





"""simple docstring"""




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 					_a		:
				UpperCamelCase								=       42
				UpperCamelCase								=       field(default_factory=_lowerCAmelCase							)
				UpperCamelCase								=       field(default_factory=_lowerCAmelCase							)
				UpperCamelCase								=       field(default_factory=_lowerCAmelCase							)






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Tensor,					lowerCAmelCase__						:       Tensor,					lowerCAmelCase__						:       str				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       len(list(m.modules()				)				) == 1 or isinstance(lowerCAmelCase__,					nn.Convad				) or isinstance(lowerCAmelCase__,					nn.BatchNormad				)
										if has_not_submodules:
																self.traced.append(lowerCAmelCase__				)
																_UpperCamelCase     :   Union[str, Any]      =       m






				def __call__(					self						:       Tuple,					lowerCAmelCase__						:       Tensor				)      -> Dict:




										'''simple docstring'''



										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						:       Union[str, Any]				)      -> Tuple:




										'''simple docstring'''



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




class 					_a		( nn.Module							):






				def __init__(					self						:       str,					lowerCAmelCase__						:       nn.Module				)      -> str:




										'''simple docstring'''



										super().__init__()

										_UpperCamelCase     :   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}"""
																_UpperCamelCase     :   Tuple      =       len(lowerCAmelCase__				) + 1
																feature_blocks.append((f"""res{block_index}""", v)				)

										_UpperCamelCase     :   str      =       nn.ModuleDict(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Tensor				)      -> int:




										'''simple docstring'''



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




class 					_a		( _lowerCAmelCase							):






				def 							snake_case      (					self						:       Union[str, Any]				)      -> List[Any]:




										'''simple docstring'''



										return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]





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

						with torch.no_grad():
												_UpperCamelCase     :   str      =       from_model.eval()
												_UpperCamelCase     :   Optional[int]      =       our_model.eval()

												_UpperCamelCase     :   Union[str, Any]      =       torch.randn((1, 3, 32, 32)  )
												# trace both
												_UpperCamelCase     :   List[str]      =       Tracker(_lowercase  )
												_UpperCamelCase     :   Optional[Any]      =       dest_tracker(_lowercase  ).parametrized

												pprint(dest_tracker.nameamodule  )
												_UpperCamelCase     :   Union[str, Any]      =       Tracker(_lowercase  )
												_UpperCamelCase     :   str      =       src_tracker(_lowercase  ).parametrized

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

						_UpperCamelCase     :   Optional[int]      =       {}

						_UpperCamelCase     :   str      =       to_params_dict(_lowercase  )
						_UpperCamelCase     :   Dict      =       to_params_dict(_lowercase  )

						for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()			,		dst_state_dict.items()  ):
												_UpperCamelCase     :   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:
												_UpperCamelCase     :   Union[str, Any]      =       '''classifier.1.weight'''
												_UpperCamelCase     :   Optional[Any]      =       '''classifier.1.bias'''

						return from_to_ours_keys





def 			a_							(				_lowercase			,		_lowercase = None			,		_lowercase = True  ):
						_UpperCamelCase     :   str      =       '''imagenet-1k-id2label.json'''
						_UpperCamelCase     :   int      =       1000

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

						_UpperCamelCase     :   Dict      =       idalabel
						_UpperCamelCase     :   Any      =       {v: k for k, v in idalabel.items()}

						_UpperCamelCase     :   Union[str, Any]      =       partial(_lowercase			,		num_labels=_lowercase			,		idalabel=_lowercase			,		labelaid=_lowercase  )

						_UpperCamelCase     :   int      =       {
						    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,		hidden_sizes=[2020, 4040, 1_1110, 2_8280]			,		groups_width=1010  ),
						    # finetuned on imagenet
						    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]			,		hidden_sizes=[2020, 4040, 1_1110, 2_8280]			,		groups_width=1010  ),
						}

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

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

						_UpperCamelCase     :   Tuple      =       get_from_to_our_keys(_lowercase  )

