



'''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()
snake_case_			=     logging.get_logger()


@dataclass
class 	a__     :
	__magic_name__		:    nn.Module
	__magic_name__		:    List[nn.Module]	    =     field(default_factory=_lowercase	)
	__magic_name__		:    list	    =     field(default_factory=_lowercase	)


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : str,					__UpperCAmelCase : Dict      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(list(m.modules()      )      ) == 1 or isinstance(__UpperCAmelCase,					nn.Convad      ) or isinstance(__UpperCAmelCase,					nn.BatchNormad      )
					if has_not_submodules:
									self.traced.append(__UpperCAmelCase      )


	def __call__(self : str,					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[Any]:

					"""simple docstring"""

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






	@property
	def 				lowercase__  (self : str      )     ->			Optional[Any]:

					"""simple docstring"""

					return list(filter(lambda __UpperCAmelCase      : len(list(x.state_dict().keys()      )      ) > 0,					self.traced      )      )



@dataclass
class 	a__     :
	__magic_name__		:    nn.Module
	__magic_name__		:    nn.Module
	__magic_name__		:    int	    =     1
	__magic_name__		:    List	    =     field(default_factory=_lowercase	)
	__magic_name__		:    List	    =     field(default_factory=_lowercase	)
	__magic_name__		:    bool	    =     True


	def __call__(self : Any,					__UpperCAmelCase : Optional[int]      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Tracker(self.dest      )(__UpperCAmelCase      ).parametrized
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= Tracker(self.src      )(__UpperCAmelCase      ).parametrized

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

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

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



class 	a__     (				nn.Module	):


	def __init__(self : str,					__UpperCAmelCase : Tuple      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
					# - 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  :						Optional[int]      					= len(__UpperCAmelCase      ) + 1
									feature_blocks.append((F'''res{block_index}''', v)      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= nn.ModuleDict(__UpperCAmelCase      )






	def 				lowercase__  (self : List[Any],					__UpperCAmelCase : Tuple      )     ->			List[Any]:

					"""simple docstring"""

					return get_trunk_forward_outputs(
					    __UpperCAmelCase,					out_feat_keys=__UpperCAmelCase,					feature_blocks=self._feature_blocks,					)



class 	a__     (				_lowercase	):


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= x.split('''-'''      )
					return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]      )






	def __getitem__(self : Dict,					__UpperCAmelCase : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					if x not in self:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.convert_name_to_timm(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Dict      					= partial(lambda: (timm.create_model(__UpperCAmelCase,					pretrained=__UpperCAmelCase      ).eval(), None)      )

					else:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= super().__getitem__(__UpperCAmelCase      )

					return val



class 	a__     (				_lowercase	):


	def __getitem__(self : str,					__UpperCAmelCase : Any      )     ->			Optional[int]:

					"""simple docstring"""

					if "seer" in x and "in1k" not in x:
									SCREAMING_SNAKE_CASE  :						Tuple      					= RegNetModel
					else:
									SCREAMING_SNAKE_CASE  :						Any      					= RegNetForImageClassification
					return val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :List[Tuple[str, str]]       ):

				'''simple docstring'''





				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       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Callable[[], nn.Module]    ,	_SCREAMING_SNAKE_CASE      :Callable[[], nn.Module]    ,	_SCREAMING_SNAKE_CASE      :RegNetConfig    ,	_SCREAMING_SNAKE_CASE      :Path    ,	_SCREAMING_SNAKE_CASE      :bool = True    ,	):

				'''simple docstring'''





				print(F'''Converting {name}...'''       )
				with torch.no_grad():
								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Tuple      					= from_model_func()
								SCREAMING_SNAKE_CASE  :						Tuple      					= our_model_func(_UpperCamelCase       ).eval()
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= ModuleTransfer(src=_UpperCamelCase    ,	dest=_UpperCamelCase    ,	raise_if_mismatch=_UpperCamelCase       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= torch.randn((1, 3, 2_24, 2_24)       )
								module_transfer(_UpperCamelCase       )

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

				SCREAMING_SNAKE_CASE  :						str      					= our_model(_UpperCamelCase    ,	output_hidden_states=_UpperCamelCase       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= (
				    our_outputs.logits if isinstance(_UpperCamelCase    ,	_UpperCamelCase       ) else our_outputs.last_hidden_state
				)

				SCREAMING_SNAKE_CASE  :						List[str]      					= from_model(_UpperCamelCase       )
				SCREAMING_SNAKE_CASE  :						Any      					= from_output[-1] if type(_UpperCamelCase       ) 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(_UpperCamelCase    ,	_UpperCamelCase       ), "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=_UpperCamelCase    ,	)

