


'''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()
__A		=logging.get_logger()


@dataclass
class 						_snake_case    :
					lowerCAmelCase					:nn.Module
					lowerCAmelCase					:List[nn.Module]            =    field(default_factory=lowerCamelCase_		)
					lowerCAmelCase					:list            =    field(default_factory=lowerCamelCase_		)




					def  snake_case__  (					self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__     :    Optional[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							,							_lowerCamelCase):
								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  snake_case__  (					self):
								# 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 						_snake_case    :
					lowerCAmelCase					:nn.Module
					lowerCAmelCase					:nn.Module
					lowerCAmelCase					:int            =    1
					lowerCAmelCase					:List            =    field(default_factory=lowerCamelCase_		)
					lowerCAmelCase					:List            =    field(default_factory=lowerCamelCase_		)
					lowerCAmelCase					:bool            =    True




					def __call__(					self							,							_lowerCamelCase):
								UpperCAmelCase__     :    Optional[int]         =   Tracker(self.dest)(__snake_case).parametrized
								UpperCAmelCase__     :    Optional[int]         =   Tracker(self.src)(__snake_case).parametrized

								UpperCAmelCase__     :    Any         =   list(filter(lambda _lowerCamelCase: type(__snake_case) not in self.src_skip							,							__snake_case))
								UpperCAmelCase__     :    Tuple         =   list(filter(lambda _lowerCamelCase: 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 						_snake_case    (   nn.Module		):




					def __init__(					self							,							_lowerCamelCase):
								super().__init__()

								UpperCAmelCase__     :    Tuple         =   []
								# - 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__     :    Optional[Any]         =   len(__snake_case) + 1
											feature_blocks.append((f'''res{block_index}''', v))

								UpperCAmelCase__     :    Union[str, Any]         =   nn.ModuleDict(__snake_case)




					def  snake_case__  (					self							,							_lowerCamelCase):
								return get_trunk_forward_outputs(
								    __snake_case							,							out_feat_keys=__snake_case							,							feature_blocks=self._feature_blocks							,							)


class 						_snake_case    (   lowerCamelCase_		):




					def  snake_case__  (					self							,							_lowerCamelCase):
								UpperCAmelCase__     :    List[str]         =   x.split("""-""")
								return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])




					def __getitem__(					self							,							_lowerCamelCase):
								# default to timm!
								if x not in self:
											UpperCAmelCase__     :    Optional[Any]         =   self.convert_name_to_timm(__snake_case)
											UpperCAmelCase__     :    Dict         =   partial(lambda: (timm.create_model(__snake_case							,							pretrained=__snake_case).eval(), None))

								else:
											UpperCAmelCase__     :    str         =   super().__getitem__(__snake_case)

								return val


class 						_snake_case    (   lowerCamelCase_		):




					def __getitem__(					self							,							_lowerCamelCase):
								if "seer" in x and "in1k" not in x:
											UpperCAmelCase__     :    Union[str, Any]         =   RegNetModel
								else:
											UpperCAmelCase__     :    Optional[Any]         =   RegNetForImageClassification
								return val







def 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__    ):
			for from_key, to_key in keys:
						UpperCAmelCase__     :    Any         =   from_state_dict[from_key].clone()
						print(f'''Copied key={from_key} to={to_key}'''    )
			return to_state_dict







def 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__ = True       ,					):
			print(f'''Converting {name}...'''    )
			with torch.no_grad():
						UpperCAmelCase__      ,				UpperCAmelCase__     :    str         =   from_model_func()
						UpperCAmelCase__     :    List[str]         =   our_model_func(__lowerCAmelCase    ).eval()
						UpperCAmelCase__     :    Union[str, Any]         =   ModuleTransfer(src=__lowerCAmelCase       ,					dest=__lowerCAmelCase       ,					raise_if_mismatch=__lowerCAmelCase    )
						UpperCAmelCase__     :    Union[str, Any]         =   torch.randn((1, 3, 2_2_4, 2_2_4)    )
						module_transfer(__lowerCAmelCase    )

