




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						:		Union[str, Any]          =						logging.get_logger()

@dataclass
class 		lowercase_       :
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= field(default_factory=lowerCAmelCase__				)
					__UpperCamelCase     	= field(default_factory=lowerCAmelCase__				)
					__UpperCamelCase     	= field(default_factory=lowerCAmelCase__				)




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		Tuple,     _lowercase:		Tensor,     _lowercase:		Tensor,     _lowercase:		str):


									'''simple docstring'''


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




					def __call__(   self:		Optional[Any],     _lowercase:		Tensor):


									'''simple docstring'''


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







					@property
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


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





class 		lowercase_       (	nn.Module				):




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


									'''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(_lowercase) + 1
													feature_blocks.append((f'''res{block_index}''', v))

									__lowerCAmelCase					=  nn.ModuleDict(_lowercase)







					def     _lowercase						(   self:		List[str],     _lowercase:		Tensor):


									'''simple docstring'''


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





class 		lowercase_       (	lowerCAmelCase__				):




					def     _lowercase						(   self:		Dict):


									'''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  (						UpperCamelCase__   )     ->     Dict[str, str]:







				'''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(UpperCamelCase__   )
				else:
								__lowerCAmelCase					=  RegNetModel(UpperCamelCase__   )
				# 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(UpperCamelCase__   )
								__lowerCAmelCase					=  dest_tracker(UpperCamelCase__   ).parametrized

								pprint(dest_tracker.nameamodule   )
								__lowerCAmelCase					=  Tracker(UpperCamelCase__   )
								__lowerCAmelCase					=  src_tracker(UpperCamelCase__   ).parametrized

				# convert the keys -> module dict to keys -> params
				def to_params_dict(UpperCamelCase__   ):
								__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(UpperCamelCase__   )
				__lowerCAmelCase					=  to_params_dict(UpperCamelCase__   )

				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  (						UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = True   )     ->     Tuple:







				'''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(UpperCamelCase__						,			UpperCamelCase__						,			repo_type="""dataset"""   )   )						,			"""r"""   )   )
				__lowerCAmelCase					=  {int(UpperCamelCase__   ): v for k, v in idalabel.items()}

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

				__lowerCAmelCase					=  partial(UpperCamelCase__						,			num_labels=UpperCamelCase__						,			idalabel=UpperCamelCase__						,			labelaid=UpperCamelCase__   )

				__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(UpperCamelCase__   ) -> Tuple[Dict, Dict]:
								__lowerCAmelCase					=  torch.hub.load_state_dict_from_url(UpperCamelCase__						,			model_dir=str(UpperCamelCase__   )						,			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(
				        UpperCamelCase__						,			"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""						,			),
				    """regnet-y-10b-seer-in1k""": partial(
				        UpperCamelCase__						,			"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""						,			),
				}

				__lowerCAmelCase					=  get_from_to_our_keys(UpperCamelCase__   )

				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(UpperCamelCase__						,			""""""						,			UpperCamelCase__   )
												# 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(UpperCamelCase__   )
								# check that all keys have been updated
								assert len(UpperCamelCase__   ) == 0, F'''Some keys where not used {','.join(UpperCamelCase__   )}'''

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

								# save our state dict to disk
								torch.save(UpperCamelCase__						,			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(UpperCamelCase__   )
								# 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(
								    UpperCamelCase__						,			UpperCamelCase__						,			[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=UpperCamelCase__   )
								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						:		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 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						:		Tuple          =						parser.parse_args()

	__A						:		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)








import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__A     						=      logging.get_logger(__name__)



class     lowercase_       (    __lowercase	):



      def __init__(     self :							List[str]     ,   *A__ :							Tuple     ,   **A__ :							int						)       ->	None:
        warnings.warn(
            '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DetrImageProcessor instead.'''     ,   A__     ,   )
        super().__init__(*A__     ,   **A__						)

