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      SCREAMING_SNAKE_CASE__					:
       A							:	nn.Module
       A							:	List[nn.Module]          =	field(default_factory=__UpperCamelCase    )
       A							:	list          =	field(default_factory=__UpperCamelCase    )
       A							:	Dict[str, nn.Module]          =	field(default_factory=__UpperCamelCase    )

       def 			snake_case__    (							self					:     int			,			_lowerCAmelCase					:     Union[str, Any]			,			_lowerCAmelCase					:     Tensor			,			_lowerCAmelCase					:     Tensor			,			_lowerCAmelCase					:     str			):
           __snake_case       :				List[str] 			=    len(list(m.modules()			)			) == 1 or isinstance(_lowerCAmelCase			,			nn.Convad			) or isinstance(_lowerCAmelCase			,			nn.BatchNormad			)
           if has_not_submodules:
               self.traced.append(_lowerCAmelCase			)
               __snake_case       :				Any 			=    m

       def __call__(							self					:     int			,			_lowerCAmelCase					:     Tensor			):
           for name, m in self.module.named_modules():
               self.handles.append(m.register_forward_hook(partial(self._forward_hook			,			name=_lowerCAmelCase			)			)			)
           self.module(_lowerCAmelCase			)
           [x.remove() for x in self.handles]
           return self





       @property
       def 			snake_case__    (							self					:     List[Any]			):
           # check the len of the state_dict keys to see if we have learnable params
           return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()			)			) > 0}







class      SCREAMING_SNAKE_CASE__					(  nn.Module    ):

       def __init__(							self					:     Optional[int]			,			_lowerCAmelCase					:     nn.Module			):
           super().__init__()

           __snake_case       :				List[Tuple[str, nn.Module]] 			=    []
           # - 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}'''
               __snake_case       :				List[str] 			=    len(_lowerCAmelCase			) + 1
               feature_blocks.append((f'''res{block_index}''', v)			)

           __snake_case       :				int 			=    nn.ModuleDict(_lowerCAmelCase			)





       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     Tensor			):
           return get_trunk_forward_outputs(
               _lowerCAmelCase			,			out_feat_keys=_lowerCAmelCase			,			feature_blocks=self._feature_blocks			,			)







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       def 			snake_case__    (							self					:     Optional[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				(		__SCREAMING_SNAKE_CASE       :					str   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    RegNetConfig(depths=[2, 7, 1_7, 1]		,							hidden_sizes=[8, 8, 8, 8]		,							groups_width=8   )
    if "in1k" in model_name:
        __snake_case       :				Optional[Any] 			=    RegNetForImageClassification(__SCREAMING_SNAKE_CASE   )
    else:
        __snake_case       :				Union[str, Any] 			=    RegNetModel(__SCREAMING_SNAKE_CASE   )
    # create from model (with small weights)
    __snake_case       :				List[Any] 			=    FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=2_7		,							group_width=1_0_1_0		,							w_a=1_7_4_4		,							w_a=6_20.83		,							w_m=2.52   )   )   )

    with torch.no_grad():
        __snake_case       :				str 			=    from_model.eval()
        __snake_case       :				Optional[Any] 			=    our_model.eval()

        __snake_case       :				str 			=    torch.randn((1, 3, 3_2, 3_2)   )
        # trace both
        __snake_case       :				int 			=    Tracker(__SCREAMING_SNAKE_CASE   )
        __snake_case       :				Optional[int] 			=    dest_tracker(__SCREAMING_SNAKE_CASE   ).parametrized

        pprint(dest_tracker.nameamodule   )
        __snake_case       :				str 			=    Tracker(__SCREAMING_SNAKE_CASE   )
        __snake_case       :				Optional[Any] 			=    src_tracker(__SCREAMING_SNAKE_CASE   ).parametrized

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

    __snake_case       :				Dict 			=    {}

    __snake_case       :				Any 			=    to_params_dict(__SCREAMING_SNAKE_CASE   )
    __snake_case       :				Dict 			=    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()   ):
        __snake_case       :				Dict 			=    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:
        __snake_case       :				Dict 			=    """classifier.1.weight"""
        __snake_case       :				Union[str, Any] 			=    """classifier.1.bias"""

    return from_to_ours_keys







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Path		,							__SCREAMING_SNAKE_CASE       :					str = None		,							__SCREAMING_SNAKE_CASE       :					bool = True   ):







