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_     :   Union[str, Any] 				=				logging.get_logger()






@dataclass
class 		_lowerCamelCase		:
    __a  =      42
    __a  =      field(default_factory=UpperCamelCase_    )
    __a  =      field(default_factory=UpperCamelCase_    )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Tuple=      len(list(m.modules()   )   ) == 1 or isinstance(lowerCAmelCase , nn.Convad   ) or isinstance(lowerCAmelCase , nn.BatchNormad   )
     if has_not_submodules:
      self.traced.append(lowerCAmelCase   )
    def __call__(  self , lowerCAmelCase   )       ->					List[Any]:
     for m in self.module.modules():
      self.handles.append(m.register_forward_hook(self._forward_hook   )   )
     self.module(lowerCAmelCase   )
     [x.remove() for x in self.handles]
     return self



    @property
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     # check the len of the state_dict keys to see if we have learnable params
     return list(filter(lambda lowerCAmelCase   : len(list(x.state_dict().keys()   )   ) > 0 , self.traced   )   )






@dataclass
class 		_lowerCamelCase		:
    __a  =      42
    __a  =      42
    __a  =      1
    __a  =      field(default_factory=UpperCamelCase_    )
    __a  =      field(default_factory=UpperCamelCase_    )
    __a  =      True
    def __call__(  self , lowerCAmelCase   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   List[str]=      Tracker(self.dest   )(lowerCAmelCase   ).parametrized
     SCREAMING_SNAKE_CASE__:   Any=      Tracker(self.src   )(lowerCAmelCase   ).parametrized

     SCREAMING_SNAKE_CASE__:   Any=      list(filter(lambda lowerCAmelCase   : type(lowerCAmelCase   ) not in self.src_skip , lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   Tuple=      list(filter(lambda lowerCAmelCase   : type(lowerCAmelCase   ) not in self.dest_skip , lowerCAmelCase   )   )

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

     for dest_m, src_m in zip(lowerCAmelCase , lowerCAmelCase   ):
      dest_m.load_state_dict(src_m.state_dict()   )
      if self.verbose == 1:
       print(f'Transfered from={src_m} to={dest_m}'   )






class 		_lowerCamelCase		(   nn.Module    ):
    def __init__(  self , lowerCAmelCase   )       ->					List[Any]:
     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__:   int=      len(lowerCAmelCase   ) + 1
      feature_blocks.append((f'res{block_index}', v)   )

     SCREAMING_SNAKE_CASE__:   Any=      nn.ModuleDict(lowerCAmelCase   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Any:
     return get_trunk_forward_outputs(
         lowerCAmelCase , out_feat_keys=lowerCAmelCase , feature_blocks=self._feature_blocks , )






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[str]=      x.split('''-'''   )
     return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]   )



    def __getitem__(  self , lowerCAmelCase   )       ->					Callable[[], Tuple[nn.Module, Dict]]:
     # default to timm!
     if x not in self:
      SCREAMING_SNAKE_CASE__:   List[Any]=      self.convert_name_to_timm(lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      partial(lambda: (timm.create_model(lowerCAmelCase , pretrained=lowerCAmelCase   ).eval(), None)   )

     else:
      SCREAMING_SNAKE_CASE__:   int=      super().__getitem__(lowerCAmelCase   )

     return val






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __getitem__(  self , lowerCAmelCase   )       ->					Callable[[], nn.Module]:
     if "seer" in x and "in1k" not in x:
      SCREAMING_SNAKE_CASE__:   int=      RegNetModel
     else:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      RegNetForImageClassification
     return val







def 			A__      (	snake_case_ :		List[Any]       ,				snake_case_ :		Any       ,				snake_case_ :		List[Tuple[str, str]]      ):
 for from_key, to_key in keys:
  SCREAMING_SNAKE_CASE__:   Dict=      from_state_dict[from_key].clone()
  print(F'Copied key={from_key} to={to_key}'      )
 return to_state_dict







