



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()
A__       =			logging.get_logger()





@dataclass
class    __lowerCAmelCase					:
				__lowerCamelCase					=							42
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)
				__lowerCamelCase					=							field(default_factory=lowerCamelCase__			)




				def 				snake_case  (				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       len(list(m.modules()						)						) == 1 or isinstance(_snake_case		,							nn.Convad						) or isinstance(_snake_case		,							nn.BatchNormad						)
									if has_not_submodules:
														self.traced.append(_snake_case						)
														_lowerCAmelCase         =       m




				def __call__(				self		,							_snake_case						):



									"""simple docstring"""





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




				@property
				def 				snake_case  (				self						):



									"""simple docstring"""





									return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()						)						) > 0}





class    __lowerCAmelCase					(	nn.Module			):




				def __init__(				self		,							_snake_case						):



									"""simple docstring"""





									super().__init__()

									_lowerCAmelCase         =       []
									# - 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}'
														_lowerCAmelCase         =       len(_snake_case						) + 1
														feature_blocks.append((F'res{block_index}', v)						)

									_lowerCAmelCase         =       nn.ModuleDict(_snake_case						)




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									return get_trunk_forward_outputs(
									    _snake_case		,							out_feat_keys=_snake_case		,							feature_blocks=self._feature_blocks		,							)





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def 				snake_case  (				self						):



									"""simple docstring"""





									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 						_UpperCAmelCase							( snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       RegNetConfig(depths=[2, 7, 17, 1]      ,		hidden_sizes=[8, 8, 8, 8]      ,		groups_width=8     )
					if "in1k" in model_name:
										_lowerCAmelCase         =       RegNetForImageClassification(snake_case     )
					else:
										_lowerCAmelCase         =       RegNetModel(snake_case     )
					# create from model (with small weights)
					_lowerCAmelCase         =       FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27      ,		group_width=10_10      ,		w_a=17_44      ,		w_a=620.83      ,		w_m=2.52     )     )     )

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

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

										pprint(dest_tracker.nameamodule     )
										_lowerCAmelCase         =       Tracker(snake_case     )
										_lowerCAmelCase         =       src_tracker(snake_case     ).parametrized

					# convert the keys -> module dict to keys -> params
					def to_params_dict(snake_case     ):
										_lowerCAmelCase         =       OrderedDict()
										for name, module in dict_with_modules.items():
															for param_name, param in module.state_dict().items():
																				_lowerCAmelCase         =       param
										return params_dict

					_lowerCAmelCase         =       {}

					_lowerCAmelCase         =       to_params_dict(snake_case     )
					_lowerCAmelCase         =       to_params_dict(snake_case     )

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()      ,		dst_state_dict.items()     ):
										_lowerCAmelCase         =       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:
										_lowerCAmelCase         =       """classifier.1.weight"""
										_lowerCAmelCase         =       """classifier.1.bias"""

					return from_to_ours_keys


def 						_UpperCAmelCase							( snake_case      ,		snake_case = None      ,		snake_case = True     ):





					"""simple docstring"""

					_lowerCAmelCase         =       """imagenet-1k-id2label.json"""
					_lowerCAmelCase         =       10_00

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

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

					_lowerCAmelCase         =       partial(snake_case      ,		num_labels=snake_case      ,		idalabel=snake_case      ,		labelaid=snake_case     )

					_lowerCAmelCase         =       {
					    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]      ,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      ,		groups_width=10_10     ),
					    # finetuned on imagenet
					    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]      ,		hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]      ,		groups_width=10_10     ),
					}

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

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

					_lowerCAmelCase         =       get_from_to_our_keys(snake_case     )

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

										_lowerCAmelCase         =       {}

										_lowerCAmelCase         =       list(from_state_dict.keys()     )
										_lowerCAmelCase         =       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
															_lowerCAmelCase         =       re.sub(snake_case      ,		""""""      ,		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
															_lowerCAmelCase         =       from_to_ours_keys[src_key]
															# store the parameter with our key
															_lowerCAmelCase         =       from_state_dict[key]
															not_used_keys.remove(snake_case     )
										# check that all keys have been updated
										assert len(snake_case     ) == 0, F'Some keys where not used {",".join(snake_case     )}'

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

										# save our state dict to disk
										torch.save(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
										_lowerCAmelCase         =       names_to_config[model_name]
										_lowerCAmelCase         =       RegNetModel
										if "in1k" in model_name:
															_lowerCAmelCase         =       RegNetForImageClassification
										_lowerCAmelCase         =       our_model_func(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
										_lowerCAmelCase         =       our_model.state_dict().keys()
										PreTrainedModel._load_pretrained_model_low_mem(
										    snake_case      ,		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      ,		)
										_lowerCAmelCase         =       3_84
										# we can use the convnext one
										_lowerCAmelCase         =       AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""      ,		size=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__":
	A__       =			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.""",
	)

	A__       =			parser.parse_args()

	A__       =			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 		snake_case_				(_a      :				Any       ,  _a      :				Union[str, Any]	):
    if n < 0:
        return 0
    # table[i] represents the number of ways to get to amount i
    UpperCAmelCase     			=						[0] * (n + 1)

    # There is exactly 1 way to get to zero(You pick no coins).
    UpperCAmelCase     			=						1

    # Pick all coins one by one and update table[] values
    # after the index greater than or equal to the value of the
    # picked coin
    for coin_val in s:
        for j in range(_a       ,  n + 1	):
            table[j] += table[j - coin_val]

    return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()