								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 2_24 if '''seer''' not in name else 3_84
								# we can use the convnext one
								SCREAMING_SNAKE_CASE  :						Any      					= AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    ,	size=_UpperCamelCase       )
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / name    ,	commit_message='''Add image processor'''    ,	use_temp_dir=_UpperCamelCase    ,	)

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Path    ,	_SCREAMING_SNAKE_CASE      :str = None    ,	_SCREAMING_SNAKE_CASE      :bool = True       ):

				'''simple docstring'''





				SCREAMING_SNAKE_CASE  :						Any      					= '''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= 10_00
				SCREAMING_SNAKE_CASE  :						Tuple      					= (1, num_labels)

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

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

				SCREAMING_SNAKE_CASE  :						Any      					= partial(_UpperCamelCase    ,	num_labels=_UpperCamelCase    ,	idalabel=_UpperCamelCase    ,	labelaid=_UpperCamelCase       )

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= {
				    '''regnet-x-002''': ImageNetPreTrainedConfig(
				        depths=[1, 1, 4, 7]    ,	hidden_sizes=[24, 56, 1_52, 3_68]    ,	groups_width=8    ,	layer_type='''x'''       ),
				    '''regnet-x-004''': ImageNetPreTrainedConfig(
				        depths=[1, 2, 7, 12]    ,	hidden_sizes=[32, 64, 1_60, 3_84]    ,	groups_width=16    ,	layer_type='''x'''       ),
				    '''regnet-x-006''': ImageNetPreTrainedConfig(
				        depths=[1, 3, 5, 7]    ,	hidden_sizes=[48, 96, 2_40, 5_28]    ,	groups_width=24    ,	layer_type='''x'''       ),
				    '''regnet-x-008''': ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 5]    ,	hidden_sizes=[64, 1_28, 2_88, 6_72]    ,	groups_width=16    ,	layer_type='''x'''       ),
				    '''regnet-x-016''': ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 2]    ,	hidden_sizes=[72, 1_68, 4_08, 9_12]    ,	groups_width=24    ,	layer_type='''x'''       ),
				    '''regnet-x-032''': ImageNetPreTrainedConfig(
				        depths=[2, 6, 15, 2]    ,	hidden_sizes=[96, 1_92, 4_32, 10_08]    ,	groups_width=48    ,	layer_type='''x'''       ),
				    '''regnet-x-040''': ImageNetPreTrainedConfig(
				        depths=[2, 5, 14, 2]    ,	hidden_sizes=[80, 2_40, 5_60, 13_60]    ,	groups_width=40    ,	layer_type='''x'''       ),
				    '''regnet-x-064''': ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1]    ,	hidden_sizes=[1_68, 3_92, 7_84, 16_24]    ,	groups_width=56    ,	layer_type='''x'''       ),
				    '''regnet-x-080''': ImageNetPreTrainedConfig(
				        depths=[2, 5, 15, 1]    ,	hidden_sizes=[80, 2_40, 7_20, 19_20]    ,	groups_width=1_20    ,	layer_type='''x'''       ),
				    '''regnet-x-120''': ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1]    ,	hidden_sizes=[2_24, 4_48, 8_96, 22_40]    ,	groups_width=1_12    ,	layer_type='''x'''       ),
				    '''regnet-x-160''': ImageNetPreTrainedConfig(
				        depths=[2, 6, 13, 1]    ,	hidden_sizes=[2_56, 5_12, 8_96, 20_48]    ,	groups_width=1_28    ,	layer_type='''x'''       ),
				    '''regnet-x-320''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 13, 1]    ,	hidden_sizes=[3_36, 6_72, 13_44, 25_20]    ,	groups_width=1_68    ,	layer_type='''x'''       ),
				    # y variant
				    '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]    ,	hidden_sizes=[24, 56, 1_52, 3_68]    ,	groups_width=8       ),
