






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       :       int        =				logging.get_logger()

@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	nn.Module
					_A  :	List[nn.Module]     =					field(default_factory=__a	)
					_A  :	list     =					field(default_factory=__a	)



					def 					lowerCAmelCase (     self :		Optional[int]						,   __a :		Union[str, Any]						,   __a :		Tensor						,   __a :		Tensor							)	->     Any:


												"""simple docstring"""

												__lowercase					:							Dict			=   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 :		Dict						,   __a :		Tensor							)	->     Tuple:


												"""simple docstring"""

												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 					lowerCAmelCase (     self :		str							)	->     Dict:


												"""simple docstring"""

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







@dataclass
class 						lowerCAmelCase   :

					'''simple docstring'''

					_A  :	nn.Module
					_A  :	nn.Module
					_A  :	int     =					1
					_A  :	List     =					field(default_factory=__a	)
					_A  :	List     =					field(default_factory=__a	)
					_A  :	bool     =					True



					def __call__(     self :		List[str]						,   __a :		Tensor							)	->     Any:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   Tracker(self.dest							)(__a							).parametrized
												__lowercase					:							List[Any]			=   Tracker(self.src							)(__a							).parametrized

												__lowercase					:							Any			=   list(filter(lambda __a							: type(__a							) not in self.src_skip						,   __a							)							)
												__lowercase					:							Dict			=   list(filter(lambda __a							: 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	):

					'''simple docstring'''

					def __init__(     self :		Dict						,   __a :		nn.Module							)	->     Tuple:


												"""simple docstring"""

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

												__lowercase					:							Any			=   nn.ModuleDict(__a							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Tensor							)	->     Optional[int]:


												"""simple docstring"""

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







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		str							)	->     str:


												"""simple docstring"""

												__lowercase					:							Union[str, Any]			=   x.split("""-"""							)
												return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)



					def __getitem__(     self :		List[Any]						,   __a :		str							)	->     Callable[[], Tuple[nn.Module, Dict]]:


												"""simple docstring"""

												if x not in self:
																			__lowercase					:							str			=   self.convert_name_to_timm(__a							)
																			__lowercase					:							List[str]			=   partial(lambda: (timm.create_model(__a						,   pretrained=__a							).eval(), None)							)

												else:
																			__lowercase					:							Optional[int]			=   super().__getitem__(__a							)

												return val







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					def __getitem__(     self :		List[Any]						,   __a :		str							)	->     Callable[[], nn.Module]:


												"""simple docstring"""

												if "seer" in x and "in1k" not in x:
																			__lowercase					:							List[Any]			=   RegNetModel
												else:
																			__lowercase					:							Tuple			=   RegNetForImageClassification
												return val







def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  Union[str, Any]				,				lowerCAmelCase_				:  List[Tuple[str, str]]					):
							for from_key, to_key in keys:
														__lowercase					:							int			=   from_state_dict[from_key].clone()
														print(F"Copied key={from_key} to={to_key}"					)
							return to_state_dict
def 					snake_case_						(   lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  Callable[[], nn.Module]				,				lowerCAmelCase_				:  Callable[[], nn.Module]				,				lowerCAmelCase_				:  RegNetConfig				,				lowerCAmelCase_				:  Path				,				lowerCAmelCase_				:  bool = True				,				):
							print(F"Converting {name}..."					)
							with torch.no_grad():
														__lowercase	,	__lowercase					:							Any			=   from_model_func()
														__lowercase					:							int			=   our_model_func(lowerCAmelCase_					).eval()
														__lowercase					:							Dict			=   ModuleTransfer(src=lowerCAmelCase_				,				dest=lowerCAmelCase_				,				raise_if_mismatch=lowerCAmelCase_					)
														__lowercase					:							Optional[Any]			=   torch.randn((1, 3, 224, 224)					)
														module_transfer(lowerCAmelCase_					)

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

							__lowercase					:							str			=   our_model(lowerCAmelCase_				,				output_hidden_states=lowerCAmelCase_					)
							__lowercase					:							Optional[int]			=   (
							    our_outputs.logits if isinstance(lowerCAmelCase_				,				lowerCAmelCase_					) else our_outputs.last_hidden_state
							)

							__lowercase					:							str			=   from_model(lowerCAmelCase_					)
							__lowercase					:							Dict			=   from_output[-1] if type(lowerCAmelCase_					) is list else from_output

							# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
							if "seer" in name and "in1k" in name:
														__lowercase					:							str			=   our_outputs.hidden_states[-1]

							assert torch.allclose(lowerCAmelCase_				,				lowerCAmelCase_					), "The model logits don't match the original one."

