






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()
UpperCamelCase								=		logging.get_logger()

@dataclass
class 				lowerCAmelCase_	:
				_UpperCamelCase					:       nn.Module
				_UpperCamelCase					:       List[nn.Module]							       = field(default_factory=__snake_case			)
				_UpperCamelCase					:       list							       = field(default_factory=__snake_case			)


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											_lowercase							:   Union[str, Any] 							=   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      ):
											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 						__a					(					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 				lowerCAmelCase_	:
				_UpperCamelCase					:       nn.Module
				_UpperCamelCase					:       nn.Module
				_UpperCamelCase					:       int							       = 1
				_UpperCamelCase					:       List							       = field(default_factory=__snake_case			)
				_UpperCamelCase					:       List							       = field(default_factory=__snake_case			)
				_UpperCamelCase					:       bool							       = True


				def __call__(					self ,						_lowerCAmelCase      ):
											_lowercase							:   str 							=   Tracker(self.dest      )(_lowerCAmelCase      ).parametrized
											_lowercase							:   Optional[int] 							=   Tracker(self.src      )(_lowerCAmelCase      ).parametrized

											_lowercase							:   str 							=   list(filter(lambda _lowerCAmelCase      : type(_lowerCAmelCase      ) not in self.src_skip ,						_lowerCAmelCase      )      )
											_lowercase							:   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      ):
											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							:   Any 							=   len(_lowerCAmelCase      ) + 1
																		feature_blocks.append((F"""res{block_index}""", v)      )

											_lowercase							:   List[str] 							=   nn.ModuleDict(_lowerCAmelCase      )


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




class 				lowerCAmelCase_	(		__snake_case			):


				def 						__a					(					self ,						_lowerCAmelCase      ):
											_lowercase							:   Optional[Any] 							=   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:
																		_lowercase							:   List[str] 							=   self.convert_name_to_timm(_lowerCAmelCase      )
																		_lowercase							:   Tuple 							=   partial(lambda: (timm.create_model(_lowerCAmelCase ,						pretrained=_lowerCAmelCase      ).eval(), None)      )

											else:
																		_lowercase							:   Tuple 							=   super().__getitem__(_lowerCAmelCase      )

											return val




class 				lowerCAmelCase_	(		__snake_case			):


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






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Union[str, Any]:




							for from_key, to_key in keys:
														_lowercase							:   List[Any] 							=   from_state_dict[from_key].clone()
														print(F"""Copied key={from_key} to={to_key}"""				)
							return to_state_dict






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = True     ,     )     ->    Tuple:
							print(F"""Converting {name}..."""				)
							with torch.no_grad():
														_lowercase							:   Any 							=   from_model_func()
														_lowercase							:   Optional[Any] 							=   our_model_func(SCREAMING_SNAKE_CASE				).eval()
														_lowercase							:   Union[str, Any] 							=   ModuleTransfer(src=SCREAMING_SNAKE_CASE     ,     dest=SCREAMING_SNAKE_CASE     ,     raise_if_mismatch=SCREAMING_SNAKE_CASE				)
														_lowercase							:   Optional[Any] 							=   torch.randn((1, 3, 224, 224)				)
														module_transfer(SCREAMING_SNAKE_CASE				)

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

							_lowercase							:   str 							=   our_model(SCREAMING_SNAKE_CASE     ,     output_hidden_states=SCREAMING_SNAKE_CASE				)
							_lowercase							:   Dict 							=   (
							    our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				) else our_outputs.last_hidden_state
							)

							_lowercase							:   Union[str, Any] 							=   from_model(SCREAMING_SNAKE_CASE				)
							_lowercase							:   int 							=   from_output[-1] if type(SCREAMING_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:
														_lowercase							:   Union[str, Any] 							=   our_outputs.hidden_states[-1]

							assert torch.allclose(SCREAMING_SNAKE_CASE     ,     SCREAMING_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=SCREAMING_SNAKE_CASE     ,     )

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

														print(F"""Pushed {name}"""				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE = None     ,     SCREAMING_SNAKE_CASE = True				)     ->    List[str]:
							_lowercase							:   Union[str, Any] 							=   "imagenet-1k-id2label.json"
							_lowercase							:   str 							=   1_000
							_lowercase							:   List[Any] 							=   (1, num_labels)

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

							_lowercase							:   Optional[Any] 							=   idalabel
							_lowercase							:   List[str] 							=   {v: k for k, v in idalabel.items()}

							_lowercase							:   int 							=   partial(SCREAMING_SNAKE_CASE     ,     num_labels=SCREAMING_SNAKE_CASE     ,     idalabel=SCREAMING_SNAKE_CASE     ,     labelaid=SCREAMING_SNAKE_CASE				)

							_lowercase							:   str 							=   {
							    "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				),
							}

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

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

							# pretrained
							_lowercase							:   Union[str, Any] 							=   partial(
							    SCREAMING_SNAKE_CASE     ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'     ,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,     )

							_lowercase							:   int 							=   partial(
							    SCREAMING_SNAKE_CASE     ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'     ,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,     )

							_lowercase							:   int 							=   partial(
							    SCREAMING_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()				)     ,     )