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

			UpperCAmelCase__     :    Optional[Any]         =   our_model(__lowerCAmelCase       ,					output_hidden_states=__lowerCAmelCase    )
			UpperCAmelCase__     :    Optional[Any]         =   (
			    our_outputs.logits if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase    ) else our_outputs.last_hidden_state
			)

			UpperCAmelCase__     :    str         =   from_model(__lowerCAmelCase    )
			UpperCAmelCase__     :    List[str]         =   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:
						UpperCAmelCase__     :    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       ,					)

						UpperCAmelCase__     :    Optional[Any]         =   2_2_4 if """seer""" not in name else 3_8_4
						# we can use the convnext one
						UpperCAmelCase__     :    Union[str, 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 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__ = None       ,					UpperCamelCase__ = True    ):
			UpperCAmelCase__     :    int         =   """imagenet-1k-id2label.json"""
			UpperCAmelCase__     :    Any         =   1_0_0_0
			UpperCAmelCase__     :    List[str]         =   (1, num_labels)

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

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

			UpperCAmelCase__     :    Optional[int]         =   partial(__lowerCAmelCase       ,					num_labels=__lowerCAmelCase       ,					idalabel=__lowerCAmelCase       ,					labelaid=__lowerCAmelCase    )

			UpperCAmelCase__     :    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    ),
			}

			UpperCAmelCase__     :    str         =   NameToOurModelFuncMap()
			UpperCAmelCase__     :    Union[str, Any]         =   NameToFromModelFuncMap()
			# add seer weights logic

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

			# pretrained
			UpperCAmelCase__     :    Tuple         =   partial(
			    __lowerCAmelCase       ,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""       ,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )       ,					)

			UpperCAmelCase__     :    List[Any]         =   partial(
			    __lowerCAmelCase       ,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""       ,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )       ,					)

			UpperCAmelCase__     :    Union[str, Any]         =   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()    )       ,					)

			UpperCAmelCase__     :    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=6_20.83       ,					w_m=2.52    )    )    )       ,					)

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

			UpperCAmelCase__     :    str         =   partial(
			    __lowerCAmelCase       ,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""       ,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()    )       ,					)

			UpperCAmelCase__     :    int         =   partial(
			    __lowerCAmelCase       ,					"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""       ,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()    )       ,					)

			UpperCAmelCase__     :    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=6_20.83       ,					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__":
		__A		=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		=parser.parse_args()

		__A		=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)



def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ):

							def get_matched_characters(__lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ) -> str:
														a__             =     []
														a__             =     min(len(_stra   )					,    len(_stra   )   ) // 2
														for i, l in enumerate(_stra   ):
																					a__             =     int(max(0					,    i - limit   )   )
																					a__             =     int(min(i + limit + 1					,    len(_stra   )   )   )
																					if l in _stra[left:right]:
																												matched.append(__lowerCAmelCase   )
																												a__             =     F'{_stra[0:_stra.index(__lowerCAmelCase   )]} {_stra[_stra.index(__lowerCAmelCase   ) + 1:]}'

														return "".join(__lowerCAmelCase   )

							# matching characters
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     len(__lowerCAmelCase   )

							# transposition
							a__             =     (
							    len([(ca, ca) for ca, ca in zip(__lowerCAmelCase					,    __lowerCAmelCase   ) if ca != ca]   ) // 2
							)

							if not match_count:
														a__             =     0.0
							else:
														a__             =     (
														    1
														    / 3
														    * (
														        match_count / len(__lowerCAmelCase   )
														        + match_count / len(__lowerCAmelCase   )
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							a__             =     0
							for ca, ca in zip(stra[:4]					,    stra[:4]   ):
														if ca == ca:
																					prefix_len += 1
														else:
																					break

							return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(jaro_winkler('''hello''', '''world'''))




