
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    _SCREAMING_SNAKE_CASE					:
      _A  :						nn.Module
      _A  :						List[nn.Module]      		=       field(default_factory=lowerCAmelCase__	)
      _A  :						list      		=       field(default_factory=lowerCAmelCase__	)
      _A  :						Dict[str, nn.Module]      		=       field(default_factory=lowerCAmelCase__	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			len(list(m.modules()	)	) == 1 or isinstance(__lowerCAmelCase						,		nn.Convad	) or isinstance(__lowerCAmelCase						,		nn.BatchNormad	)
          if has_not_submodules:
              self.traced.append(__lowerCAmelCase	)
              snake_case__  =			m
      def __call__(     self						,		lowerCamelCase	):


          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 A_     (     self	):
          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						,		lowerCamelCase	):
          super().__init__()

          snake_case__  =			[]
          # - 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}"""
              snake_case__  =			len(__lowerCAmelCase	) + 1
              feature_blocks.append((F"""res{block_index}""", v)	)

          snake_case__  =			nn.ModuleDict(__lowerCAmelCase	)



      def A_     (     self						,		lowerCamelCase	):
          return get_trunk_forward_outputs(
              __lowerCAmelCase						,		out_feat_keys=__lowerCAmelCase						,		feature_blocks=self._feature_blocks						,		)





class    _SCREAMING_SNAKE_CASE					( lowerCAmelCase__	):
      def A_     (     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 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			RegNetConfig(depths=[2, 7, 17, 1]      ,						hidden_sizes=[8, 8, 8, 8]      ,						groups_width=8							)
    if "in1k" in model_name:
        snake_case__  =			RegNetForImageClassification(_A							)
    else:
        snake_case__  =			RegNetModel(_A							)
    # create from model (with small weights)
    snake_case__  =			FakeRegNetVisslWrapper(
        RegNet(FakeRegNetParams(depth=27      ,						group_width=1_010      ,						w_a=1_744      ,						w_a=620.83      ,						w_m=2.52							)							)							)

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

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

        pprint(dest_tracker.nameamodule							)
        snake_case__  =			Tracker(_A							)
        snake_case__  =			src_tracker(_A							).parametrized

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

    snake_case__  =			{}

    snake_case__  =			to_params_dict(_A							)
    snake_case__  =			to_params_dict(_A							)

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

    return from_to_ours_keys





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase = None      ,						__lowerCAmelCase = True							):
    snake_case__  =			"imagenet-1k-id2label.json"
    snake_case__  =			1_000

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

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

    snake_case__  =			partial(_A      ,						num_labels=_A      ,						idalabel=_A      ,						labelaid=_A							)

    snake_case__  =			{
        "regnet-y-10b-seer": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]      ,						hidden_sizes=[2_020, 4_040, 11_110, 28_280]      ,						groups_width=1_010							),
        # finetuned on imagenet
        "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
            depths=[2, 7, 17, 1]      ,						hidden_sizes=[2_020, 4_040, 11_110, 28_280]      ,						groups_width=1_010							),
    }

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

    snake_case__  =			{
        "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"      ,						),
    }

    snake_case__  =			get_from_to_our_keys(_A							)

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

        snake_case__  =			{}

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




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__magic_name__           =					{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__           =					[
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

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



