

'''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()
lowerCAmelCase__								=		logging.get_logger()







@dataclass
class 	lowercase    :


		"""simple docstring"""




		a__					       =  42
		a__					       =  field(default_factory=lowercase__							)
		a__					       =  field(default_factory=lowercase__							)


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Tuple			=						len(list(m.modules())) == 1 or isinstance(__snake_case	,       nn.Convad) or isinstance(__snake_case	,       nn.BatchNormad)
			if has_not_submodules:
				self.traced.append(__snake_case)


		def __call__(				self	,       __snake_case):
			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 		A__					(				self):
			# check the len of the state_dict keys to see if we have learnable params
			return list(filter(lambda __snake_case: len(list(x.state_dict().keys())) > 0	,       self.traced))







@dataclass
class 	lowercase    :


		"""simple docstring"""




		a__					       =  42
		a__					       =  42
		a__					       =  1
		a__					       =  field(default_factory=lowercase__							)
		a__					       =  field(default_factory=lowercase__							)
		a__					       =  True


		def __call__(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						Tracker(self.dest)(__snake_case).parametrized
			_UpperCamelCase   :		Dict			=						Tracker(self.src)(__snake_case).parametrized

			_UpperCamelCase   :		Tuple			=						list(filter(lambda __snake_case: type(__snake_case) not in self.src_skip	,       __snake_case))
			_UpperCamelCase   :		str			=						list(filter(lambda __snake_case: type(__snake_case) not in self.dest_skip	,       __snake_case))

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

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







class 	lowercase    (				nn.Module							):


		"""simple docstring"""




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

			_UpperCamelCase   :		List[Any]			=						[]
			# - 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   :		List[str]			=						len(__snake_case) + 1
				feature_blocks.append((f'''res{block_index}''', v))

			_UpperCamelCase   :		Tuple			=						nn.ModuleDict(__snake_case)






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







class 	lowercase    (				lowercase__							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Union[str, Any]			=						x.split('-')
			return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])






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

			else:
				_UpperCamelCase   :		int			=						super().__getitem__(__snake_case)

			return val







class 	lowercase    (				lowercase__							):


		"""simple docstring"""




		def __getitem__(				self	,       __snake_case):
			if "seer" in x and "in1k" not in x:
				_UpperCamelCase   :		List[str]			=						RegNetModel
			else:
				_UpperCamelCase   :		Optional[int]			=						RegNetForImageClassification
			return val






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Optional[Any]	)						->				Tuple:




	'''simple docstring'''




	for from_key, to_key in keys:
		_UpperCamelCase   :		int			=						from_state_dict[from_key].clone()
		print(F'''Copied key={from_key} to={to_key}'''	)
	return to_state_dict






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Dict = True							,       )						->				Optional[Any]:




	'''simple docstring'''




	print(F'''Converting {name}...'''	)
	with torch.no_grad():
		_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						from_model_func()
		_UpperCamelCase   :		Tuple			=						our_model_func(__SCREAMING_SNAKE_CASE	).eval()
		_UpperCamelCase   :		str			=						ModuleTransfer(src=__SCREAMING_SNAKE_CASE							,       dest=__SCREAMING_SNAKE_CASE							,       raise_if_mismatch=__SCREAMING_SNAKE_CASE	)
		_UpperCamelCase   :		Any			=						torch.randn((1, 3, 2_2_4, 2_2_4)	)
		module_transfer(__SCREAMING_SNAKE_CASE	)

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

	_UpperCamelCase   :		Tuple			=						our_model(__SCREAMING_SNAKE_CASE							,       output_hidden_states=__SCREAMING_SNAKE_CASE	)
	_UpperCamelCase   :		Dict			=						(
	    our_outputs.logits if isinstance(__SCREAMING_SNAKE_CASE							,       __SCREAMING_SNAKE_CASE	) else our_outputs.last_hidden_state
	)

	_UpperCamelCase   :		Union[str, Any]			=						from_model(__SCREAMING_SNAKE_CASE	)
	_UpperCamelCase   :		List[str]			=						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:
		_UpperCamelCase   :		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							,       )

		_UpperCamelCase   :		Any			=						2_2_4 if 'seer' not in name else 3_8_4
		# we can use the convnext one
		_UpperCamelCase   :		int			=						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 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     str = None							,       UpperCAmelCase_							:     Tuple = True	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=						'imagenet-1k-id2label.json'
	_UpperCamelCase   :		Any			=						1_0_0_0
	_UpperCamelCase   :		Tuple			=						(1, num_labels)

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

	_UpperCamelCase   :		Optional[int]			=						idalabel
	_UpperCamelCase   :		Union[str, Any]			=						{v: k for k, v in idalabel.items()}

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

	_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   :		Dict			=						NameToOurModelFuncMap()
	_UpperCamelCase   :		Any			=						NameToFromModelFuncMap()
	# add seer weights logic

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

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

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

	_UpperCamelCase   :		Optional[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()	)							,       )

	_UpperCamelCase   :		List[str]			=						partial(
	    __SCREAMING_SNAKE_CASE							,       '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_2_0.8_3							,       w_m=2.5_2	)	)	)							,       )

	# IN1K finetuned
	_UpperCamelCase   :		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()	)							,       )

	_UpperCamelCase   :		Dict			=						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()	)							,       )

	_UpperCamelCase   :		int			=						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()	)							,       )

	_UpperCamelCase   :		Dict			=						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=2_7							,       group_width=1_0_1_0							,       w_a=1_7_4_4							,       w_a=6_2_0.8_3							,       w_m=2.5_2	)	)	)							,       )

	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__":
							lowerCAmelCase__								=		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.""",
							)

							lowerCAmelCase__								=		parser.parse_args()

							lowerCAmelCase__								=		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 logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