    '''simple docstring'''
    __snake_case       :				Any 			=    """imagenet-1k-id2label.json"""
    __snake_case       :				Tuple 			=    1_0_0_0

    __snake_case       :				List[str] 			=    """huggingface/label-files"""
    __snake_case       :				int 			=    num_labels
    __snake_case       :				str 			=    json.load(open(cached_download(hf_hub_url(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE		,							repo_type="""dataset"""   )   )		,							"""r"""   )   )
    __snake_case       :				Union[str, Any] 			=    {int(__SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}

    __snake_case       :				Optional[Any] 			=    idalabel
    __snake_case       :				str 			=    {v: k for k, v in idalabel.items()}

    __snake_case       :				List[str] 			=    partial(__SCREAMING_SNAKE_CASE		,							num_labels=__SCREAMING_SNAKE_CASE		,							idalabel=__SCREAMING_SNAKE_CASE		,							labelaid=__SCREAMING_SNAKE_CASE   )

    __snake_case       :				str 			=    {
        """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
            depths=[2, 7, 1_7, 1]		,							hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]		,							groups_width=1_0_1_0   ),
        # finetuned on imagenet
        """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
            depths=[2, 7, 1_7, 1]		,							hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]		,							groups_width=1_0_1_0   ),
    }

    # add seer weights logic
    def load_using_classy_vision(__SCREAMING_SNAKE_CASE       :					str   ) -> Tuple[Dict, Dict]:
        __snake_case       :				Optional[Any] 			=    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
        __snake_case       :				int 			=    files["""classy_state_dict"""]["""base_model"""]["""model"""]
        return model_state_dict["trunk"], model_state_dict["heads"]

    __snake_case       :				List[str] 			=    {
        """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"""		,							),
    }

    __snake_case       :				Optional[int] 			=    get_from_to_our_keys(__SCREAMING_SNAKE_CASE   )

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

        __snake_case       :				Optional[Any] 			=    {}

        __snake_case       :				str 			=    list(from_state_dict.keys()   )
        __snake_case       :				int 			=    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
            __snake_case       :				Dict 			=    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
            __snake_case       :				Dict 			=    from_to_ours_keys[src_key]
            # store the parameter with our key
            __snake_case       :				Any 			=    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
        __snake_case       :				List[str] 			=    names_to_config[model_name]
        __snake_case       :				List[str] 			=    RegNetModel
        if "in1k" in model_name:
            __snake_case       :				Optional[Any] 			=    RegNetForImageClassification
        __snake_case       :				str 			=    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
        __snake_case       :				Union[str, Any] 			=    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		,							)
        __snake_case       :				Optional[int] 			=    3_8_4
        # we can use the convnext one
        __snake_case       :				Any 			=    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)




import math
from timeit import timeit







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    __snake_case       :				Optional[Any] 			=    0
    __snake_case       :				Union[str, Any] 			=    abs(__SCREAMING_SNAKE_CASE   )
    while True:
        __snake_case       :				List[str] 			=    n // 1_0
        digits += 1
        if n == 0:
            break
    return digits







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return 1 if n == 0 else math.floor(math.log(abs(__SCREAMING_SNAKE_CASE   )		,							1_0   ) + 1   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					int   ):







    '''simple docstring'''
    return len(str(abs(__SCREAMING_SNAKE_CASE   )   )   )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    from collections.abc import Callable

    def benchmark_a_function(__SCREAMING_SNAKE_CASE       :					Callable		,							__SCREAMING_SNAKE_CASE       :					int   ) -> None:
        __snake_case       :				Tuple 			=    F'''{func.__name__}({value})'''
        __snake_case       :				Union[str, Any] 			=    timeit(F'''__main__.{call}'''		,							setup="""import __main__"""   )
        print(F'''{call}: {func(__SCREAMING_SNAKE_CASE   )} -- {timing} seconds'''   )

    for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
        for func in (num_digits, num_digits_fast, num_digits_faster):
            benchmark_a_function(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()