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / name				,				commit_message="""Add model"""				,				use_temp_dir=lowerCAmelCase_				,				)

														__lowercase					:							Tuple			=   224 if """seer""" not in name else 384
														# we can use the convnext one
														__lowercase					:							str			=   AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""				,				size=lowerCAmelCase_					)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name				,				commit_message="""Add image processor"""				,				use_temp_dir=lowerCAmelCase_				,				)

														print(F"Pushed {name}"					)
def 					snake_case_						(   lowerCAmelCase_				:  Path				,				lowerCAmelCase_				:  str = None				,				lowerCAmelCase_				:  bool = True					):
							__lowercase					:							Any			=   """imagenet-1k-id2label.json"""
							__lowercase					:							str			=   1000
							__lowercase					:							int			=   (1, num_labels)

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

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

							__lowercase					:							Any			=   partial(lowerCAmelCase_				,				num_labels=lowerCAmelCase_				,				idalabel=lowerCAmelCase_				,				labelaid=lowerCAmelCase_					)

							__lowercase					:							Tuple			=   {
							    """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					),
							}

							__lowercase					:							Optional[int]			=   NameToOurModelFuncMap()
							__lowercase					:							List[str]			=   NameToFromModelFuncMap()
							# add seer weights logic

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

							# pretrained
							__lowercase					:							int			=   partial(
							    lowerCAmelCase_				,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""				,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,				)

							__lowercase					:							Dict			=   partial(
							    lowerCAmelCase_				,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""				,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,				)

							__lowercase					:							Any			=   partial(
							    lowerCAmelCase_				,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""				,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)				,				)

							__lowercase					:							Dict			=   partial(
							    lowerCAmelCase_				,				"""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=2.52					)					)					)				,				)

							# IN1K finetuned
							__lowercase					:							Optional[Any]			=   partial(
							    lowerCAmelCase_				,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,				)

							__lowercase					:							Tuple			=   partial(
							    lowerCAmelCase_				,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)				,				)

							__lowercase					:							Union[str, Any]			=   partial(
							    lowerCAmelCase_				,				"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""				,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)				,				)

							__lowercase					:							int			=   partial(
							    lowerCAmelCase_				,				"""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=2.52					)					)					)				,				)

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


if __name__ == "__main__":
	lowerCamelCase       :       Tuple        =				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       :       List[str]        =				parser.parse_args()

	lowerCamelCase       :       Path        =				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)







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


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

lowerCamelCase       :       List[str]        =				{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''canine'''



					def __init__(     self :		List[str]						,   __a :		Optional[int]=768						,   __a :		Any=12						,   __a :		Any=12						,   __a :		Dict=3072						,   __a :		Dict="gelu"						,   __a :		List[Any]=0.1						,   __a :		List[Any]=0.1						,   __a :		Tuple=16384						,   __a :		List[Any]=16						,   __a :		List[Any]=0.02						,   __a :		Optional[Any]=1E-12						,   __a :		Dict=0						,   __a :		List[Any]=0xe_0_0_0						,   __a :		Optional[int]=0xe_0_0_1						,   __a :		Any=4						,   __a :		Dict=4						,   __a :		Optional[int]=8						,   __a :		Any=16384						,   __a :		Optional[Any]=128						,   **__a :		List[str]						,   )	->     Union[str, Any]:


												"""simple docstring"""

												super().__init__(pad_token_id=__a						,   bos_token_id=__a						,   eos_token_id=__a						,   **__a							)

												__lowercase					:							int			=   max_position_embeddings
												__lowercase					:							List[str]			=   hidden_size
												__lowercase					:							List[Any]			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							int			=   intermediate_size
												__lowercase					:							Dict			=   hidden_act
												__lowercase					:							Tuple			=   hidden_dropout_prob
												__lowercase					:							Union[str, Any]			=   attention_probs_dropout_prob
												__lowercase					:							Union[str, Any]			=   initializer_range
												__lowercase					:							Any			=   type_vocab_size
												__lowercase					:							int			=   layer_norm_eps

												# Character config:
												__lowercase					:							int			=   downsampling_rate
												__lowercase					:							str			=   upsampling_kernel_size
												__lowercase					:							Union[str, Any]			=   num_hash_functions
												__lowercase					:							Optional[Any]			=   num_hash_buckets
												__lowercase					:							Optional[int]			=   local_transformer_stride