
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
lowercase_       :						List[Any]     					=	logging.get_logger()
@dataclass
class 			UpperCamelCase      :
     A__      =			42
     A__      =			field(default_factory=__SCREAMING_SNAKE_CASE  )
     A__      =			field(default_factory=__SCREAMING_SNAKE_CASE  )
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = len(list(m.modules()				)				) == 1 or isinstance(snake_case__  ,     nn.Convad				) or isinstance(snake_case__  ,     nn.BatchNormad				)
       if has_not_submodules:
         self.traced.append(snake_case__				)
     def __call__(     self  ,     snake_case__				):




       """simple docstring"""


       for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook				)				)
       self.module(snake_case__				)
       [x.remove() for x in self.handles]
       return self




     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return list(filter(lambda snake_case__				: len(list(x.state_dict().keys()				)				) > 0  ,     self.traced				)				)
@dataclass
class 			UpperCamelCase      :
     A__      =			42
     A__      =			42
     A__      =			1
     A__      =			field(default_factory=__SCREAMING_SNAKE_CASE  )
     A__      =			field(default_factory=__SCREAMING_SNAKE_CASE  )
     A__      =			True
     def __call__(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = Tracker(self.dest				)(snake_case__				).parametrized
       _SCREAMING_SNAKE_CASE   :				Any							    = Tracker(self.src				)(snake_case__				).parametrized

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = list(filter(lambda snake_case__				: type(snake_case__				) not in self.src_skip  ,     snake_case__				)				)
       _SCREAMING_SNAKE_CASE   :				int							    = list(filter(lambda snake_case__				: type(snake_case__				) not in self.dest_skip  ,     snake_case__				)				)

       if len(snake_case__				) != len(snake_case__				) and self.raise_if_mismatch:
         raise Exception(
             F'''Numbers of operations are different. Source module has {len(snake_case__				)} operations while'''
             F''' destination module has {len(snake_case__				)}.'''				)

       for dest_m, src_m in zip(snake_case__  ,     snake_case__				):
         dest_m.load_state_dict(src_m.state_dict()				)
         if self.verbose == 1:
           print(F'''Transfered from={src_m} to={dest_m}'''				)
class 			UpperCamelCase      (							nn.Module  ):
     def __init__(     self  ,     snake_case__				):




       """simple docstring"""


       super().__init__()

       _SCREAMING_SNAKE_CASE   :				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}'''
         _SCREAMING_SNAKE_CASE   :				List[str]							    = len(snake_case__				) + 1
         feature_blocks.append((F'''res{block_index}''', v)				)

       _SCREAMING_SNAKE_CASE   :				Any							    = nn.ModuleDict(snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       return get_trunk_forward_outputs(
           snake_case__  ,     out_feat_keys=snake_case__  ,     feature_blocks=self._feature_blocks  ,     )
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = x.split("-"				)
       return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)




     def __getitem__(     self  ,     snake_case__				):




       """simple docstring"""


       if x not in self:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = self.convert_name_to_timm(snake_case__				)
         _SCREAMING_SNAKE_CASE   :				str							    = partial(lambda: (timm.create_model(snake_case__  ,     pretrained=snake_case__				).eval(), None)				)

       else:
         _SCREAMING_SNAKE_CASE   :				str							    = super().__getitem__(snake_case__				)

       return val
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __getitem__(     self  ,     snake_case__				):




       """simple docstring"""


       if "seer" in x and "in1k" not in x:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = RegNetModel
       else:
         _SCREAMING_SNAKE_CASE   :				str							    = RegNetForImageClassification
       return val





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							List[Tuple[str, str]]				)  ->    str:
  for from_key, to_key in keys:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = from_state_dict[from_key].clone()
    print(f'''Copied key={from_key} to={to_key}'''				)
  return to_state_dict





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							Callable[[], nn.Module],						lowerCamelCase__							:							Callable[[], nn.Module],						lowerCamelCase__							:							RegNetConfig,						lowerCamelCase__							:							Path,						lowerCamelCase__							:							bool = True,						)  ->    Optional[int]:
  print(f'''Converting {name}...'''				)
  with torch.no_grad():
    _SCREAMING_SNAKE_CASE   :				int							    = from_model_func()
    _SCREAMING_SNAKE_CASE   :				int							    = our_model_func(lowerCamelCase__				).eval()
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = ModuleTransfer(src=lowerCamelCase__,						dest=lowerCamelCase__,						raise_if_mismatch=lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = torch.randn((1, 3, 2_2_4, 2_2_4)				)
    module_transfer(lowerCamelCase__				)

  if from_state_dict is not None:
    _SCREAMING_SNAKE_CASE   :				str							    = []
    # for seer - in1k finetuned we have to manually copy the head
    if "seer" in name and "in1k" in name:
      _SCREAMING_SNAKE_CASE   :				Tuple							    = [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = manually_copy_vissl_head(lowerCamelCase__,						our_model.state_dict(),						lowerCamelCase__				)
    our_model.load_state_dict(lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Tuple							    = our_model(lowerCamelCase__,						output_hidden_states=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = (
      our_outputs.logits if isinstance(lowerCamelCase__,						lowerCamelCase__				) else our_outputs.last_hidden_state
  )

  _SCREAMING_SNAKE_CASE   :				Dict							    = from_model(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = from_output[-1] if type(lowerCamelCase__				) is list else from_output

  # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
  if "seer" in name and "in1k" in name:
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = our_outputs.hidden_states[-1]

  assert torch.allclose(lowerCamelCase__,						lowerCamelCase__				), "The model logits don't match the original one."

