


'''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()
a__    :   Any								=			logging.get_logger()
@dataclass
class    lowercase_	:
    __UpperCAmelCase       =      42
    __UpperCAmelCase       =      field(default_factory=__A  )
    __UpperCAmelCase       =      field(default_factory=__A  )
    __UpperCAmelCase       =      field(default_factory=__A  )


    def   __a				(	self    ,   a    ,   a    ,   a    ,   a       ):
          UpperCamelCase__      =							len(list(m.modules()       )       ) == 1 or isinstance(__UpperCAmelCase    ,   nn.Convad       ) or isinstance(__UpperCAmelCase    ,   nn.BatchNormad       )
          if has_not_submodules:
                self.traced.append(__UpperCAmelCase       )
                UpperCamelCase__      =							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=__UpperCAmelCase       )       )       )
          self.module(__UpperCAmelCase       )
          [x.remove() for x in self.handles]
          return self


    @property
    def   __a				(	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    lowercase_	(       nn.Module  ):


    def __init__(	self    ,   a       ):
          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(__UpperCAmelCase       ) + 1
                feature_blocks.append((f'''res{block_index}''', v)       )

          UpperCamelCase__      =							nn.ModuleDict(__UpperCAmelCase       )


    def   __a				(	self    ,   a       ):
          return get_trunk_forward_outputs(
              __UpperCAmelCase    ,   out_feat_keys=__UpperCAmelCase    ,   feature_blocks=self._feature_blocks    ,   )

class    lowercase_	(       __A  ):


    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 _UpperCamelCase			(	__A       )				->						Union[str, Any]:


      '''simple docstring'''



      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=620.83 ,     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(__A       ):
            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 _UpperCamelCase			(	__A ,     __A = None ,     __A = True       )				->						List[str]:


      '''simple docstring'''



      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(__A       ) -> 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__":
  a__    :   Dict								=			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.',
  )

  a__    :   int								=			parser.parse_args()

  a__    :   List[str]								=			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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase									=	{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	["LayoutLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "LayoutLMForMaskedLM",
				    "LayoutLMForSequenceClassification",
				    "LayoutLMForTokenClassification",
				    "LayoutLMForQuestionAnswering",
				    "LayoutLMModel",
				    "LayoutLMPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase									=	[
				    "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFLayoutLMForMaskedLM",
				    "TFLayoutLMForSequenceClassification",
				    "TFLayoutLMForTokenClassification",
				    "TFLayoutLMForQuestionAnswering",
				    "TFLayoutLMMainLayer",
				    "TFLayoutLMModel",
				    "TFLayoutLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
				from .tokenization_layoutlm import LayoutLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlm import (
								    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMForMaskedLM,
								    LayoutLMForQuestionAnswering,
								    LayoutLMForSequenceClassification,
								    LayoutLMForTokenClassification,
								    LayoutLMModel,
								    LayoutLMPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlm import (
								    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMForMaskedLM,
								    TFLayoutLMForQuestionAnswering,
								    TFLayoutLMForSequenceClassification,
								    TFLayoutLMForTokenClassification,
								    TFLayoutLMMainLayer,
								    TFLayoutLMModel,
								    TFLayoutLMPreTrainedModel,
								)

else:
				import sys

				__lowerCamelCase									=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