def 			A__      (	snake_case_ :		str       ,				snake_case_ :		Callable[[], nn.Module]       ,				snake_case_ :		Callable[[], nn.Module]       ,				snake_case_ :		RegNetConfig       ,				snake_case_ :		Path       ,				snake_case_ :		bool = True       ,				):
 print(F'Converting {name}...'      )
 with torch.no_grad():
  SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      from_model_func()
  SCREAMING_SNAKE_CASE__:   Any=      our_model_func(snake_case_      ).eval()
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      ModuleTransfer(src=snake_case_       ,				dest=snake_case_       ,				raise_if_mismatch=snake_case_      )
  SCREAMING_SNAKE_CASE__:   List[str]=      torch.randn((1, 3, 224, 224)      )
  module_transfer(snake_case_      )

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

 SCREAMING_SNAKE_CASE__:   Tuple=      our_model(snake_case_       ,				output_hidden_states=snake_case_      )
 SCREAMING_SNAKE_CASE__:   List[str]=      (
     our_outputs.logits if isinstance(snake_case_       ,				snake_case_      ) else our_outputs.last_hidden_state
 )

 SCREAMING_SNAKE_CASE__:   str=      from_model(snake_case_      )
 SCREAMING_SNAKE_CASE__:   List[str]=      from_output[-1] if type(snake_case_      ) 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__:   Tuple=      our_outputs.hidden_states[-1]

 assert torch.allclose(snake_case_       ,				snake_case_      ), "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=snake_case_       ,				)

  SCREAMING_SNAKE_CASE__:   List[str]=      224 if '''seer''' not in name else 384
  # we can use the convnext one
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       ,				size=snake_case_      )
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / name       ,				commit_message='''Add image processor'''       ,				use_temp_dir=snake_case_       ,				)

  print(F'Pushed {name}'      )







def 			A__      (	snake_case_ :		Path       ,				snake_case_ :		str = None       ,				snake_case_ :		bool = True      ):
 SCREAMING_SNAKE_CASE__:   List[str]=      '''imagenet-1k-id2label.json'''
 SCREAMING_SNAKE_CASE__:   int=      1_000
 SCREAMING_SNAKE_CASE__:   List[Any]=      (1, num_labels)

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

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

 SCREAMING_SNAKE_CASE__:   List[Any]=      partial(snake_case_       ,				num_labels=snake_case_       ,				idalabel=snake_case_       ,				labelaid=snake_case_      )

