




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 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = field(default_factory=lowerCamelCase  )
					_lowerCamelCase		  = field(default_factory=lowerCamelCase  )
					_lowerCamelCase		  = field(default_factory=lowerCamelCase  )

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       __lowercase ,       __lowercase					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							len(list(m.modules()					)					) == 1 or isinstance(__lowercase ,       nn.Convad					) or isinstance(__lowercase ,       nn.BatchNormad					)
						if has_not_submodules:
							self.traced.append(__lowercase					)
							lowerCamelCase_							:		Tuple							   =							m

					def __call__(				self ,       __lowercase					):







						'''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					):







						'''simple docstring'''




						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 ,       __lowercase					):







						'''simple docstring'''




						super().__init__()

						lowerCamelCase_							:		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}"
							lowerCamelCase_							:		str							   =							len(__lowercase					) + 1
							feature_blocks.append((f"res{block_index}", v)					)

						lowerCamelCase_							:		List[str]							   =							nn.ModuleDict(__lowercase					)





					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




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


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):

					def 	lowercase_							(				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 					snake_case					(							A			)							->							Dict[str, str]:




	'''simple docstring'''


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

	with torch.no_grad():
		lowerCamelCase_							:		str							   =							from_model.eval()
		lowerCamelCase_							:		Tuple							   =							our_model.eval()

		lowerCamelCase_							:		Any							   =							torch.randn((1, 3, 32, 32)			)
		# trace both
		lowerCamelCase_							:		str							   =							Tracker(A			)
		lowerCamelCase_							:		int							   =							dest_tracker(A			).parametrized

		pprint(dest_tracker.nameamodule			)
		lowerCamelCase_							:		List[str]							   =							Tracker(A			)
		lowerCamelCase_							:		List[str]							   =							src_tracker(A			).parametrized

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

	lowerCamelCase_							:		Optional[Any]							   =							{}

	lowerCamelCase_							:		str							   =							to_params_dict(A			)
	lowerCamelCase_							:		List[str]							   =							to_params_dict(A			)

	for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()						,   dst_state_dict.items()			):
		lowerCamelCase_							:		List[str]							   =							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_							:		Tuple							   =							'classifier.1.weight'
		lowerCamelCase_							:		str							   =							'classifier.1.bias'

	return from_to_ours_keys






def 					snake_case					(							A						,   A = None						,   A = True			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		str							   =							'imagenet-1k-id2label.json'
	lowerCamelCase_							:		List[Any]							   =							1000

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

	lowerCamelCase_							:		str							   =							idalabel
	lowerCamelCase_							:		Tuple							   =							{v: k for k, v in idalabel.items()}

	lowerCamelCase_							:		int							   =							partial(A						,   num_labels=A						,   idalabel=A						,   labelaid=A			)

	lowerCamelCase_							:		Any							   =							{
	    '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(A			) -> Tuple[Dict, Dict]:
		lowerCamelCase_							:		Dict							   =							torch.hub.load_state_dict_from_url(A						,   model_dir=str(A			)						,   map_location='cpu'			)
		# check if we have a head, if yes add it
		lowerCamelCase_							:		List[Any]							   =							files['classy_state_dict']['base_model']['model']
		return model_state_dict["trunk"], model_state_dict["heads"]

	lowerCamelCase_							:		Union[str, Any]							   =							{
	    'regnet-y-10b-seer': partial(
	        A						,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'						,   ),
	    'regnet-y-10b-seer-in1k': partial(
	        A						,   'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'						,   ),
	}

	lowerCamelCase_							:		Optional[int]							   =							get_from_to_our_keys(A			)

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

		lowerCamelCase_							:		Dict							   =							{}

		lowerCamelCase_							:		Optional[Any]							   =							list(from_state_dict.keys()			)
		lowerCamelCase_							:		List[str]							   =							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_							:		Dict							   =							re.sub(A						,   ''						,   A			)
			# 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_							:		str							   =							from_to_ours_keys[src_key]
			# store the parameter with our key
			lowerCamelCase_							:		int							   =							from_state_dict[key]
			not_used_keys.remove(A			)
		# check that all keys have been updated
		assert len(A			) == 0, f"Some keys where not used {','.join(A			)}"

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

		# save our state dict to disk
		torch.save(A						,   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_							:		Union[str, Any]							   =							names_to_config[model_name]
		lowerCamelCase_							:		Union[str, Any]							   =							RegNetModel
		if "in1k" in model_name:
			lowerCamelCase_							:		Optional[int]							   =							RegNetForImageClassification
		lowerCamelCase_							:		Dict							   =							our_model_func(A			)
		# 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_							:		Dict							   =							our_model.state_dict().keys()
		PreTrainedModel._load_pretrained_model_low_mem(
		    A						,   A						,   [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_							:		Optional[int]							   =							384
		# we can use the convnext one
		lowerCamelCase_							:		int							   =							AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'						,   size=A			)
		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)



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


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="nystromformer"







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    List[Any]=3_00_00       ,							lowerCAmelCase :    Union[str, Any]=7_68       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    Tuple=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    Any=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    Tuple=5_10       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    Any=64       ,							lowerCAmelCase :    Dict=64       ,							lowerCAmelCase :    List[str]=65       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    int=0.02       ,							lowerCAmelCase :    int=1e-5       ,							lowerCAmelCase :    Dict=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    List[Any]=2       ,							**lowerCAmelCase :    int       ,							)							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						vocab_size
     __lowerCAmelCase  :			Any					    =						max_position_embeddings
     __lowerCAmelCase  :			List[Any]					    =						hidden_size
     __lowerCAmelCase  :			Dict					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			int					    =						intermediate_size
     __lowerCAmelCase  :			str					    =						hidden_act
     __lowerCAmelCase  :			Any					    =						hidden_dropout_prob
     __lowerCAmelCase  :			str					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						initializer_range
     __lowerCAmelCase  :			List[str]					    =						type_vocab_size
     __lowerCAmelCase  :			Union[str, Any]					    =						segment_means_seq_len
     __lowerCAmelCase  :			Tuple					    =						num_landmarks
     __lowerCAmelCase  :			Dict					    =						conv_kernel_size
     __lowerCAmelCase  :			Tuple					    =						inv_coeff_init_option
     __lowerCAmelCase  :			List[Any]					    =						layer_norm_eps
     super().__init__(pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )


