


"""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()
__SCREAMING_SNAKE_CASE							:      int						=      logging.get_logger()





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						nn.Module
							__lowercase:						List[nn.Module]							 =      field(default_factory=snake_case__)
							__lowercase:						list							 =      field(default_factory=snake_case__)



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: int						,    UpperCAmelCase_							: Tensor						,    UpperCAmelCase_							: Tensor    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						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							: int						,    UpperCAmelCase_							: Tensor    )   ->Optional[int]:



														"""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 						lowerCAmelCase    (			self							: Tuple    )   ->List[str]:



														"""simple docstring"""



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





@dataclass
class        __A       :





							'''simple docstring'''

							__lowercase:						nn.Module
							__lowercase:						nn.Module
							__lowercase:						int							 =      1
							__lowercase:						List							 =      field(default_factory=snake_case__)
							__lowercase:						List							 =      field(default_factory=snake_case__)
							__lowercase:						bool							 =      True



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



														"""simple docstring"""



														snake_case_          =						Tracker(self.dest    )(UpperCAmelCase_    ).parametrized
														snake_case_          =						Tracker(self.src    )(UpperCAmelCase_    ).parametrized

														snake_case_          =						list(filter(lambda UpperCAmelCase_    : type(UpperCAmelCase_    ) not in self.src_skip						,    UpperCAmelCase_    )    )
														snake_case_          =						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):





							'''simple docstring'''

							def __init__(			self							: Optional[int]						,    UpperCAmelCase_							: nn.Module    )   ->Union[str, Any]:



														"""simple docstring"""



														super().__init__()

														snake_case_          =						[]
														# - 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}"""
																					snake_case_          =						len(UpperCAmelCase_    ) + 1
																					feature_blocks.append((F"""res{block_index}""", v)    )

														snake_case_          =						nn.ModuleDict(UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: List[Any]						,    UpperCAmelCase_							: Tensor    )   ->Union[str, Any]:



														"""simple docstring"""



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





class        __A       (snake_case__):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: Any						,    UpperCAmelCase_							: str    )   ->str:



														"""simple docstring"""



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



							def __getitem__(			self							: Optional[int]						,    UpperCAmelCase_							: str    )   ->Callable[[], Tuple[nn.Module, Dict]]:



														"""simple docstring"""



														if x not in self:
																					snake_case_          =						self.convert_name_to_timm(UpperCAmelCase_    )
																					snake_case_          =						partial(lambda: (timm.create_model(UpperCAmelCase_						,    pretrained=UpperCAmelCase_    ).eval(), None)    )

														else:
																					snake_case_          =						super().__getitem__(UpperCAmelCase_    )

														return val





class        __A       (snake_case__):





							'''simple docstring'''

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



														"""simple docstring"""



														if "seer" in x and "in1k" not in x:
																					snake_case_          =						RegNetModel
														else:
																					snake_case_          =						RegNetForImageClassification
														return val







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Tuple:
							for from_key, to_key in keys:
														snake_case_          =						from_state_dict[from_key].clone()
														print(f"""Copied key={from_key} to={to_key}"""			)
							return to_state_dict







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = True			,	)      ->			Any:
							print(f"""Converting {name}..."""			)
							with torch.no_grad():
														snake_case_    ,  snake_case_          =						from_model_func()
														snake_case_          =						our_model_func(_SCREAMING_SNAKE_CASE			).eval()
														snake_case_          =						ModuleTransfer(src=_SCREAMING_SNAKE_CASE			,	dest=_SCREAMING_SNAKE_CASE			,	raise_if_mismatch=_SCREAMING_SNAKE_CASE			)
														snake_case_          =						torch.randn((1, 3, 224, 224)			)
														module_transfer(_SCREAMING_SNAKE_CASE			)

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

							snake_case_          =						our_model(_SCREAMING_SNAKE_CASE			,	output_hidden_states=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						(
							    our_outputs.logits if isinstance(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			) else our_outputs.last_hidden_state
							)

							snake_case_          =						from_model(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						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:
														snake_case_          =						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			,	)

														snake_case_          =						224 if """seer""" not in name else 384
														# we can use the convnext one
														snake_case_          =						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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE = None			,	_SCREAMING_SNAKE_CASE = True			)      ->			Tuple:
							snake_case_          =						"""imagenet-1k-id2label.json"""
							snake_case_          =						1_000
							snake_case_          =						(1, num_labels)

							snake_case_          =						"""huggingface/label-files"""
							snake_case_          =						num_labels
							snake_case_          =						json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	repo_type="""dataset"""			)			)			,	"""r"""			)			)
							snake_case_          =						{int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}

