


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()
__magic_name__       					=      logging.get_logger()





@dataclass
class    lowerCAmelCase__				:






						"""simple docstring"""
						__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 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_				,							a_     ):
							lowerCamelCase_			:							int      =       len(list(m.modules()     )     ) == 1 or isinstance(a_				,							nn.Convad     ) or isinstance(a_				,							nn.BatchNormad     )
							if has_not_submodules:
								self.traced.append(a_     )
								lowerCamelCase_			:							List[Any]      =       m




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




						@property
						def 					_UpperCamelCase			(   self     ):
							# check the len of the state_dict keys to see if we have learnable params
							return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()     )     ) > 0}





class    lowerCAmelCase__				(			nn.Module       ):






						"""simple docstring"""




						def __init__(   self				,							a_     ):
							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_			:							Optional[Any]      =       len(a_     ) + 1
								feature_blocks.append((F"""res{block_index}""", v)     )

							lowerCamelCase_			:							Optional[Any]      =       nn.ModuleDict(a_     )




						def 					_UpperCamelCase			(   self				,							a_     ):
							return get_trunk_forward_outputs(
							    a_				,							out_feat_keys=a_				,							feature_blocks=self._feature_blocks				,							)





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""




						def 					_UpperCamelCase			(   self     ):
							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     __magic_name__  (      lowerCAmelCase_):
	'''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_			:							str      =       RegNetForImageClassification(lowerCAmelCase_)
	else:
		lowerCamelCase_			:							Tuple      =       RegNetModel(lowerCAmelCase_)
	# create from model (with small weights)
	lowerCamelCase_			:							Union[str, Any]      =       FakeRegNetVisslWrapper(
	    RegNet(FakeRegNetParams(depth=27      ,					group_width=1010      ,					w_a=1744      ,					w_a=620.83      ,					w_m=2.52)))

	with torch.no_grad():
		lowerCamelCase_			:							Optional[Any]      =       from_model.eval()
		lowerCamelCase_			:							Union[str, Any]      =       our_model.eval()

		lowerCamelCase_			:							int      =       torch.randn((1, 3, 32, 32))
		# trace both
		lowerCamelCase_			:							Tuple      =       Tracker(lowerCAmelCase_)
		lowerCamelCase_			:							List[str]      =       dest_tracker(lowerCAmelCase_).parametrized

		pprint(dest_tracker.nameamodule)
		lowerCamelCase_			:							Optional[int]      =       Tracker(lowerCAmelCase_)
		lowerCamelCase_			:							List[Any]      =       src_tracker(lowerCAmelCase_).parametrized

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

	lowerCamelCase_			:							Any      =       {}

	lowerCamelCase_			:							List[str]      =       to_params_dict(lowerCAmelCase_)
	lowerCamelCase_			:							int      =       to_params_dict(lowerCAmelCase_)

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

	return from_to_ours_keys







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_ = None      ,					lowerCAmelCase_ = True):
	'''simple docstring'''


	lowerCamelCase_			:							Tuple      =       "imagenet-1k-id2label.json"
	lowerCamelCase_			:							Tuple      =       1000

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

	lowerCamelCase_			:							List[str]      =       idalabel
	lowerCamelCase_			:							Optional[Any]      =       {v: k for k, v in idalabel.items()}

	lowerCamelCase_			:							List[Any]      =       partial(lowerCAmelCase_      ,					num_labels=lowerCAmelCase_      ,					idalabel=lowerCAmelCase_      ,					labelaid=lowerCAmelCase_)

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

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

	lowerCamelCase_			:							Optional[Any]      =       get_from_to_our_keys(lowerCAmelCase_)

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

		lowerCamelCase_			:							Optional[int]      =       {}

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

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

		# save our state dict to disk
		torch.save(lowerCAmelCase_      ,					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_			:							Optional[Any]      =       names_to_config[model_name]
		lowerCamelCase_			:							Optional[Any]      =       RegNetModel
		if "in1k" in model_name:
			lowerCamelCase_			:							Any      =       RegNetForImageClassification
		lowerCamelCase_			:							Any      =       our_model_func(lowerCAmelCase_)
		# 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_			:							Union[str, Any]      =       our_model.state_dict().keys()
		PreTrainedModel._load_pretrained_model_low_mem(
		    lowerCAmelCase_      ,					lowerCAmelCase_      ,					[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_			:							Tuple      =       AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"      ,					size=lowerCAmelCase_)
		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__":
				__magic_name__       					=      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.''',
				)

				__magic_name__       					=      parser.parse_args()

				__magic_name__       					=      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__       					=      {'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''LevitFeatureExtractor''']
							__magic_name__       					=      ['''LevitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LevitForImageClassification''',
							    '''LevitForImageClassificationWithTeacher''',
							    '''LevitModel''',
							    '''LevitPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_levit import LevitFeatureExtractor
														from .image_processing_levit import LevitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_levit import (
														    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LevitForImageClassification,
														    LevitForImageClassificationWithTeacher,
														    LevitModel,
														    LevitPreTrainedModel,
														)
else:
							import sys

							__magic_name__       					=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

