


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 							lowerCamelCase   :
	_lowerCAmelCase     :		Optional[int]										=						4_2
	_lowerCAmelCase     :		Any										=						field(default_factory=SCREAMING_SNAKE_CASE_ )
	_lowerCAmelCase     :		List[str]										=						field(default_factory=SCREAMING_SNAKE_CASE_ )


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							List[Any]	    =	len(list(m.modules())) == 1 or isinstance(__a				,     nn.Convad) or isinstance(__a				,     nn.BatchNormad)
							if has_not_submodules:
													self.traced.append(__a)


	def __call__(    self				,     lowercase__):
							for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook))
							self.module(__a)
							[x.remove() for x in self.handles]
							return self


	@property
	def  A(    self):
							return list(filter(lambda lowercase__: len(list(x.state_dict().keys())) > 0				,     self.traced))






@dataclass
class 							lowerCamelCase   :
	_lowerCAmelCase     :		Any										=						4_2
	_lowerCAmelCase     :		Union[str, Any]										=						4_2
	_lowerCAmelCase     :		Any										=						1
	_lowerCAmelCase     :		Tuple										=						field(default_factory=SCREAMING_SNAKE_CASE_ )
	_lowerCAmelCase     :		Optional[int]										=						field(default_factory=SCREAMING_SNAKE_CASE_ )
	_lowerCAmelCase     :		Optional[int]										=						True


	def __call__(    self				,     lowercase__):
							__UpperCAmelCase			:							Dict	    =	Tracker(self.dest)(__a).parametrized
							__UpperCAmelCase			:							List[Any]	    =	Tracker(self.src)(__a).parametrized

							__UpperCAmelCase			:							List[str]	    =	list(filter(lambda lowercase__: type(__a) not in self.src_skip				,     __a))
							__UpperCAmelCase			:							Tuple	    =	list(filter(lambda lowercase__: type(__a) not in self.dest_skip				,     __a))

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

							for dest_m, src_m in zip(__a				,     __a):
													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				,     lowercase__):
							super().__init__()

							__UpperCAmelCase			:							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}"
													__UpperCAmelCase			:							Any	    =	len(__a) + 1
													feature_blocks.append((F"res{block_index}", v))

							__UpperCAmelCase			:							Tuple	    =	nn.ModuleDict(__a)


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






class 							lowerCamelCase   (  SCREAMING_SNAKE_CASE_ ):


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							str	    =	x.split('''-''')
							return x_split[0] + x_split[1] + "_" + "".join(x_split[2:])


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

							else:
													__UpperCAmelCase			:							Tuple	    =	super().__getitem__(__a)

							return val






class 							lowerCamelCase   (  SCREAMING_SNAKE_CASE_ ):


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






def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Dict:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_ = True      ,       )		->     Dict:




						'''simple docstring'''







						print(f"Converting {name}..."      )
						with torch.no_grad():
												__UpperCAmelCase			:							Union[str, Any]	    =	from_model_func()
												__UpperCAmelCase			:							Optional[int]	    =	our_model_func(_UpperCAmelCase      ).eval()
												__UpperCAmelCase			:							Tuple	    =	ModuleTransfer(src=_UpperCAmelCase      ,       dest=_UpperCAmelCase      ,       raise_if_mismatch=_UpperCAmelCase      )
												__UpperCAmelCase			:							int	    =	torch.randn((1, 3, 224, 224)      )
												module_transfer(_UpperCAmelCase      )

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

						__UpperCAmelCase			:							Any	    =	our_model(_UpperCAmelCase      ,       output_hidden_states=_UpperCAmelCase      )
						__UpperCAmelCase			:							Optional[int]	    =	(
						    our_outputs.logits if isinstance(_UpperCAmelCase      ,       _UpperCAmelCase      ) else our_outputs.last_hidden_state
						)

						__UpperCAmelCase			:							Dict	    =	from_model(_UpperCAmelCase      )
						__UpperCAmelCase			:							Union[str, 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:
												__UpperCAmelCase			:							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      ,       )