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

							snake_case_          =						partial(_SCREAMING_SNAKE_CASE			,	num_labels=_SCREAMING_SNAKE_CASE			,	idalabel=_SCREAMING_SNAKE_CASE			,	labelaid=_SCREAMING_SNAKE_CASE			)

							snake_case_          =						{
							    """regnet-x-002""": ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]			,	hidden_sizes=[24, 56, 152, 368]			,	groups_width=8			,	layer_type="""x"""			),
							    """regnet-x-004""": ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 12]			,	hidden_sizes=[32, 64, 160, 384]			,	groups_width=16			,	layer_type="""x"""			),
							    """regnet-x-006""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]			,	hidden_sizes=[48, 96, 240, 528]			,	groups_width=24			,	layer_type="""x"""			),
							    """regnet-x-008""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]			,	hidden_sizes=[64, 128, 288, 672]			,	groups_width=16			,	layer_type="""x"""			),
							    """regnet-x-016""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 2]			,	hidden_sizes=[72, 168, 408, 912]			,	groups_width=24			,	layer_type="""x"""			),
							    """regnet-x-032""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 15, 2]			,	hidden_sizes=[96, 192, 432, 1_008]			,	groups_width=48			,	layer_type="""x"""			),
							    """regnet-x-040""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]			,	hidden_sizes=[80, 240, 560, 1_360]			,	groups_width=40			,	layer_type="""x"""			),
							    """regnet-x-064""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]			,	hidden_sizes=[168, 392, 784, 1_624]			,	groups_width=56			,	layer_type="""x"""			),
							    """regnet-x-080""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]			,	hidden_sizes=[80, 240, 720, 1_920]			,	groups_width=120			,	layer_type="""x"""			),
							    """regnet-x-120""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]			,	hidden_sizes=[224, 448, 896, 2_240]			,	groups_width=112			,	layer_type="""x"""			),
							    """regnet-x-160""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]			,	hidden_sizes=[256, 512, 896, 2_048]			,	groups_width=128			,	layer_type="""x"""			),
							    """regnet-x-320""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]			,	hidden_sizes=[336, 672, 1_344, 2_520]			,	groups_width=168			,	layer_type="""x"""			),
							    # y variant
							    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]			,	hidden_sizes=[24, 56, 152, 368]			,	groups_width=8			),
							    """regnet-y-004""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]			,	hidden_sizes=[48, 104, 208, 440]			,	groups_width=8			),
							    """regnet-y-006""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]			,	hidden_sizes=[48, 112, 256, 608]			,	groups_width=16			),
							    """regnet-y-008""": ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]			,	hidden_sizes=[64, 128, 320, 768]			,	groups_width=16			),
							    """regnet-y-016""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 17, 2]			,	hidden_sizes=[48, 120, 336, 888]			,	groups_width=24			),
							    """regnet-y-032""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 13, 1]			,	hidden_sizes=[72, 216, 576, 1_512]			,	groups_width=24			),
							    """regnet-y-040""": ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]			,	hidden_sizes=[128, 192, 512, 1_088]			,	groups_width=64			),
							    """regnet-y-064""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]			,	hidden_sizes=[144, 288, 576, 1_296]			,	groups_width=72			),
							    """regnet-y-080""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]			,	hidden_sizes=[168, 448, 896, 2_016]			,	groups_width=56			),
							    """regnet-y-120""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]			,	hidden_sizes=[224, 448, 896, 2_240]			,	groups_width=112			),
							    """regnet-y-160""": ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]			,	hidden_sizes=[224, 448, 1_232, 3_024]			,	groups_width=112			),
							    """regnet-y-320""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]			,	hidden_sizes=[232, 696, 1_392, 3_712]			,	groups_width=232			),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]			,	hidden_sizes=[232, 696, 1_392, 3_712]			,	groups_width=232			),
							    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]			,	hidden_sizes=[328, 984, 1_968, 4_920]			,	groups_width=328			),
							    """regnet-y-1280-seer""": RegNetConfig(
							        depths=[2, 7, 17, 1]			,	hidden_sizes=[528, 1_056, 2_904, 7_392]			,	groups_width=264			),
							    """regnet-y-2560-seer""": RegNetConfig(
							        depths=[3, 7, 16, 1]			,	hidden_sizes=[640, 1_696, 2_544, 5_088]			,	groups_width=640			),
							    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]			,	hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,	groups_width=1_010			),
							    # finetuned on imagenet
							    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]			,	hidden_sizes=[232, 696, 1_392, 3_712]			,	groups_width=232			),
							    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]			,	hidden_sizes=[328, 984, 1_968, 4_920]			,	groups_width=328			),
							    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]			,	hidden_sizes=[528, 1_056, 2_904, 7_392]			,	groups_width=264			),
							    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]			,	hidden_sizes=[640, 1_696, 2_544, 5_088]			,	groups_width=640			),
							    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]			,	hidden_sizes=[2_020, 4_040, 11_110, 28_280]			,	groups_width=1_010			),
							}