				    '''regnet-y-004''': ImageNetPreTrainedConfig(
				        depths=[1, 3, 6, 6]    ,	hidden_sizes=[48, 1_04, 2_08, 4_40]    ,	groups_width=8       ),
				    '''regnet-y-006''': ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 4]    ,	hidden_sizes=[48, 1_12, 2_56, 6_08]    ,	groups_width=16       ),
				    '''regnet-y-008''': ImageNetPreTrainedConfig(
				        depths=[1, 3, 8, 2]    ,	hidden_sizes=[64, 1_28, 3_20, 7_68]    ,	groups_width=16       ),
				    '''regnet-y-016''': ImageNetPreTrainedConfig(
				        depths=[2, 6, 17, 2]    ,	hidden_sizes=[48, 1_20, 3_36, 8_88]    ,	groups_width=24       ),
				    '''regnet-y-032''': ImageNetPreTrainedConfig(
				        depths=[2, 5, 13, 1]    ,	hidden_sizes=[72, 2_16, 5_76, 15_12]    ,	groups_width=24       ),
				    '''regnet-y-040''': ImageNetPreTrainedConfig(
				        depths=[2, 6, 12, 2]    ,	hidden_sizes=[1_28, 1_92, 5_12, 10_88]    ,	groups_width=64       ),
				    '''regnet-y-064''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 14, 2]    ,	hidden_sizes=[1_44, 2_88, 5_76, 12_96]    ,	groups_width=72       ),
				    '''regnet-y-080''': ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1]    ,	hidden_sizes=[1_68, 4_48, 8_96, 20_16]    ,	groups_width=56       ),
				    '''regnet-y-120''': ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1]    ,	hidden_sizes=[2_24, 4_48, 8_96, 22_40]    ,	groups_width=1_12       ),
				    '''regnet-y-160''': ImageNetPreTrainedConfig(
				        depths=[2, 4, 11, 1]    ,	hidden_sizes=[2_24, 4_48, 12_32, 30_24]    ,	groups_width=1_12       ),
				    '''regnet-y-320''': ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]    ,	hidden_sizes=[2_32, 6_96, 13_92, 37_12]    ,	groups_width=2_32       ),
				    # models created by SEER -> https://arxiv.org/abs/2202.08360
				    '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]    ,	hidden_sizes=[2_32, 6_96, 13_92, 37_12]    ,	groups_width=2_32       ),
				    '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]    ,	hidden_sizes=[3_28, 9_84, 19_68, 49_20]    ,	groups_width=3_28       ),
				    '''regnet-y-1280-seer''': RegNetConfig(
				        depths=[2, 7, 17, 1]    ,	hidden_sizes=[5_28, 10_56, 29_04, 73_92]    ,	groups_width=2_64       ),
				    '''regnet-y-2560-seer''': RegNetConfig(
				        depths=[3, 7, 16, 1]    ,	hidden_sizes=[6_40, 16_96, 25_44, 50_88]    ,	groups_width=6_40       ),
				    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]    ,	hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]    ,	groups_width=10_10       ),
				    # finetuned on imagenet
				    '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]    ,	hidden_sizes=[2_32, 6_96, 13_92, 37_12]    ,	groups_width=2_32       ),
				    '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1]    ,	hidden_sizes=[3_28, 9_84, 19_68, 49_20]    ,	groups_width=3_28       ),
				    '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]    ,	hidden_sizes=[5_28, 10_56, 29_04, 73_92]    ,	groups_width=2_64       ),
				    '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
				        depths=[3, 7, 16, 1]    ,	hidden_sizes=[6_40, 16_96, 25_44, 50_88]    ,	groups_width=6_40       ),
				    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]    ,	hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]    ,	groups_width=10_10       ),
				}