							_lowercase							:   Tuple 							=   partial(
							    SCREAMING_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=620.83     ,     w_m=2.52				)				)				)     ,     )

							# IN1K finetuned
							_lowercase							:   Optional[int] 							=   partial(
							    SCREAMING_SNAKE_CASE     ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,     )

							_lowercase							:   int 							=   partial(
							    SCREAMING_SNAKE_CASE     ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)     ,     )

							_lowercase							:   List[str] 							=   partial(
							    SCREAMING_SNAKE_CASE     ,     'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'     ,     lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)     ,     )

							_lowercase							:   List[Any] 							=   partial(
							    SCREAMING_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=620.83     ,     w_m=2.52				)				)				)     ,     )

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


if __name__ == "__main__":
							UpperCamelCase								=		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.",
							)

							UpperCamelCase								=		parser.parse_args()

							UpperCamelCase								=		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)








import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE__     					=			"examples/"
SCREAMING_SNAKE_CASE__     					=			{
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
SCREAMING_SNAKE_CASE__     					=			{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
SCREAMING_SNAKE_CASE__     					=			"README.md"


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		with open(a     , "r"     , encoding="utf-8"     , newline="\n" ) as f:
				SCREAMING_SNAKE_CASE_				:Dict				     =						f.read()
		SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						REPLACE_PATTERNS[pattern]
		SCREAMING_SNAKE_CASE_				:List[str]				     =						replace.replace("VERSION"     , a )
		SCREAMING_SNAKE_CASE_				:str				     =						re_pattern.sub(a     , a )
		with open(a     , "w"     , encoding="utf-8"     , newline="\n" ) as f:
				f.write(a )


def     lowercase	(    a ):



		'''simple docstring'''




		for folder, directories, fnames in os.walk(a ):
				# Removing some of the folders with non-actively maintained examples from the walk
				if "research_projects" in directories:
						directories.remove("research_projects" )
				if "legacy" in directories:
						directories.remove("legacy" )
				for fname in fnames:
						if fname.endswith(".py" ):
								update_version_in_file(os.path.join(a     , a )     , a     , pattern="examples" )


def     lowercase	(    a     , a=False ):



		'''simple docstring'''




		for pattern, fname in REPLACE_FILES.items():
				update_version_in_file(a     , a     , a )
		if not patch:
				update_version_in_examples(a )


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						"🤗 Transformers currently provides the following architectures"
		SCREAMING_SNAKE_CASE_				:Any				     =						"1. Want to contribute a new model?"
		with open(a     , "r"     , encoding="utf-8"     , newline="\n" ) as f:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						f.readlines()

		# Find the start of the list.
		SCREAMING_SNAKE_CASE_				:int				     =						0
		while not lines[start_index].startswith(_start_prompt ):
				start_index += 1
		start_index += 1

		SCREAMING_SNAKE_CASE_				:int				     =						start_index
		# Update the lines in the model list.
		while not lines[index].startswith(_end_prompt ):
				if lines[index].startswith("1." ):
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						lines[index].replace(
						    "https://huggingface.co/docs/transformers/main/model_doc"     , "https://huggingface.co/docs/transformers/model_doc"     , )
				index += 1

		with open(a     , "w"     , encoding="utf-8"     , newline="\n" ) as f:
				f.writelines(a )


def     lowercase	(    ):



		'''simple docstring'''




		with open(REPLACE_FILES["init"]     , "r" ) as f:
				SCREAMING_SNAKE_CASE_				:str				     =						f.read()
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						REPLACE_PATTERNS["init"][0].search(a ).groups()[0]
		return packaging.version.parse(a )


def     lowercase	(    a=False ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:str				     =						get_version()
		if patch and default_version.is_devrelease:
				raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
		if default_version.is_devrelease:
				SCREAMING_SNAKE_CASE_				:int				     =						default_version.base_version
		elif patch:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						F"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
		else:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						F"{default_version.major}.{default_version.minor + 1}.0"

		# Now let's ask nicely if that's the right one.
		SCREAMING_SNAKE_CASE_				:List[Any]				     =						input(F"Which version are you releasing? [{default_version}]" )
		if len(a ) == 0:
				SCREAMING_SNAKE_CASE_				:Any				     =						default_version

		print(F"Updating version to {version}." )
		global_version_update(a     , patch=a )
		if not patch:
				print("Cleaning main README, don't forget to run `make fix-copies`." )
				clean_main_ref_in_model_list()


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						get_version()
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						F"{current_version.major}.{current_version.minor + 1}.0.dev0"
		SCREAMING_SNAKE_CASE_				:str				     =						current_version.base_version

		# Check with the user we got that right.
		SCREAMING_SNAKE_CASE_				:Any				     =						input(F"Which version are we developing now? [{dev_version}]" )
		if len(a ) == 0:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dev_version

		print(F"Updating version to {version}." )
		global_version_update(a )
		print("Cleaning main README, don't forget to run `make fix-copies`." )
		clean_main_ref_in_model_list()


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			argparse.ArgumentParser()
		parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
		parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
		SCREAMING_SNAKE_CASE__     					=			parser.parse_args()
		if not args.post_release:
				pre_release_work(patch=args.patch)
		elif args.patch:
				print("Nothing to do after a patch :-)")
		else:
				post_release_work()






