





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase				:       Tuple	   =				logging.get_logger()


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = True )			->     int:
    print(F"""Converting {name}...""" )

    with torch.no_grad():
        if hidden_sizes == 128:
            if name[-1] == "S":
                lowerCamelCase__    :      Union[str, Any]					 =		timm.create_model('levit_128s'						,	pretrained=_UpperCAmelCase )
            else:
                lowerCamelCase__    :      int					 =		timm.create_model('levit_128'						,	pretrained=_UpperCAmelCase )
        if hidden_sizes == 192:
            lowerCamelCase__    :      List[str]					 =		timm.create_model('levit_192'						,	pretrained=_UpperCAmelCase )
        if hidden_sizes == 256:
            lowerCamelCase__    :      List[Any]					 =		timm.create_model('levit_256'						,	pretrained=_UpperCAmelCase )
        if hidden_sizes == 384:
            lowerCamelCase__    :      int					 =		timm.create_model('levit_384'						,	pretrained=_UpperCAmelCase )

        from_model.eval()
        lowerCamelCase__    :      Optional[Any]					 =		LevitForImageClassificationWithTeacher(_UpperCAmelCase ).eval()
        lowerCamelCase__    :      List[str]					 =		OrderedDict()

        lowerCamelCase__    :      str					 =		from_model.state_dict()
        lowerCamelCase__    :      Optional[Any]					 =		list(from_model.state_dict().keys() )
        lowerCamelCase__    :      str					 =		list(our_model.state_dict().keys() )
        print(len(_UpperCAmelCase )						,	len(_UpperCAmelCase ) )
        for i in range(len(_UpperCAmelCase ) ):
            lowerCamelCase__    :      List[Any]					 =		weights[og_keys[i]]
        our_model.load_state_dict(_UpperCAmelCase )

        lowerCamelCase__    :      int					 =		torch.randn((2, 3, 224, 224) )
        lowerCamelCase__    :      Optional[int]					 =		from_model(_UpperCAmelCase )
        lowerCamelCase__    :      Any					 =		our_model(_UpperCAmelCase ).logits

    assert torch.allclose(_UpperCAmelCase						,	_UpperCAmelCase ), "The model logits don't match the original one."

    lowerCamelCase__    :      Optional[Any]					 =		name
    print(_UpperCAmelCase )

    if push_to_hub:
        our_model.save_pretrained(save_directory / checkpoint_name )
        lowerCamelCase__    :      int					 =		LevitImageProcessor()
        image_processor.save_pretrained(save_directory / checkpoint_name )

        print(F"""Pushed {checkpoint_name}""" )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True )			->     List[str]:
    lowerCamelCase__    :      Optional[int]					 =		'imagenet-1k-id2label.json'
    lowerCamelCase__    :      Tuple					 =		1000
    lowerCamelCase__    :      Union[str, Any]					 =		(1, num_labels)

    lowerCamelCase__    :      List[str]					 =		'huggingface/label-files'
    lowerCamelCase__    :      int					 =		num_labels
    lowerCamelCase__    :      Tuple					 =		json.load(open(hf_hub_download(_UpperCAmelCase						,	_UpperCAmelCase						,	repo_type='dataset' )						,	'r' ) )
    lowerCamelCase__    :      List[Any]					 =		{int(_UpperCAmelCase ): v for k, v in idalabel.items()}

    lowerCamelCase__    :      List[Any]					 =		idalabel
    lowerCamelCase__    :      Union[str, Any]					 =		{v: k for k, v in idalabel.items()}

    lowerCamelCase__    :      Union[str, Any]					 =		partial(_UpperCAmelCase						,	num_labels=_UpperCAmelCase						,	idalabel=_UpperCAmelCase						,	labelaid=_UpperCAmelCase )

    lowerCamelCase__    :      int					 =		{
        'levit-128S': 128,
        'levit-128': 128,
        'levit-192': 192,
        'levit-256': 256,
        'levit-384': 384,
    }

