=			CONFIG_MAP[model_name]["""depth_coef"""]
    _SCREAMING_SNAKE_CASE									=			CONFIG_MAP[model_name]["""image_size"""]
    _SCREAMING_SNAKE_CASE									=			CONFIG_MAP[model_name]["""dropout_rate"""]
    _SCREAMING_SNAKE_CASE									=			CONFIG_MAP[model_name]["""dw_padding"""]

    _SCREAMING_SNAKE_CASE									=			"""huggingface/label-files"""
    _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
    _SCREAMING_SNAKE_CASE									=			1000
    _SCREAMING_SNAKE_CASE									=			json.load(open(hf_hub_download(__lowerCamelCase					,       __lowerCamelCase					,       repo_type="""dataset"""    )					,       """r"""    )    )
    _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}

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





def lowerCamelCase						(     )						->str:
    _SCREAMING_SNAKE_CASE									=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    _SCREAMING_SNAKE_CASE									=			Image.open(requests.get(__lowerCamelCase					,       stream=__lowerCamelCase    ).raw    )
    return im





def lowerCamelCase						(     __lowerCamelCase :    Tuple    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			CONFIG_MAP[model_name]["""image_size"""]
    _SCREAMING_SNAKE_CASE									=			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=__lowerCamelCase					,       )
    return preprocessor





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]    )						->Optional[int]:
    _SCREAMING_SNAKE_CASE									=			[v.split("""_"""    )[0].split("""block"""    )[1] for v in original_param_names if v.startswith("""block"""    )]
    _SCREAMING_SNAKE_CASE									=			sorted(set(__lowerCamelCase    )    )
    _SCREAMING_SNAKE_CASE									=			len(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			{b: str(__lowerCamelCase    ) for b, i in zip(__lowerCamelCase					,       range(__lowerCamelCase    )    )}

    _SCREAMING_SNAKE_CASE									=			[]
    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:
        _SCREAMING_SNAKE_CASE									=			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""")    )

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

    _SCREAMING_SNAKE_CASE									=			"""classifier.weight"""
    _SCREAMING_SNAKE_CASE									=			"""classifier.bias"""
    return key_mapping





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    List[str]					,       __lowerCamelCase :    List[str]    )						->List[str]:
    for key, value in tf_params.items():
        if "normalization" in key:
            continue

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

    _SCREAMING_SNAKE_CASE									=			original_model.trainable_variables
    _SCREAMING_SNAKE_CASE									=			original_model.non_trainable_variables
    _SCREAMING_SNAKE_CASE									=			{param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        _SCREAMING_SNAKE_CASE									=			param.numpy()
    _SCREAMING_SNAKE_CASE									=			list(tf_params.keys()    )

    # Load HuggingFace model
    _SCREAMING_SNAKE_CASE									=			get_efficientnet_config(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			EfficientNetForImageClassification(__lowerCamelCase    ).eval()
    _SCREAMING_SNAKE_CASE									=			hf_model.state_dict()

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

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

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        _SCREAMING_SNAKE_CASE									=			hf_model(**__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			outputs.logits.detach().numpy()

    # Original model inference
    _SCREAMING_SNAKE_CASE									=			False
    _SCREAMING_SNAKE_CASE									=			CONFIG_MAP[model_name]["""image_size"""]
    _SCREAMING_SNAKE_CASE									=			prepare_img().resize((image_size, image_size)					,       resample=PIL.Image.NEAREST    )
    _SCREAMING_SNAKE_CASE									=			image.img_to_array(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			np.expand_dims(__lowerCamelCase					,       axis=0    )
    _SCREAMING_SNAKE_CASE									=			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...'    )
        _SCREAMING_SNAKE_CASE									=			F'efficientnet-{model_name}'
        preprocessor.push_to_hub(__lowerCamelCase    )
        hf_model.push_to_hub(__lowerCamelCase    )


if __name__ == "__main__":
      lowercase_					= 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""")

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










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			classifier_dropout




class 					a_     (				snake_case_       ):

    '''simple docstring'''


    @property
    def 			snake_case_(		self       )		->   Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE									=			{0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]       )