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / name,						commit_message="Add model",						use_temp_dir=lowerCamelCase__,						)

    _SCREAMING_SNAKE_CASE   :				str							    = 2_2_4 if "seer" not in name else 3_8_4
    # we can use the convnext one
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k",						size=lowerCamelCase__				)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / name,						commit_message="Add image processor",						use_temp_dir=lowerCamelCase__,						)

    print(f'''Pushed {name}'''				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Path,						lowerCamelCase__							:							str = None,						lowerCamelCase__							:							bool = True				)  ->    str:
  _SCREAMING_SNAKE_CASE   :				Dict							    = "imagenet-1k-id2label.json"
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 1_0_0_0
  _SCREAMING_SNAKE_CASE   :				List[str]							    = (1, num_labels)

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

  _SCREAMING_SNAKE_CASE   :				int							    = idalabel
  _SCREAMING_SNAKE_CASE   :				Tuple							    = {v: k for k, v in idalabel.items()}

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = partial(lowerCamelCase__,						num_labels=lowerCamelCase__,						idalabel=lowerCamelCase__,						labelaid=lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = {
      "regnet-x-002": ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7],						hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8],						groups_width=8,						layer_type="x"				),
      "regnet-x-004": ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 1_2],						hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4],						groups_width=1_6,						layer_type="x"				),
      "regnet-x-006": ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7],						hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8],						groups_width=2_4,						layer_type="x"				),
      "regnet-x-008": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5],						hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2],						groups_width=1_6,						layer_type="x"				),
      "regnet-x-016": ImageNetPreTrainedConfig(
          depths=[2, 4, 1_0, 2],						hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2],						groups_width=2_4,						layer_type="x"				),
      "regnet-x-032": ImageNetPreTrainedConfig(
          depths=[2, 6, 1_5, 2],						hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8],						groups_width=4_8,						layer_type="x"				),
      "regnet-x-040": ImageNetPreTrainedConfig(
          depths=[2, 5, 1_4, 2],						hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0],						groups_width=4_0,						layer_type="x"				),
      "regnet-x-064": ImageNetPreTrainedConfig(
          depths=[2, 4, 1_0, 1],						hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4],						groups_width=5_6,						layer_type="x"				),
      "regnet-x-080": ImageNetPreTrainedConfig(
          depths=[2, 5, 1_5, 1],						hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0],						groups_width=1_2_0,						layer_type="x"				),
      "regnet-x-120": ImageNetPreTrainedConfig(
          depths=[2, 5, 1_1, 1],						hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0],						groups_width=1_1_2,						layer_type="x"				),
      "regnet-x-160": ImageNetPreTrainedConfig(
          depths=[2, 6, 1_3, 1],						hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8],						groups_width=1_2_8,						layer_type="x"				),
      "regnet-x-320": ImageNetPreTrainedConfig(
          depths=[2, 7, 1_3, 1],						hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0],						groups_width=1_6_8,						layer_type="x"				),
      # y variant
      "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7],						hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8],						groups_width=8				),
      "regnet-y-004": ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6],						hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0],						groups_width=8				),
      "regnet-y-006": ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4],						hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8],						groups_width=1_6				),
      "regnet-y-008": ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2],						hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8],						groups_width=1_6				),
      "regnet-y-016": ImageNetPreTrainedConfig(
          depths=[2, 6, 1_7, 2],						hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8],						groups_width=2_4				),
      "regnet-y-032": ImageNetPreTrainedConfig(
          depths=[2, 5, 1_3, 1],						hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2],						groups_width=2_4				),
      "regnet-y-040": ImageNetPreTrainedConfig(
          depths=[2, 6, 1_2, 2],						hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8],						groups_width=6_4				),
      "regnet-y-064": ImageNetPreTrainedConfig(
          depths=[2, 7, 1_4, 2],						hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6],						groups_width=7_2				),
      "regnet-y-080": ImageNetPreTrainedConfig(
          depths=[2, 4, 1_0, 1],						hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6],						groups_width=5_6				),
      "regnet-y-120": ImageNetPreTrainedConfig(
          depths=[2, 5, 1_1, 1],						hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0],						groups_width=1_1_2				),
      "regnet-y-160": ImageNetPreTrainedConfig(
          depths=[2, 4, 1_1, 1],						hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4],						groups_width=1_1_2				),
      "regnet-y-320": ImageNetPreTrainedConfig(
          depths=[2, 5, 1_2, 1],						hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2],						groups_width=2_3_2				),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 1_2, 1],						hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2],						groups_width=2_3_2				),
      "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 1_2, 1],						hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0],						groups_width=3_2_8				),
      "regnet-y-1280-seer": RegNetConfig(
          depths=[2, 7, 1_7, 1],						hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2],						groups_width=2_6_4				),
      "regnet-y-2560-seer": RegNetConfig(
          depths=[3, 7, 1_6, 1],						hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8],						groups_width=6_4_0				),
      "regnet-y-10b-seer": ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1],						hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],						groups_width=1_0_1_0				),
      # finetuned on imagenet
      "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 5, 1_2, 1],						hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2],						groups_width=2_3_2				),
      "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 5, 1_2, 1],						hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0],						groups_width=3_2_8				),
      "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1],						hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2],						groups_width=2_6_4				),
      "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
          depths=[3, 7, 1_6, 1],						hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8],						groups_width=6_4_0				),
      "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 1_7, 1],						hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],						groups_width=1_0_1_0				),
  }