				SCREAMING_SNAKE_CASE  :						str      					= NameToOurModelFuncMap()
				SCREAMING_SNAKE_CASE  :						str      					= NameToFromModelFuncMap()
				# add seer weights logic

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

				# pretrained
				SCREAMING_SNAKE_CASE  :						Dict      					= partial(
				    _UpperCamelCase    ,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()       )    ,	)

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

				SCREAMING_SNAKE_CASE  :						List[Any]      					= partial(
				    _UpperCamelCase    ,	'''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(
				    _UpperCamelCase    ,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''    ,	lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27    ,	group_width=10_10    ,	w_a=17_44    ,	w_a=6_20.83    ,	w_m=2.52       )       )       )    ,	)

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

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= partial(
				    _UpperCamelCase    ,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()       )    ,	)

				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= partial(
				    _UpperCamelCase    ,	'''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(
				    _UpperCamelCase    ,	'''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=10_10    ,	w_a=17_44    ,	w_a=6_20.83    ,	w_m=2.52       )       )       )    ,	)

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


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

	snake_case_			=     parser.parse_args()

	snake_case_			=     args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     "wav2vec2"


	def __init__(self : Tuple,					__UpperCAmelCase : Tuple=32,					__UpperCAmelCase : int=768,					__UpperCAmelCase : Optional[Any]=12,					__UpperCAmelCase : Any=12,					__UpperCAmelCase : Any=3072,					__UpperCAmelCase : Optional[int]="gelu",					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : List[str]=0.1,					__UpperCAmelCase : List[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.0,					__UpperCAmelCase : str=0.0,					__UpperCAmelCase : Optional[Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.1,					__UpperCAmelCase : Union[str, Any]=0.02,					__UpperCAmelCase : Dict=1e-5,					__UpperCAmelCase : List[str]="group",					__UpperCAmelCase : str="gelu",					__UpperCAmelCase : str=(512, 512, 512, 512, 512, 512, 512),					__UpperCAmelCase : str=(5, 2, 2, 2, 2, 2, 2),					__UpperCAmelCase : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2),					__UpperCAmelCase : Any=False,					__UpperCAmelCase : Optional[Any]=128,					__UpperCAmelCase : Tuple=16,					__UpperCAmelCase : str=False,					__UpperCAmelCase : int=True,					__UpperCAmelCase : Tuple=0.05,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Union[str, Any]=2,					__UpperCAmelCase : Optional[Any]=0.0,					__UpperCAmelCase : List[Any]=10,					__UpperCAmelCase : Dict=0,					__UpperCAmelCase : Any=320,					__UpperCAmelCase : int=2,					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : Tuple=100,					__UpperCAmelCase : Any=256,					__UpperCAmelCase : Tuple=256,					__UpperCAmelCase : str=0.1,					__UpperCAmelCase : Any="sum",					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : str=False,					__UpperCAmelCase : str=256,					__UpperCAmelCase : Union[str, Any]=(512, 512, 512, 512, 1500),					__UpperCAmelCase : Optional[int]=(5, 3, 3, 1, 1),					__UpperCAmelCase : Dict=(1, 2, 3, 1, 1),					__UpperCAmelCase : List[str]=512,					__UpperCAmelCase : Union[str, Any]=0,					__UpperCAmelCase : List[Any]=1,					__UpperCAmelCase : List[str]=2,					__UpperCAmelCase : List[Any]=False,					__UpperCAmelCase : str=3,					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : str=3,					__UpperCAmelCase : int=None,					__UpperCAmelCase : List[Any]=None,					**__UpperCAmelCase : int,					)     ->			Tuple:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase,					pad_token_id=__UpperCAmelCase,					bos_token_id=__UpperCAmelCase,					eos_token_id=__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= feat_extract_norm
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= feat_extract_activation
					SCREAMING_SNAKE_CASE  :						Dict      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= conv_bias
					SCREAMING_SNAKE_CASE  :						Dict      					= num_conv_pos_embeddings
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_conv_pos_embedding_groups
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= len(self.conv_dim      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						str      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Any      					= hidden_act
					SCREAMING_SNAKE_CASE  :						str      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						int      					= hidden_dropout
					SCREAMING_SNAKE_CASE  :						Any      					= attention_dropout
					SCREAMING_SNAKE_CASE  :						int      					= activation_dropout
					SCREAMING_SNAKE_CASE  :						int      					= feat_proj_dropout
					SCREAMING_SNAKE_CASE  :						List[str]      					= final_dropout
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= layerdrop
					SCREAMING_SNAKE_CASE  :						Tuple      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						Dict      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= do_stable_layer_norm
					SCREAMING_SNAKE_CASE  :						Dict      					= use_weighted_layer_sum

					if (
					    (len(self.conv_stride      ) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
					    or (len(self.conv_dim      ) != self.num_feat_extract_layers)
					):
									raise ValueError(
									    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
									    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
									    F''' {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,'''
									    F''' `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'''      )

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					SCREAMING_SNAKE_CASE  :						str      					= apply_spec_augment
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_time_prob
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= mask_time_length
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= mask_time_min_masks
					SCREAMING_SNAKE_CASE  :						Dict      					= mask_feature_prob
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_length
					SCREAMING_SNAKE_CASE  :						Tuple      					= mask_feature_min_masks

					# parameters for pretraining with codevector quantized representations
					SCREAMING_SNAKE_CASE  :						Any      					= num_codevectors_per_group
					SCREAMING_SNAKE_CASE  :						int      					= num_codevector_groups
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= contrastive_logits_temperature
					SCREAMING_SNAKE_CASE  :						List[Any]      					= feat_quantizer_dropout
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= num_negatives
					SCREAMING_SNAKE_CASE  :						Any      					= codevector_dim
					SCREAMING_SNAKE_CASE  :						str      					= proj_codevector_dim
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= diversity_loss_weight

					# ctc loss
					SCREAMING_SNAKE_CASE  :						Tuple      					= ctc_loss_reduction
					SCREAMING_SNAKE_CASE  :						Any      					= ctc_zero_infinity

					# adapter
					SCREAMING_SNAKE_CASE  :						int      					= add_adapter
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_kernel_size
					SCREAMING_SNAKE_CASE  :						int      					= adapter_stride
					SCREAMING_SNAKE_CASE  :						List[str]      					= num_adapter_layers
					SCREAMING_SNAKE_CASE  :						Tuple      					= output_hidden_size or hidden_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= adapter_attn_dim

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						Dict      					= classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= list(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= xvector_output_dim






	@property
	def 				lowercase__  (self : Tuple      )     ->			Optional[Any]:

					"""simple docstring"""

					return functools.reduce(operator.mul,					self.conv_stride,					1      )