												__UpperCAmelCase			:							str	    =	224 if 'seer' not in name else 384
												# we can use the convnext one
												__UpperCAmelCase			:							str	    =	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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = None      ,       lowercase_ = True      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							List[str]	    =	'imagenet-1k-id2label.json'
						__UpperCAmelCase			:							str	    =	1000
						__UpperCAmelCase			:							int	    =	(1, num_labels)

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

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

						__UpperCAmelCase			:							str	    =	partial(_UpperCAmelCase      ,       num_labels=_UpperCAmelCase      ,       idalabel=_UpperCAmelCase      ,       labelaid=_UpperCAmelCase      )

						__UpperCAmelCase			:							List[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, 1008]      ,       groups_width=48      ,       layer_type='''x'''      ),
						    'regnet-x-040': ImageNetPreTrainedConfig(
						        depths=[2, 5, 14, 2]      ,       hidden_sizes=[80, 240, 560, 1360]      ,       groups_width=40      ,       layer_type='''x'''      ),
						    'regnet-x-064': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]      ,       hidden_sizes=[168, 392, 784, 1624]      ,       groups_width=56      ,       layer_type='''x'''      ),
						    'regnet-x-080': ImageNetPreTrainedConfig(
						        depths=[2, 5, 15, 1]      ,       hidden_sizes=[80, 240, 720, 1920]      ,       groups_width=120      ,       layer_type='''x'''      ),
						    'regnet-x-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]      ,       hidden_sizes=[224, 448, 896, 2240]      ,       groups_width=112      ,       layer_type='''x'''      ),
						    'regnet-x-160': ImageNetPreTrainedConfig(
						        depths=[2, 6, 13, 1]      ,       hidden_sizes=[256, 512, 896, 2048]      ,       groups_width=128      ,       layer_type='''x'''      ),
						    'regnet-x-320': ImageNetPreTrainedConfig(
						        depths=[2, 7, 13, 1]      ,       hidden_sizes=[336, 672, 1344, 2520]      ,       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, 1512]      ,       groups_width=24      ),
						    'regnet-y-040': ImageNetPreTrainedConfig(
						        depths=[2, 6, 12, 2]      ,       hidden_sizes=[128, 192, 512, 1088]      ,       groups_width=64      ),
						    'regnet-y-064': ImageNetPreTrainedConfig(
						        depths=[2, 7, 14, 2]      ,       hidden_sizes=[144, 288, 576, 1296]      ,       groups_width=72      ),
						    'regnet-y-080': ImageNetPreTrainedConfig(
						        depths=[2, 4, 10, 1]      ,       hidden_sizes=[168, 448, 896, 2016]      ,       groups_width=56      ),
						    'regnet-y-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 11, 1]      ,       hidden_sizes=[224, 448, 896, 2240]      ,       groups_width=112      ),
						    'regnet-y-160': ImageNetPreTrainedConfig(
						        depths=[2, 4, 11, 1]      ,       hidden_sizes=[224, 448, 1232, 3024]      ,       groups_width=112      ),
						    'regnet-y-320': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]      ,       hidden_sizes=[232, 696, 1392, 3712]      ,       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, 1392, 3712]      ,       groups_width=232      ),
						    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]      ,       hidden_sizes=[328, 984, 1968, 4920]      ,       groups_width=328      ),
						    'regnet-y-1280-seer': RegNetConfig(
						        depths=[2, 7, 17, 1]      ,       hidden_sizes=[528, 1056, 2904, 7392]      ,       groups_width=264      ),
						    'regnet-y-2560-seer': RegNetConfig(
						        depths=[3, 7, 16, 1]      ,       hidden_sizes=[640, 1696, 2544, 5088]      ,       groups_width=640      ),
						    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]      ,       hidden_sizes=[2020, 4040, 11110, 28280]      ,       groups_width=1010      ),
						    # finetuned on imagenet
						    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]      ,       hidden_sizes=[232, 696, 1392, 3712]      ,       groups_width=232      ),
						    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 12, 1]      ,       hidden_sizes=[328, 984, 1968, 4920]      ,       groups_width=328      ),
						    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]      ,       hidden_sizes=[528, 1056, 2904, 7392]      ,       groups_width=264      ),
						    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[3, 7, 16, 1]      ,       hidden_sizes=[640, 1696, 2544, 5088]      ,       groups_width=640      ),
						    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1]      ,       hidden_sizes=[2020, 4040, 11110, 28280]      ,       groups_width=1010      ),
						}

