





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()
_UpperCAmelCase										=					logging.get_logger()



@dataclass
class 				_UpperCamelCase       :
       _UpperCamelCase					:       nn.Module
       _UpperCamelCase					:       List[nn.Module]       = field(default_factory=lowerCAmelCase_  )
       _UpperCamelCase					:       list       = field(default_factory=lowerCAmelCase_  )
       _UpperCamelCase					:       Dict[str, nn.Module]       = field(default_factory=lowerCAmelCase_  )

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Tensor       , _SCREAMING_SNAKE_CASE:			Tensor       , _SCREAMING_SNAKE_CASE:			str		)							->			Union[str, Any]:




              """simple docstring"""


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

       def __call__(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Tensor		)							->			Optional[Any]:




              """simple docstring"""


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



       @property
       def lowercase			(		self:			Dict		)							->			Dict:




              """simple docstring"""


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



class 				_UpperCamelCase       (						nn.Module  ):

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			nn.Module		)							->			int:




              """simple docstring"""


              super().__init__()

              UpperCamelCase_ 							=	[]
              # - 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}'''
                     UpperCamelCase_ 							=	len(_SCREAMING_SNAKE_CASE		) + 1
                     feature_blocks.append((f'''res{block_index}''', v)		)

              UpperCamelCase_ 							=	nn.ModuleDict(_SCREAMING_SNAKE_CASE		)



       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			Tensor		)							->			List[str]:




              """simple docstring"""


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



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def lowercase			(		self:			Dict		)							->			int:




              """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 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Dict[str, str]:
       UpperCamelCase_ 							=	RegNetConfig(depths=[2, 7, 17, 1]       ,		hidden_sizes=[8, 8, 8, 8]       ,		groups_width=8	)
       if "in1k" in model_name:
              UpperCamelCase_ 							=	RegNetForImageClassification(UpperCamelCase_	)
       else:
              UpperCamelCase_ 							=	RegNetModel(UpperCamelCase_	)
       # create from model (with small weights)
       UpperCamelCase_ 							=	FakeRegNetVisslWrapper(
           RegNet(FakeRegNetParams(depth=27       ,		group_width=1010       ,		w_a=1744       ,		w_a=6_20.83       ,		w_m=2.52	)	)	)

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

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

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

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

       UpperCamelCase_ 							=	{}

       UpperCamelCase_ 							=	to_params_dict(UpperCamelCase_	)
       UpperCamelCase_ 							=	to_params_dict(UpperCamelCase_	)

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

       return from_to_ours_keys




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_ = None       ,		UpperCamelCase_ = True	)  ->		Dict:
       UpperCamelCase_ 							=	"imagenet-1k-id2label.json"
       UpperCamelCase_ 							=	1000

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

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

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

       UpperCamelCase_ 							=	{
           "regnet-y-10b-seer": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]       ,		hidden_sizes=[2020, 4040, 11110, 28280]       ,		groups_width=1010	),
           # finetuned on imagenet
           "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]       ,		hidden_sizes=[2020, 4040, 11110, 28280]       ,		groups_width=1010	),
       }

       # add seer weights logic
       def load_using_classy_vision(UpperCamelCase_	) -> Tuple[Dict, Dict]:
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	files["classy_state_dict"]["base_model"]["model"]
              return model_state_dict["trunk"], model_state_dict["heads"]

       UpperCamelCase_ 							=	{
           "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"       ,		),
       }

       UpperCamelCase_ 							=	get_from_to_our_keys(UpperCamelCase_	)

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

              UpperCamelCase_ 							=	{}

              UpperCamelCase_ 							=	list(from_state_dict.keys()	)
              UpperCamelCase_ 							=	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
                     UpperCamelCase_ 							=	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
                     UpperCamelCase_ 							=	from_to_ours_keys[src_key]
                     # store the parameter with our key
                     UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	names_to_config[model_name]
              UpperCamelCase_ 							=	RegNetModel
              if "in1k" in model_name:
                     UpperCamelCase_ 							=	RegNetForImageClassification
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	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       ,		)
              UpperCamelCase_ 							=	384
              # we can use the convnext one
              UpperCamelCase_ 							=	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__":
     _UpperCAmelCase										=					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.',
     )

     _UpperCAmelCase										=					parser.parse_args()

     _UpperCAmelCase										=					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 typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	"""prophetnet"""
       lowerCAmelCase__    			=	["""past_key_values"""]
       lowerCAmelCase__    			=	{
           """num_attention_heads""": """num_encoder_attention_heads""",
       }






       def __init__( self     ,	a__ = 0.1     ,	a__ = "gelu"     ,	a__ = 3_05_22     ,	a__ = 10_24     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 0.1     ,	a__ = 0.1     ,	a__ = 5_12     ,	a__ = 0.02     ,	a__ = True     ,	a__ = True     ,	a__ = 0     ,	a__ = 2     ,	a__ = 32     ,	a__ = 1_28     ,	a__ = False     ,	a__ = 0.0     ,	a__ = True     ,	a__ = 0     ,	a__ = 1     ,	a__ = 2     ,	**a__     ,	):
              _UpperCAmelCase			     =  vocab_size
              _UpperCAmelCase			     =  hidden_size
              _UpperCAmelCase			     =  encoder_ffn_dim
              _UpperCAmelCase			     =  num_encoder_layers
              _UpperCAmelCase			     =  num_encoder_attention_heads
              _UpperCAmelCase			     =  decoder_ffn_dim
              _UpperCAmelCase			     =  num_decoder_layers
              _UpperCAmelCase			     =  num_decoder_attention_heads
              _UpperCAmelCase			     =  max_position_embeddings
              _UpperCAmelCase			     =  init_std  # Normal(0, this parameter)
              _UpperCAmelCase			     =  activation_function

              # parameters for prophetnet
              _UpperCAmelCase			     =  ngram
              _UpperCAmelCase			     =  num_buckets
              _UpperCAmelCase			     =  relative_max_distance
              _UpperCAmelCase			     =  disable_ngram_loss
              _UpperCAmelCase			     =  eps

              # 3 Types of Dropout
              _UpperCAmelCase			     =  attention_dropout
              _UpperCAmelCase			     =  activation_dropout
              _UpperCAmelCase			     =  dropout

              _UpperCAmelCase			     =  use_cache

              super().__init__(
                  pad_token_id=a__     ,	bos_token_id=a__     ,	eos_token_id=a__     ,	is_encoder_decoder=a__     ,	add_cross_attention=a__     ,	decoder_start_token_id=a__     ,	**a__     ,	)






       @property
       def 				__A			( self  ):
              return self.num_encoder_layers + self.num_decoder_layers






       @num_hidden_layers.setter
       def 				__A			( self     ,	a__  ):
              raise NotImplementedError(
                  'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
                  ' `num_decoder_layers`.'  )