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

												_UpperCamelCase     :   Tuple      =       {}

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

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

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




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_attn_layer.in_proj_weight.chunk(3			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Dict      =       pt_attn_layer.in_proj_bias.chunk(3			,		dim=0  )

						_UpperCamelCase     :   int      =       pt_attn_layer.out_proj.weight
						_UpperCamelCase     :   List[Any]      =       pt_attn_layer.out_proj.bias

						_UpperCamelCase     :   Any      =       q_proj
						_UpperCamelCase     :   str      =       q_proj_bias

						_UpperCamelCase     :   str      =       k_proj
						_UpperCamelCase     :   List[str]      =       k_proj_bias

						_UpperCamelCase     :   Dict      =       v_proj
						_UpperCamelCase     :   Dict      =       v_proj_bias

						_UpperCamelCase     :   Dict      =       out_proj_weights
						_UpperCamelCase     :   Optional[Any]      =       out_proj_bias





def 			a_							(				_lowercase			,		_lowercase  ):
						copy_linear(hf_mlp.fca			,		pt_mlp.c_fc  )
						copy_linear(hf_mlp.fca			,		pt_mlp.c_proj  )





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       pt_linear.weight
						_UpperCamelCase     :   List[str]      =       pt_linear.bias





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy layer norms
						copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a  )
						copy_linear(hf_layer.layer_norma			,		pt_layer.ln_a  )

						# copy MLP
						copy_mlp(hf_layer.mlp			,		pt_layer.mlp  )

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,		pt_layer.attn  )





def 			a_							(				_lowercase			,		_lowercase  ):
						for hf_layer, pt_layer in zip(_lowercase			,		_lowercase  ):
												copy_layer(_lowercase			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy  embeds
						_UpperCamelCase     :   Tuple      =       pt_model.token_embedding.weight
						_UpperCamelCase     :   List[Any]      =       pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,		pt_model.ln_final  )

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,		pt_model.transformer.resblocks  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   str      =       pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,		_lowercase  )





def 			a_							(				_lowercase			,		_lowercase  ):
						# copy projection
						_UpperCamelCase     :   Tuple      =       pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm			,		pt_model.visual.ln_pre  )
						copy_linear(hf_model.vision_model.post_layernorm			,		pt_model.visual.ln_post  )

						# copy embeds
						_UpperCamelCase     :   str      =       pt_model.visual.conva.weight.data
						_UpperCamelCase     :   Dict      =       pt_model.visual.class_embedding
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,		pt_model.visual.transformer.resblocks  )





@torch.no_grad()
def 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None  ):
						if config_path is not None:
												_UpperCamelCase     :   List[Any]      =       CLIPConfig.from_pretrained(_lowercase  )
						else:
												_UpperCamelCase     :   Optional[int]      =       CLIPConfig(projection_dim=512			,		text_config={}			,		vision_config={}  )

						_UpperCamelCase     :   str      =       CLIPModel(_lowercase  ).eval()

						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[int]      =       load(_lowercase			,		device='''cpu'''			,		jit=_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       pt_model.eval()

						copy_text_model_and_projection(_lowercase			,		_lowercase  )
						copy_vison_model_and_projection(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   int      =       pt_model.logit_scale

						_UpperCamelCase     :   List[Any]      =       torch.arange(0			,		77  ).unsqueeze(0  )
						_UpperCamelCase     :   Optional[int]      =       torch.randn(1			,		3			,		224			,		224  )

						_UpperCamelCase     :   Tuple      =       hf_model(input_ids=_lowercase			,		pixel_values=_lowercase			,		return_dict=_lowercase  )
						_UpperCamelCase     :   Dict      =       hf_outputs.logits_per_image
						_UpperCamelCase     :   Any      =       hf_outputs.logits_per_text
						_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       pt_model(_lowercase			,		_lowercase  )

						assert torch.allclose(_lowercase			,		_lowercase			,		atol=1E-3  )
						assert torch.allclose(_lowercase			,		_lowercase			,		atol=1E-3  )

						hf_model.save_pretrained(_lowercase  )


if __name__ == "__main__":
						UpperCamelCase_	 =argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						UpperCamelCase_	 =parser.parse_args()

						convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