						__UpperCAmelCase			:							Tuple	    =	NameToOurModelFuncMap()
						__UpperCAmelCase			:							Tuple	    =	NameToFromModelFuncMap()
						# add seer weights logic

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

						# pretrained
						__UpperCAmelCase			:							str	    =	partial(
						    _UpperCAmelCase      ,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''      ,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )      ,       )

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

						__UpperCAmelCase			:							Optional[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()      )      ,       )

						__UpperCAmelCase			:							int	    =	partial(
						    _UpperCAmelCase      ,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''      ,       lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=27      ,       group_width=1010      ,       w_a=1744      ,       w_a=620.83      ,       w_m=2.5_2      )      )      )      ,       )

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

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

						__UpperCAmelCase			:							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()      )      ,       )

						__UpperCAmelCase			:							Union[str, Any]	    =	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=1010      ,       w_a=1744      ,       w_a=620.83      ,       w_m=2.5_2      )      )      )      ,       )

						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__":
				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)






'''simple docstring'''





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


A__    :   Optional[Any]           =					logging.get_logger(__name__)

A__    :   List[str]           =					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''cvt'''
 def __init__(   self						:							Optional[Any]      ,     __a						:							Union[str, Any]=3      ,     __a						:							List[Any]=[7, 3, 3]      ,     __a						:							Optional[int]=[4, 2, 2]      ,     __a						:							Dict=[2, 1, 1]      ,     __a						:							Union[str, Any]=[64, 192, 384]      ,     __a						:							int=[1, 3, 6]      ,     __a						:							List[str]=[1, 2, 10]      ,     __a						:							Optional[Any]=[4.0, 4.0, 4.0]      ,     __a						:							Any=[0.0, 0.0, 0.0]      ,     __a						:							List[str]=[0.0, 0.0, 0.0]      ,     __a						:							List[Any]=[0.0, 0.0, 0.1]      ,     __a						:							List[str]=[True, True, True]      ,     __a						:							int=[False, False, True]      ,     __a						:							Dict=["dw_bn", "dw_bn", "dw_bn"]      ,     __a						:							List[str]=[3, 3, 3]      ,     __a						:							Union[str, Any]=[1, 1, 1]      ,     __a						:							Optional[int]=[2, 2, 2]      ,     __a						:							Optional[Any]=[1, 1, 1]      ,     __a						:							List[str]=[1, 1, 1]      ,     __a						:							List[str]=0.0_2      ,     __a						:							List[str]=1e-12      ,     **__a						:							List[str]      ,     )      ->   Dict:







       '''simple docstring'''
       super().__init__(**__a  )
       __snake_case :   int													=    num_channels
       __snake_case :   Union[str, Any]													=    patch_sizes
       __snake_case :   Any													=    patch_stride
       __snake_case :   List[str]													=    patch_padding
       __snake_case :   Optional[Any]													=    embed_dim
       __snake_case :   Union[str, Any]													=    num_heads
       __snake_case :   Dict													=    depth
       __snake_case :   Optional[Any]													=    mlp_ratio
       __snake_case :   List[str]													=    attention_drop_rate
       __snake_case :   Optional[int]													=    drop_rate
       __snake_case :   Optional[int]													=    drop_path_rate
       __snake_case :   Any													=    qkv_bias
       __snake_case :   int													=    cls_token
       __snake_case :   Optional[int]													=    qkv_projection_method
       __snake_case :   List[Any]													=    kernel_qkv
       __snake_case :   List[Any]													=    padding_kv
       __snake_case :   int													=    stride_kv
       __snake_case :   List[str]													=    padding_q
       __snake_case :   Dict													=    stride_q
       __snake_case :   Tuple													=    initializer_range
       __snake_case :   List[Any]													=    layer_norm_eps



