





'''simple docstring'''

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_			       =				logging.get_logger()


@dataclass
class       a_  :

    '''simple docstring'''
    UpperCamelCase									=				42
    UpperCamelCase									=				field(default_factory=lowerCAmelCase_ )
    UpperCamelCase									=				field(default_factory=lowerCAmelCase_ )
    UpperCamelCase									=				field(default_factory=lowerCAmelCase_ )


    def     snake_case_(	self				,	A				,	A				,	A				,	A )	->     Union[str, Any]:
        _SCREAMING_SNAKE_CASE             =    len(list(m.modules() ) ) == 1 or isinstance(__a				,	nn.Convad ) or isinstance(__a				,	nn.BatchNormad )
        if has_not_submodules:
            self.traced.append(__a )
            _SCREAMING_SNAKE_CASE             =    m


    def __call__(	self				,	A )	->     Any:





        for name, m in self.module.named_modules():
            self.handles.append(m.register_forward_hook(partial(self._forward_hook				,	name=__a ) ) )
        self.module(__a )
        [x.remove() for x in self.handles]
        return self





    @property
    def     snake_case_(	self )	->     str:
        return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys() ) ) > 0}




class       a_  (       nn.Module ):

    '''simple docstring'''


    def __init__(	self				,	A )	->     Optional[int]:
        super().__init__()

        _SCREAMING_SNAKE_CASE             =    []
        # - get the stem
        feature_blocks.append(("""conv1""", model.stem) )
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
            assert k.startswith("""block""" ), f'Unexpected layer name {k}'
            _SCREAMING_SNAKE_CASE             =    len(__a ) + 1
            feature_blocks.append((f'res{block_index}', v) )

        _SCREAMING_SNAKE_CASE             =    nn.ModuleDict(__a )





    def     snake_case_(	self				,	A )	->     Dict:
        return get_trunk_forward_outputs(
            __a				,	out_feat_keys=__a				,	feature_blocks=self._feature_blocks				,	)




class       a_  (       lowerCAmelCase_ ):

    '''simple docstring'''


    def     snake_case_(	self )	->     List[Any]:
        return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]





def lowerCamelCase		(  __lowerCamelCase				:					str					)		->Dict:
    _SCREAMING_SNAKE_CASE             =    RegNetConfig(depths=[2, 7, 17, 1]      , hidden_sizes=[8, 8, 8, 8]      , groups_width=8					)
    if "in1k" in model_name:
        _SCREAMING_SNAKE_CASE             =    RegNetForImageClassification(_SCREAMING_SNAKE_CASE					)
    else:
        _SCREAMING_SNAKE_CASE             =    RegNetModel(_SCREAMING_SNAKE_CASE					)
    # create from model (with small weights)
    _SCREAMING_SNAKE_CASE             =    FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27      , group_width=1010      , w_a=1744      , w_a=620.83      , w_m=2.52					)					)					)

    with torch.no_grad():
        _SCREAMING_SNAKE_CASE             =    from_model.eval()
        _SCREAMING_SNAKE_CASE             =    our_model.eval()

        _SCREAMING_SNAKE_CASE             =    torch.randn((1, 3, 32, 32)					)
        # trace both
        _SCREAMING_SNAKE_CASE             =    Tracker(_SCREAMING_SNAKE_CASE					)
        _SCREAMING_SNAKE_CASE             =    dest_tracker(_SCREAMING_SNAKE_CASE					).parametrized

        pprint(dest_tracker.nameamodule					)
        _SCREAMING_SNAKE_CASE             =    Tracker(_SCREAMING_SNAKE_CASE					)
        _SCREAMING_SNAKE_CASE             =    src_tracker(_SCREAMING_SNAKE_CASE					).parametrized

    # convert the keys -> module dict to keys -> params
    def to_params_dict(__lowerCamelCase				:					List[Any]					):
        _SCREAMING_SNAKE_CASE             =    OrderedDict()
        for name, module in dict_with_modules.items():
            for param_name, param in module.state_dict().items():
                _SCREAMING_SNAKE_CASE             =    param
        return params_dict

    _SCREAMING_SNAKE_CASE             =    {}

    _SCREAMING_SNAKE_CASE             =    to_params_dict(_SCREAMING_SNAKE_CASE					)
    _SCREAMING_SNAKE_CASE             =    to_params_dict(_SCREAMING_SNAKE_CASE					)

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()      , dst_state_dict.items()					):
        _SCREAMING_SNAKE_CASE             =    dest_key
        logger.info(F'{src_key} -> {dest_key}'					)
    # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
    if "in1k" in model_name:
        _SCREAMING_SNAKE_CASE             =    'classifier.1.weight'
        _SCREAMING_SNAKE_CASE             =    'classifier.1.bias'

    return from_to_ours_keys





def lowerCamelCase		(  __lowerCamelCase				:					Path      , __lowerCamelCase				:					str = None      , __lowerCamelCase				:					bool = True					)		->Tuple:
    _SCREAMING_SNAKE_CASE             =    'imagenet-1k-id2label.json'
    _SCREAMING_SNAKE_CASE             =    1000