 SCREAMING_SNAKE_CASE__:   Tuple=      {
     '''regnet-x-002''': ImageNetPreTrainedConfig(
         depths=[1, 1, 4, 7]       ,				hidden_sizes=[24, 56, 152, 368]       ,				groups_width=8       ,				layer_type='''x'''      ),
     '''regnet-x-004''': ImageNetPreTrainedConfig(
         depths=[1, 2, 7, 12]       ,				hidden_sizes=[32, 64, 160, 384]       ,				groups_width=16       ,				layer_type='''x'''      ),
     '''regnet-x-006''': ImageNetPreTrainedConfig(
         depths=[1, 3, 5, 7]       ,				hidden_sizes=[48, 96, 240, 528]       ,				groups_width=24       ,				layer_type='''x'''      ),
     '''regnet-x-008''': ImageNetPreTrainedConfig(
         depths=[1, 3, 7, 5]       ,				hidden_sizes=[64, 128, 288, 672]       ,				groups_width=16       ,				layer_type='''x'''      ),
     '''regnet-x-016''': ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 2]       ,				hidden_sizes=[72, 168, 408, 912]       ,				groups_width=24       ,				layer_type='''x'''      ),
     '''regnet-x-032''': ImageNetPreTrainedConfig(
         depths=[2, 6, 15, 2]       ,				hidden_sizes=[96, 192, 432, 1_008]       ,				groups_width=48       ,				layer_type='''x'''      ),
     '''regnet-x-040''': ImageNetPreTrainedConfig(
         depths=[2, 5, 14, 2]       ,				hidden_sizes=[80, 240, 560, 1_360]       ,				groups_width=40       ,				layer_type='''x'''      ),
     '''regnet-x-064''': ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]       ,				hidden_sizes=[168, 392, 784, 1_624]       ,				groups_width=56       ,				layer_type='''x'''      ),
     '''regnet-x-080''': ImageNetPreTrainedConfig(
         depths=[2, 5, 15, 1]       ,				hidden_sizes=[80, 240, 720, 1_920]       ,				groups_width=120       ,				layer_type='''x'''      ),
     '''regnet-x-120''': ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]       ,				hidden_sizes=[224, 448, 896, 2_240]       ,				groups_width=112       ,				layer_type='''x'''      ),
     '''regnet-x-160''': ImageNetPreTrainedConfig(
         depths=[2, 6, 13, 1]       ,				hidden_sizes=[256, 512, 896, 2_048]       ,				groups_width=128       ,				layer_type='''x'''      ),
     '''regnet-x-320''': ImageNetPreTrainedConfig(
         depths=[2, 7, 13, 1]       ,				hidden_sizes=[336, 672, 1_344, 2_520]       ,				groups_width=168       ,				layer_type='''x'''      ),
     # y variant
     '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]       ,				hidden_sizes=[24, 56, 152, 368]       ,				groups_width=8      ),
     '''regnet-y-004''': ImageNetPreTrainedConfig(
         depths=[1, 3, 6, 6]       ,				hidden_sizes=[48, 104, 208, 440]       ,				groups_width=8      ),
     '''regnet-y-006''': ImageNetPreTrainedConfig(
         depths=[1, 3, 7, 4]       ,				hidden_sizes=[48, 112, 256, 608]       ,				groups_width=16      ),
     '''regnet-y-008''': ImageNetPreTrainedConfig(
         depths=[1, 3, 8, 2]       ,				hidden_sizes=[64, 128, 320, 768]       ,				groups_width=16      ),
     '''regnet-y-016''': ImageNetPreTrainedConfig(
         depths=[2, 6, 17, 2]       ,				hidden_sizes=[48, 120, 336, 888]       ,				groups_width=24      ),
     '''regnet-y-032''': ImageNetPreTrainedConfig(
         depths=[2, 5, 13, 1]       ,				hidden_sizes=[72, 216, 576, 1_512]       ,				groups_width=24      ),
     '''regnet-y-040''': ImageNetPreTrainedConfig(
         depths=[2, 6, 12, 2]       ,				hidden_sizes=[128, 192, 512, 1_088]       ,				groups_width=64      ),
     '''regnet-y-064''': ImageNetPreTrainedConfig(
         depths=[2, 7, 14, 2]       ,				hidden_sizes=[144, 288, 576, 1_296]       ,				groups_width=72      ),
     '''regnet-y-080''': ImageNetPreTrainedConfig(
         depths=[2, 4, 10, 1]       ,				hidden_sizes=[168, 448, 896, 2_016]       ,				groups_width=56      ),
     '''regnet-y-120''': ImageNetPreTrainedConfig(
         depths=[2, 5, 11, 1]       ,				hidden_sizes=[224, 448, 896, 2_240]       ,				groups_width=112      ),
     '''regnet-y-160''': ImageNetPreTrainedConfig(
         depths=[2, 4, 11, 1]       ,				hidden_sizes=[224, 448, 1_232, 3_024]       ,				groups_width=112      ),
     '''regnet-y-320''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]       ,				hidden_sizes=[232, 696, 1_392, 3_712]       ,				groups_width=232      ),
     # models created by SEER -> https://arxiv.org/abs/2202.08360
     '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]       ,				hidden_sizes=[232, 696, 1_392, 3_712]       ,				groups_width=232      ),
     '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]       ,				hidden_sizes=[328, 984, 1_968, 4_920]       ,				groups_width=328      ),
     '''regnet-y-1280-seer''': RegNetConfig(
         depths=[2, 7, 17, 1]       ,				hidden_sizes=[528, 1_056, 2_904, 7_392]       ,				groups_width=264      ),
     '''regnet-y-2560-seer''': RegNetConfig(
         depths=[3, 7, 16, 1]       ,				hidden_sizes=[640, 1_696, 2_544, 5_088]       ,				groups_width=640      ),
     '''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-320-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]       ,				hidden_sizes=[232, 696, 1_392, 3_712]       ,				groups_width=232      ),
     '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 5, 12, 1]       ,				hidden_sizes=[328, 984, 1_968, 4_920]       ,				groups_width=328      ),
     '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[2, 7, 17, 1]       ,				hidden_sizes=[528, 1_056, 2_904, 7_392]       ,				groups_width=264      ),
     '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
         depths=[3, 7, 16, 1]       ,				hidden_sizes=[640, 1_696, 2_544, 5_088]       ,				groups_width=640      ),
     '''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      ),
 }