							snake_case_          =						NameToOurModelFuncMap()
							snake_case_          =						NameToFromModelFuncMap()
							# add seer weights logic

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

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

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

							snake_case_          =						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()			)			,	)

							snake_case_          =						partial(
							    _SCREAMING_SNAKE_CASE			,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""			,	lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27			,	group_width=1_010			,	w_a=1_744			,	w_a=620.83			,	w_m=2.52			)			)			)			,	)

							# IN1K finetuned
							snake_case_          =						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()			)			,	)

							snake_case_          =						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()			)			,	)

							snake_case_          =						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()			)			,	)

							snake_case_          =						partial(
							    _SCREAMING_SNAKE_CASE			,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""			,	lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27			,	group_width=1_010			,	w_a=1_744			,	w_a=620.83			,	w_m=2.52			)			)			)			,	)

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      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.',
				)

				__SCREAMING_SNAKE_CASE							:      str						=      parser.parse_args()

				__SCREAMING_SNAKE_CASE							:      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)







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      int						=      {
    '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        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						List[str]							 =      """canine"""



							def __init__(			self							: Union[str, Any]						,    UpperCAmelCase_							: str=768						,    UpperCAmelCase_							: Union[str, Any]=12						,    UpperCAmelCase_							: Optional[Any]=12						,    UpperCAmelCase_							: Optional[Any]=3_072						,    UpperCAmelCase_							: List[Any]="gelu"						,    UpperCAmelCase_							: int=0.1						,    UpperCAmelCase_							: Any=0.1						,    UpperCAmelCase_							: List[str]=16_384						,    UpperCAmelCase_							: Tuple=16						,    UpperCAmelCase_							: int=0.02						,    UpperCAmelCase_							: Tuple=1E-12						,    UpperCAmelCase_							: str=0						,    UpperCAmelCase_							: int=0XE000						,    UpperCAmelCase_							: Optional[int]=0XE001						,    UpperCAmelCase_							: Dict=4						,    UpperCAmelCase_							: List[Any]=4						,    UpperCAmelCase_							: List[Any]=8						,    UpperCAmelCase_							: Dict=16_384						,    UpperCAmelCase_							: Optional[int]=128						,    **UpperCAmelCase_							: Any						,    )   ->int:



														"""simple docstring"""



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

														snake_case_          =						max_position_embeddings
														snake_case_          =						hidden_size
														snake_case_          =						num_hidden_layers
														snake_case_          =						num_attention_heads
														snake_case_          =						intermediate_size
														snake_case_          =						hidden_act
														snake_case_          =						hidden_dropout_prob
														snake_case_          =						attention_probs_dropout_prob
														snake_case_          =						initializer_range
														snake_case_          =						type_vocab_size
														snake_case_          =						layer_norm_eps

														# Character config:
														snake_case_          =						downsampling_rate
														snake_case_          =						upsampling_kernel_size
														snake_case_          =						num_hash_functions
														snake_case_          =						num_hash_buckets
														snake_case_          =						local_transformer_stride