    _SCREAMING_SNAKE_CASE             =    'huggingface/label-files'
    _SCREAMING_SNAKE_CASE             =    num_labels
    _SCREAMING_SNAKE_CASE             =    json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , repo_type="""dataset"""					)					)      , """r"""					)					)
    _SCREAMING_SNAKE_CASE             =    {int(_SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}

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

    _SCREAMING_SNAKE_CASE             =    partial(_SCREAMING_SNAKE_CASE      , num_labels=_SCREAMING_SNAKE_CASE      , idalabel=_SCREAMING_SNAKE_CASE      , labelaid=_SCREAMING_SNAKE_CASE					)

    _SCREAMING_SNAKE_CASE             =    {
        'regnet-y-10b-seer': ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]      , hidden_sizes=[2020, 4040, 1_1110, 2_8280]      , groups_width=1010					),
        # finetuned on imagenet
        'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]      , hidden_sizes=[2020, 4040, 1_1110, 2_8280]      , groups_width=1010					),
    }

    # add seer weights logic
    def load_using_classy_vision(__lowerCamelCase				:					str					) -> Tuple[Dict, Dict]:
        _SCREAMING_SNAKE_CASE             =    torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE      , model_dir=str(_SCREAMING_SNAKE_CASE					)      , map_location="""cpu"""					)
        # check if we have a head, if yes add it
        _SCREAMING_SNAKE_CASE             =    files['classy_state_dict']['base_model']['model']
        return model_state_dict["trunk"], model_state_dict["heads"]

    _SCREAMING_SNAKE_CASE             =    {
        'regnet-y-10b-seer': partial(
            _SCREAMING_SNAKE_CASE      , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""      , ),
        'regnet-y-10b-seer-in1k': partial(
            _SCREAMING_SNAKE_CASE      , """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""      , ),
    }

    _SCREAMING_SNAKE_CASE             =    get_from_to_our_keys(_SCREAMING_SNAKE_CASE					)

    if not (save_directory / F'{model_name}.pth').exists():
        logger.info("""Loading original state_dict."""					)
        _SCREAMING_SNAKE_CASE             =    names_to_from_model[model_name]()
        _SCREAMING_SNAKE_CASE             =    from_state_dict_trunk
        if "in1k" in model_name:
            # add the head
            _SCREAMING_SNAKE_CASE             =    {**from_state_dict_trunk, **from_state_dict_head}
        logger.info("""Done!"""					)

        _SCREAMING_SNAKE_CASE             =    {}

        _SCREAMING_SNAKE_CASE             =    list(from_state_dict.keys()					)
        _SCREAMING_SNAKE_CASE             =    r'\.block.-part.'
        # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
        for key in from_state_dict.keys():
            # remove the weird "block[0,1]-part" from the key
            _SCREAMING_SNAKE_CASE             =    re.sub(_SCREAMING_SNAKE_CASE      , """"""      , _SCREAMING_SNAKE_CASE					)
            # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
            _SCREAMING_SNAKE_CASE             =    from_to_ours_keys[src_key]
            # store the parameter with our key
            _SCREAMING_SNAKE_CASE             =    from_state_dict[key]
            not_used_keys.remove(_SCREAMING_SNAKE_CASE					)
        # check that all keys have been updated
        assert len(_SCREAMING_SNAKE_CASE					) == 0, F'Some keys where not used {",".join(_SCREAMING_SNAKE_CASE					)}'

        logger.info(F'The following keys were not used: {",".join(_SCREAMING_SNAKE_CASE					)}'					)

