
"""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      :	str       		=	logging.get_logger()

@dataclass
class 		a__						:
   __lowerCAmelCase						=    42
   __lowerCAmelCase						=    field(default_factory=a_						)
   __lowerCAmelCase						=    field(default_factory=a_						)
   __lowerCAmelCase						=    field(default_factory=a_						)
   def       __magic_name__      (      self     ,   _a     ,   _a     ,   _a     ,   _a    ):
          lowercase   :    int			=							len(list(m.modules()    )    ) == 1 or isinstance(_a     ,   nn.Convad    ) or isinstance(_a     ,   nn.BatchNormad    )
          if has_not_submodules:
                 self.traced.append(_a    )
                 lowercase   :    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       __magic_name__      (      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 		a__						(				nn.Module						):
   def __init__(      self     ,   _a    ):
          super().__init__()

          lowercase   :    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}"""
                 lowercase   :    List[Any]			=							len(_a    ) + 1
                 feature_blocks.append((f"""res{block_index}""", v)    )

          lowercase   :    Tuple			=							nn.ModuleDict(_a    )

   def       __magic_name__      (      self     ,   _a    ):
          return get_trunk_forward_outputs(
              _a     ,   out_feat_keys=_a     ,   feature_blocks=self._feature_blocks     ,   )


class 		a__						(				a_						):
   def       __magic_name__      (      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__     (				__snake_case    :	str  )			->    Dict[str, str]:
       lowercase   :    Any			=							RegNetConfig(depths=[2, 7, 17, 1]					,			hidden_sizes=[8, 8, 8, 8]					,			groups_width=8  )
       if "in1k" in model_name:
              lowercase   :    Tuple			=							RegNetForImageClassification(__snake_case  )
       else:
              lowercase   :    Optional[Any]			=							RegNetModel(__snake_case  )
       # create from model (with small weights)
       lowercase   :    List[str]			=							FakeRegNetVisslWrapper(
           RegNet(FakeRegNetParams(depth=27					,			group_width=1010					,			w_a=1744					,			w_a=6_20.83					,			w_m=2.52  )  )  )

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

              lowercase   :    int			=							torch.randn((1, 3, 32, 32)  )
              # trace both
              lowercase   :    Any			=							Tracker(__snake_case  )
              lowercase   :    Tuple			=							dest_tracker(__snake_case  ).parametrized

              pprint(dest_tracker.nameamodule  )
              lowercase   :    str			=							Tracker(__snake_case  )
              lowercase   :    Any			=							src_tracker(__snake_case  ).parametrized

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

       lowercase   :    List[Any]			=							{}

       lowercase   :    Dict			=							to_params_dict(__snake_case  )
       lowercase   :    Optional[Any]			=							to_params_dict(__snake_case  )

       for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()					,			dst_state_dict.items()  ):
              lowercase   :    Tuple			=							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:
              lowercase   :    List[str]			=							"classifier.1.weight"
              lowercase   :    Optional[int]			=							"classifier.1.bias"

       return from_to_ours_keys


def   __magic_name__     (				__snake_case    :	Path					,			__snake_case    :	str = None					,			__snake_case    :	bool = True  )			->    Dict:
       lowercase   :    Union[str, Any]			=							"imagenet-1k-id2label.json"
       lowercase   :    List[Any]			=							1000

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

       lowercase   :    List[str]			=							idalabel
       lowercase   :    int			=							{v: k for k, v in idalabel.items()}

       lowercase   :    List[str]			=							partial(__snake_case					,			num_labels=__snake_case					,			idalabel=__snake_case					,			labelaid=__snake_case  )

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

       lowercase   :    Any			=							{
           "regnet-y-10b-seer": partial(
               __snake_case					,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"					,			),
           "regnet-y-10b-seer-in1k": partial(
               __snake_case					,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"					,			),
       }

       lowercase   :    Optional[Any]			=							get_from_to_our_keys(__snake_case  )

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

              lowercase   :    str			=							{}

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

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

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

   _A      :	Path       		=	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 os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 		a__						(				a_,      unittest.TestCase						):
   __lowerCAmelCase						=    BertweetTokenizer
   __lowerCAmelCase						=    False
   def       __magic_name__      (      self    ):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowercase   :    Tuple			=							["I", "m", "V@@", "R@@", "r", "e@@"]
          lowercase   :    List[Any]			=							dict(zip(_a     ,   range(len(_a    )    )    )    )
          lowercase   :    Union[str, Any]			=							["#version: 0.2", "a m</w>"]
          lowercase   :    List[Any]			=							{"unk_token": "<unk>"}

          lowercase   :    Optional[Any]			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]    )
          lowercase   :    Any			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["merges_file"]    )
          with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 for token in vocab_tokens:
                        fp.write(f"""{token} {vocab_tokens[token]}\n"""    )
          with open(self.merges_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 fp.write("\n".join(_a    )    )
   def       __magic_name__      (      self     ,   **_a    ):
          kwargs.update(self.special_tokens_map    )
          return BertweetTokenizer.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Union[str, Any]			=							"I am VinAI Research"
          lowercase   :    str			=							"I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
          return input_text, output_text

   def       __magic_name__      (      self    ):
          lowercase   :    List[Any]			=							BertweetTokenizer(self.vocab_file     ,   self.merges_file     ,   **self.special_tokens_map    )
          lowercase   :    int			=							"I am VinAI Research"
          lowercase   :    Dict			=							"I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
          lowercase   :    Tuple			=							tokenizer.tokenize(_a    )
          self.assertListEqual(_a     ,   _a    )

          lowercase   :    Any			=							tokens + [tokenizer.unk_token]

          lowercase   :    List[Any]			=							[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a    )     ,   _a    )