    lowerCamelCase__    :      int					 =		{
        'levit-128S': ImageNetPreTrainedConfig(
            hidden_sizes=[128, 256, 384]						,	num_attention_heads=[4, 6, 8]						,	depths=[2, 3, 4]						,	key_dim=[16, 16, 16]						,	drop_path_rate=0						,	),
        'levit-128': ImageNetPreTrainedConfig(
            hidden_sizes=[128, 256, 384]						,	num_attention_heads=[4, 8, 12]						,	depths=[4, 4, 4]						,	key_dim=[16, 16, 16]						,	drop_path_rate=0						,	),
        'levit-192': ImageNetPreTrainedConfig(
            hidden_sizes=[192, 288, 384]						,	num_attention_heads=[3, 5, 6]						,	depths=[4, 4, 4]						,	key_dim=[32, 32, 32]						,	drop_path_rate=0						,	),
        'levit-256': ImageNetPreTrainedConfig(
            hidden_sizes=[256, 384, 512]						,	num_attention_heads=[4, 6, 8]						,	depths=[4, 4, 4]						,	key_dim=[32, 32, 32]						,	drop_path_rate=0						,	),
        'levit-384': ImageNetPreTrainedConfig(
            hidden_sizes=[384, 512, 768]						,	num_attention_heads=[6, 9, 12]						,	depths=[4, 4, 4]						,	key_dim=[32, 32, 32]						,	drop_path_rate=0.1						,	),
    }

    if model_name:
        convert_weight_and_push(
            names_to_hidden_sizes[model_name]						,	_UpperCAmelCase						,	names_to_config[model_name]						,	_UpperCAmelCase						,	_UpperCAmelCase )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(names_to_hidden_sizes[model_name]						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )
    return config, expected_shape


if __name__ == "__main__":
    _UpperCAmelCase				:       Union[str, Any]	   =				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--model_name""",
        default=None,
        type=str,
        help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default="""levit-dump-folder/""",
        type=Path,
        required=False,
        help="""Path to the output PyTorch model directory.""",
    )
    parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
    parser.add_argument(
        """--no-push_to_hub""",
        dest="""push_to_hub""",
        action="""store_false""",
        help="""Do not push model and image processor to the hub""",
    )

    _UpperCAmelCase				:       Optional[Any]	   =				parser.parse_args()
    _UpperCAmelCase				:       Path	   =				args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










"""simple docstring"""






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def 			_lowercase				(  __snake_case = "laptop"	)						->   DataFrame:
			__lowerCAmelCase      :  str												=       F"""https://www.amazon.in/laptop/s?k={product}"""
			__lowerCAmelCase      :  Union[str, Any]												=       {
			    "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
			    "Accept-Language": "en-US, en;q=0.5",
			}
			__lowerCAmelCase      :  List[str]												=       BeautifulSoup(requests.get(__snake_case					,headers=__snake_case	).text	)
			# Initialize a Pandas dataframe with the column titles
			__lowerCAmelCase      :  Dict												=       DataFrame(
			    columns=[
			        "Product Title",
			        "Product Link",
			        "Current Price of the product",
			        "Product Rating",
			        "MRP of the product",
			        "Discount",
			    ]	)
			# Loop through each entry and store them in the dataframe
			for item, _ in zip_longest(
			    soup.find_all(
			        "div"					,attrs={"class": "s-result-item", "data-component-type": "s-search-result"}					,)					,soup.find_all("div"					,attrs={"class": "a-row a-size-base a-color-base"}	)					,):
						try:
									__lowerCAmelCase      :  Any												=       item.ha.text
									__lowerCAmelCase      :  Union[str, Any]												=       "https://www.amazon.in/" + item.ha.a["href"]
									__lowerCAmelCase      :  Any												=       item.find("span"					,attrs={"class": "a-offscreen"}	).text
									try:
												__lowerCAmelCase      :  Union[str, Any]												=       item.find("span"					,attrs={"class": "a-icon-alt"}	).text
									except AttributeError:
												__lowerCAmelCase      :  Optional[Any]												=       "Not available"
									try:
												__lowerCAmelCase      :  Union[str, Any]												=       (
												    "₹"
												    + item.find(
												        "span"					,attrs={"class": "a-price a-text-price"}	).text.split("₹"	)[1]
												)
									except AttributeError:
												__lowerCAmelCase      :  Dict												=       ""
									try:
												__lowerCAmelCase      :  str												=       float(
												    (
												        (
												            float(product_mrp.strip("₹"	).replace(","					,""	)	)
												            - float(product_price.strip("₹"	).replace(","					,""	)	)
												        )
												        / float(product_mrp.strip("₹"	).replace(","					,""	)	)
												    )
												    * 100	)
									except ValueError:
												__lowerCAmelCase      :  List[str]												=       float("nan"	)
						except AttributeError:
									pass
						__lowerCAmelCase      :  int												=       [
						    product_title,
						    product_link,
						    product_price,
						    product_rating,
						    product_mrp,
						    discount,
						]
			__lowerCAmelCase      :  Union[str, Any]												=       " "
			__lowerCAmelCase      :  Union[str, Any]												=       " "
			data_frame.index += 1
			return data_frame


if __name__ == "__main__":
	__snake_case							:   Any      =    'headphones'
	get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")