 SCREAMING_SNAKE_CASE__:   List[str]=      NameToOurModelFuncMap()
 SCREAMING_SNAKE_CASE__:   Tuple=      NameToFromModelFuncMap()
 # add seer weights logic

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

 # pretrained
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      partial(
     snake_case_       ,				'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''       ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )       ,				)

 SCREAMING_SNAKE_CASE__:   Dict=      partial(
     snake_case_       ,				'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''       ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )       ,				)

 SCREAMING_SNAKE_CASE__:   Tuple=      partial(
     snake_case_       ,				'''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__:   Any=      partial(
     snake_case_       ,				'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''       ,				lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27       ,				group_width=1_010       ,				w_a=1_744       ,				w_a=6_20.83       ,				w_m=2.52      )      )      )       ,				)

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

 SCREAMING_SNAKE_CASE__:   Optional[Any]=      partial(
     snake_case_       ,				'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''       ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )       ,				)

 SCREAMING_SNAKE_CASE__:   Any=      partial(
     snake_case_       ,				'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''       ,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )       ,				)

 SCREAMING_SNAKE_CASE__:   int=      partial(
     snake_case_       ,				'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''       ,				lambda: FakeRegNetVisslWrapper(
         RegNet(RegNetParams(depth=27       ,				group_width=1_010       ,				w_a=1_744       ,				w_a=6_20.83       ,				w_m=2.52      )      )      )       ,				)

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


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

      lowercase_     :   Optional[int] 				=				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)


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


lowercase_     :   str 				=				re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 		_lowerCamelCase		:
    __a  =      42
    __a  =      None
    __a  =      None
    __a  =      None
    __a  =      None
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   int=      _str_to_version_tuple(self.version_str   )
    def __repr__(  self   )       ->					Dict:
     return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'
    @property
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     return self.major, self.minor, self.patch
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Dict:
     if isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return Version(lowerCAmelCase   )
     elif isinstance(lowerCAmelCase , lowerCAmelCase   ):
      return other
     raise TypeError(f'{other} (type {type(lowerCAmelCase   )}) cannot be compared to version.'   )
    def __eq__(  self , lowerCAmelCase   )       ->					Optional[int]:
     try:
      SCREAMING_SNAKE_CASE__:   List[str]=      self._validate_operand(lowerCAmelCase   )
     except (TypeError, ValueError):
      return False
     else:
      return self.tuple == other.tuple
    def __lt__(  self , lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self._validate_operand(lowerCAmelCase   )
     return self.tuple < other.tuple
    def __hash__(  self   )       ->					List[Any]:
     return hash(_version_tuple_to_str(self.tuple   )   )
    @classmethod
    def 						UpperCamelCase_				(  cls , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Dict=      {f.name for f in dataclasses.fields(cls   )}
     return cls(**{k: v for k, v in dic.items() if k in field_names}   )



    def 						UpperCamelCase_				(  self   )       ->					str:
     return self.version_str







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   List[Any]=      _VERSION_REG.match(snake_case_      )
 if not res:
  raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'      )
 return tuple(int(snake_case_      ) for v in [res.group('''major'''      ), res.group('''minor'''      ), res.group('''patch'''      )]      )







def 			A__      (	snake_case_ :		Union[str, Any]      ):
 return ".".join(str(snake_case_      ) for v in version_tuple      )

