

import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text




def 					_lowercase     (				a_				:					List[str]				)				->	str:




					'''simple docstring'''







					__magic_name__							=    []
					for line in lines:
										__magic_name__							=    re.sub(R'#.*'					,''					,a_				)  # remove comments
										if line:
															filtered_lines.append(a_				)
					__magic_name__							=    '\n'.join(a_				)

					# Make a hash from all this code
					__magic_name__							=    full_str.encode('utf-8'				)
					return shaaaa(a_				).hexdigest()


# get importable module names and hash for caching
A__						=							{
    "csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    "json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    "pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    "parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    "arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    "text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    "imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    "audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
A__						=							{
    ".csv": ("csv", {}),
    ".tsv": ("csv", {"sep": "\t"}),
    ".json": ("json", {}),
    ".jsonl": ("json", {}),
    ".parquet": ("parquet", {}),
    ".arrow": ("arrow", {}),
    ".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A__						=							{"imagefolder", "audiofolder"}

# Used to filter data files based on extensions given a module name
A__						=							{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
						_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")


_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")







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__						=							logging.get_logger(__name__)

A__						=							{
    "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__						=							{
    "b0": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 224,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1280,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 240,
        "dropout_rate": 0.2,
        "dw_padding": [16],
    },
    "b2": {
        "hidden_dim": 1408,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 260,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 16],
    },
    "b3": {
        "hidden_dim": 1536,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 300,
        "dropout_rate": 0.3,
        "dw_padding": [5, 18],
    },
    "b4": {
        "hidden_dim": 1792,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 380,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2048,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 456,
        "dropout_rate": 0.4,
        "dw_padding": [13, 27],
    },
    "b6": {
        "hidden_dim": 2304,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 528,
        "dropout_rate": 0.5,
        "dw_padding": [31],
    },
    "b7": {
        "hidden_dim": 2560,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 600,
        "dropout_rate": 0.5,
        "dw_padding": [18],
    },
}




def 					_lowercase     (				a_				:					List[Any]				)				->	Any:




					'''simple docstring'''







					__magic_name__							=    EfficientNetConfig()
					__magic_name__							=    CONFIG_MAP[model_name]['hidden_dim']
					__magic_name__							=    CONFIG_MAP[model_name]['width_coef']
					__magic_name__							=    CONFIG_MAP[model_name]['depth_coef']
					__magic_name__							=    CONFIG_MAP[model_name]['image_size']
					__magic_name__							=    CONFIG_MAP[model_name]['dropout_rate']
					__magic_name__							=    CONFIG_MAP[model_name]['dw_padding']

					__magic_name__							=    'huggingface/label-files'
					__magic_name__							=    'imagenet-1k-id2label.json'
					__magic_name__							=    1_0_0_0
					__magic_name__							=    json.load(open(hf_hub_download(a_					,a_					,repo_type='dataset'				)					,'r'				)				)
					__magic_name__							=    {int(a_				): v for k, v in idalabel.items()}

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




def 					_lowercase     (				)				->	int:




					'''simple docstring'''







					__magic_name__							=    'http://images.cocodataset.org/val2017/000000039769.jpg'
					__magic_name__							=    Image.open(requests.get(a_					,stream=a_				).raw				)
					return im




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




					'''simple docstring'''







					__magic_name__							=    CONFIG_MAP[model_name]['image_size']
					__magic_name__							=    EfficientNetImageProcessor(
					    size={'height': size, 'width': size}					,image_mean=[0.485, 0.456, 0.406]					,image_std=[0.47853944, 0.4732864, 0.47434163]					,do_center_crop=a_					,)
					return preprocessor




def 					_lowercase     (				a_				:					List[str]				)				->	List[Any]:




					'''simple docstring'''







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

					__magic_name__							=    []
					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:
										__magic_name__							=    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')				)

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

					__magic_name__							=    'classifier.weight'
					__magic_name__							=    'classifier.bias'
					return key_mapping




def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Optional[Any]					,a_				:					List[Any]				)				->	Any:




					'''simple docstring'''







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

										__magic_name__							=    key_mapping[key]
										if "_conv" in key and "kernel" in key:
															__magic_name__							=    torch.from_numpy(a_				).permute(3					,2					,0					,1				)
										elif "depthwise_kernel" in key:
															__magic_name__							=    torch.from_numpy(a_				).permute(2					,3					,0					,1				)
										elif "kernel" in key:
															__magic_name__							=    torch.from_numpy(np.transpose(a_				)				)
										else:
															__magic_name__							=    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 					_lowercase     (				a_				:					Dict					,a_				:					Optional[Any]					,a_				:					Any					,a_				:					Dict				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    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'					,)

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

					# Load HuggingFace model
					__magic_name__							=    get_efficientnet_config(a_				)
					__magic_name__							=    EfficientNetForImageClassification(a_				).eval()
					__magic_name__							=    hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print('Converting parameters...'				)
					__magic_name__							=    rename_keys(a_				)
					replace_params(a_					,a_					,a_				)

					# Initialize preprocessor and preprocess input image
					__magic_name__							=    convert_image_processor(a_				)
					__magic_name__							=    preprocessor(images=prepare_img()					,return_tensors='pt'				)

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

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


if __name__ == "__main__":
						A__						=							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__						=							parser.parse_args()
						convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