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = NameToOurModelFuncMap()
  _SCREAMING_SNAKE_CASE   :				Any							    = NameToFromModelFuncMap()
  # add seer weights logic

  def load_using_classy_vision(lowerCamelCase__							:							str,						lowerCamelCase__							:							Callable[[], nn.Module]				) -> Tuple[nn.Module, Dict]:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = torch.hub.load_state_dict_from_url(lowerCamelCase__,						model_dir=str(lowerCamelCase__				),						map_location="cpu"				)
    _SCREAMING_SNAKE_CASE   :				str							    = model_func()
    # check if we have a head, if yes add it
    _SCREAMING_SNAKE_CASE   :				Tuple							    = files["classy_state_dict"]["base_model"]["model"]
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = model_state_dict["trunk"]
    model.load_state_dict(lowerCamelCase__				)
    return model.eval(), model_state_dict["heads"]

  # pretrained
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = partial(
      lowerCamelCase__,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch",						lambda: FakeRegNetVisslWrapper(RegNetYaagf()				),						)

  _SCREAMING_SNAKE_CASE   :				Dict							    = partial(
      lowerCamelCase__,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch",						lambda: FakeRegNetVisslWrapper(RegNetYaagf()				),						)

  _SCREAMING_SNAKE_CASE   :				str							    = partial(
      lowerCamelCase__,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch",						lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				),						)

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = partial(
      lowerCamelCase__,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",						lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=2_7,						group_width=1_0_1_0,						w_a=1_7_4_4,						w_a=620.83,						w_m=2.52				)				)				),						)

  # IN1K finetuned
  _SCREAMING_SNAKE_CASE   :				Tuple							    = partial(
      lowerCamelCase__,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch",						lambda: FakeRegNetVisslWrapper(RegNetYaagf()				),						)

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = partial(
      lowerCamelCase__,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch",						lambda: FakeRegNetVisslWrapper(RegNetYaagf()				),						)

  _SCREAMING_SNAKE_CASE   :				str							    = partial(
      lowerCamelCase__,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch",						lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				),						)

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = partial(
      lowerCamelCase__,						"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",						lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=2_7,						group_width=1_0_1_0,						w_a=1_7_4_4,						w_a=620.83,						w_m=2.52				)				)				),						)

  if model_name:
    convert_weight_and_push(
        lowerCamelCase__,						names_to_from_model_map[model_name],						names_to_ours_model_map[model_name],						names_to_config[model_name],						lowerCamelCase__,						lowerCamelCase__,						)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          lowerCamelCase__,						names_to_from_model_map[model_name],						names_to_ours_model_map[model_name],						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						)
  return config, expected_shape


if __name__ == "__main__":
     lowercase_       :						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.''',
     )

     lowercase_       :						Dict     					=	parser.parse_args()

     lowercase_       :						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"""
from typing import List

from .keymap import KEYMAP, get_character





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Optional[int]:

  def decorator(lowerCamelCase__							:							int				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += [key]
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator





def    _lowerCAmelCase    (     *lowerCamelCase__							:							List[str]				)  ->    Tuple:

  def decorator(lowerCamelCase__							:							Dict				):
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = getattr(lowerCamelCase__,						"handle_key",						[]				)
    handle += keys
    setattr(lowerCamelCase__,						"handle_key",						lowerCamelCase__				)
    return func

  return decorator
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def __new__(     cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = super().__new__(cls  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
       if not hasattr(snake_case__  ,     "key_handler"				):
         setattr(snake_case__  ,     "key_handler"  ,     {}				)
       setattr(snake_case__  ,     "handle_input"  ,     KeyHandler.handle_input				)

       for value in attrs.values():
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = getattr(snake_case__  ,     "handle_key"  ,     []				)
         for key in handled_keys:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = value
       return new_cls




     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = get_character()
       if char != KEYMAP["undefined"]:
         _SCREAMING_SNAKE_CASE   :				Dict							    = ord(snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = cls.key_handler.get(snake_case__				)
       if handler:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = char
         return handler(cls				)
       else:
         return None





def    _lowerCAmelCase    (     cls							:							List[Any]				)  ->    str:
  return KeyHandler(cls.__name__,						cls.__bases__,						cls.__dict__.copy()				)