        # save our state dict to disk
        torch.save(_SCREAMING_SNAKE_CASE      , save_directory / F'{model_name}.pth'					)

        del converted_state_dict
    else:
        logger.info("""The state_dict was already stored on disk."""					)
    if push_to_hub:
        logger.info(F'Token is {os.environ["HF_TOKEN"]}'					)
        logger.info("""Loading our model."""					)
        # create our model
        _SCREAMING_SNAKE_CASE             =    names_to_config[model_name]
        _SCREAMING_SNAKE_CASE             =    RegNetModel
        if "in1k" in model_name:
            _SCREAMING_SNAKE_CASE             =    RegNetForImageClassification
        _SCREAMING_SNAKE_CASE             =    our_model_func(_SCREAMING_SNAKE_CASE					)
        # place our model to the meta device (so remove all the weights)
        our_model.to(torch.device("""meta"""					)					)
        logger.info("""Loading state_dict in our model."""					)
        # load state dict
        _SCREAMING_SNAKE_CASE             =    our_model.state_dict().keys()
        PreTrainedModel._load_pretrained_model_low_mem(
            _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , [save_directory / F'{model_name}.pth']					)
        logger.info("""Finally, pushing!"""					)
        # push it to hub
        our_model.push_to_hub(
            repo_path_or_name=save_directory / model_name      , commit_message="""Add model"""      , output_dir=save_directory / model_name      , )
        _SCREAMING_SNAKE_CASE             =    384
        # we can use the convnext one
        _SCREAMING_SNAKE_CASE             =    AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      , size=_SCREAMING_SNAKE_CASE					)
        image_processor.push_to_hub(
            repo_path_or_name=save_directory / model_name      , commit_message="""Add image processor"""      , output_dir=save_directory / model_name      , )


if __name__ == "__main__":
       lowercase_			       =				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 regnet* architecture,"""
               """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
           ),
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""",
           default=None,
           type=Path,
           required=True,
           help="""Path to the output PyTorch model directory.""",
       )
       parser.add_argument(
           """--push_to_hub""",
           default=True,
           type=bool,
           required=False,
           help="""If True, push model and image processor to the hub.""",
       )

       lowercase_			       =				parser.parse_args()

       lowercase_			       =				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'''

def lowerCamelCase		(  __lowerCamelCase				:					float      , __lowerCamelCase				:					float      , __lowerCamelCase				:					float					)		->list[int]:
    if hue < 0 or hue > 360:
        raise Exception("""hue should be between 0 and 360"""					)

    if saturation < 0 or saturation > 1:
        raise Exception("""saturation should be between 0 and 1"""					)

    if value < 0 or value > 1:
        raise Exception("""value should be between 0 and 1"""					)

    _SCREAMING_SNAKE_CASE             =    value * saturation
    _SCREAMING_SNAKE_CASE             =    hue / 60
    _SCREAMING_SNAKE_CASE             =    chroma * (1 - abs(hue_section % 2 - 1					))
    _SCREAMING_SNAKE_CASE             =    value - chroma

    if hue_section >= 0 and hue_section <= 1:
        _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
    elif hue_section > 1 and hue_section <= 2:
        _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
    elif hue_section > 2 and hue_section <= 3:
        _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
    elif hue_section > 3 and hue_section <= 4:
        _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
    elif hue_section > 4 and hue_section <= 5:
        _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
    else:
        _SCREAMING_SNAKE_CASE             =    round(255 * (chroma + match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (match_value)					)
        _SCREAMING_SNAKE_CASE             =    round(255 * (second_largest_component + match_value)					)

    return [red, green, blue]





def lowerCamelCase		(  __lowerCamelCase				:					int      , __lowerCamelCase				:					int      , __lowerCamelCase				:					int					)		->list[float]:
    if red < 0 or red > 255:
        raise Exception("""red should be between 0 and 255"""					)

    if green < 0 or green > 255:
        raise Exception("""green should be between 0 and 255"""					)

    if blue < 0 or blue > 255:
        raise Exception("""blue should be between 0 and 255"""					)

    _SCREAMING_SNAKE_CASE             =    red / 255
    _SCREAMING_SNAKE_CASE             =    green / 255
    _SCREAMING_SNAKE_CASE             =    blue / 255
    _SCREAMING_SNAKE_CASE             =    max(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
    _SCREAMING_SNAKE_CASE             =    value - min(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
    _SCREAMING_SNAKE_CASE             =    0 if value == 0 else chroma / value

    if chroma == 0:
        _SCREAMING_SNAKE_CASE             =    0.0
    elif value == float_red:
        _SCREAMING_SNAKE_CASE             =    60 * (0 + (float_green - float_blue) / chroma)
    elif value == float_green:
        _SCREAMING_SNAKE_CASE             =    60 * (2 + (float_blue - float_red) / chroma)
    else:
        _SCREAMING_SNAKE_CASE             =    60 * (4 + (float_red - float_green) / chroma)

    _SCREAMING_SNAKE_CASE             =    (hue + 360) % 360

    return [hue, saturation, value]






def lowerCamelCase		(  __lowerCamelCase				:					list[float]      , __lowerCamelCase				:					list[float]					)		->bool:
    _SCREAMING_SNAKE_CASE             =    abs(hsv_a[0] - hsv_a[0]					) < 0.2
    _SCREAMING_SNAKE_CASE             =    abs(hsv_a[1] - hsv_a[1]					) < 0.002
    _SCREAMING_SNAKE_CASE             =    abs(hsv_a[2] - hsv_a[2]					) < 0.002

    return check_hue and check_saturation and check_value



