


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






@dataclass
class      A:

		"""simple docstring"""





		A    =							42
		A    =							field(default_factory=_snake_case		)
		A    =							field(default_factory=_snake_case		)
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	len(list(m.modules()   )   ) == 1 or isinstance(__lowerCamelCase					,				nn.Convad   ) or isinstance(__lowerCamelCase					,				nn.BatchNormad   )
					if has_not_submodules:
								self.traced.append(__lowerCamelCase   )
		def __call__( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



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





		@property
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



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






@dataclass
class      A:

		"""simple docstring"""





		A    =							42
		A    =							42
		A    =							1
		A    =							field(default_factory=_snake_case		)
		A    =							field(default_factory=_snake_case		)
		A    =							True
		def __call__( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	Tracker(self.dest   )(__lowerCamelCase   ).parametrized
					_UpperCamelCase					:str					     =	Tracker(self.src   )(__lowerCamelCase   ).parametrized

					_UpperCamelCase					:Optional[Any]					     =	list(filter(lambda SCREAMING_SNAKE_CASE__   : type(__lowerCamelCase   ) not in self.src_skip					,				__lowerCamelCase   )   )
					_UpperCamelCase					:List[Any]					     =	list(filter(lambda SCREAMING_SNAKE_CASE__   : type(__lowerCamelCase   ) not in self.dest_skip					,				__lowerCamelCase   )   )

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

					for dest_m, src_m in zip(__lowerCamelCase					,				__lowerCamelCase   ):
								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					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					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					:Tuple					     =	len(__lowerCamelCase   ) + 1
								feature_blocks.append((f"res{block_index}", v)   )

					_UpperCamelCase					:Optional[Any]					     =	nn.ModuleDict(__lowerCamelCase   )





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



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






class      A(  _snake_case		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



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





		def __getitem__( self					,				SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					if x not in self:
								_UpperCamelCase					:Tuple					     =	self.convert_name_to_timm(__lowerCamelCase   )
								_UpperCamelCase					:Any					     =	partial(lambda: (timm.create_model(__lowerCamelCase					,				pretrained=__lowerCamelCase   ).eval(), None)   )

					else:
								_UpperCamelCase					:Optional[int]					     =	super().__getitem__(__lowerCamelCase   )

					return val






class      A(  _snake_case		):

		"""simple docstring"""





		def __getitem__( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					if "seer" in x and "in1k" not in x:
								_UpperCamelCase					:Dict					     =	RegNetModel
					else:
								_UpperCamelCase					:int					     =	RegNetForImageClassification
					return val





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Any:
			for from_key, to_key in keys:
						_UpperCamelCase					:str					     =	from_state_dict[from_key].clone()
						print(f"Copied key={from_key} to={to_key}"		)
			return to_state_dict





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__ = True       ,					)					->  List[str]:
			print(f"Converting {name}..."		)
			with torch.no_grad():
						_UpperCamelCase					:Union[str, Any]					     =	from_model_func()
						_UpperCamelCase					:Any					     =	our_model_func(UpperCamelCase_		).eval()
						_UpperCamelCase					:Optional[int]					     =	ModuleTransfer(src=UpperCamelCase_       ,					dest=UpperCamelCase_       ,					raise_if_mismatch=UpperCamelCase_		)
						_UpperCamelCase					:Tuple					     =	torch.randn((1, 3, 2_24, 2_24)		)
						module_transfer(UpperCamelCase_		)

			if from_state_dict is not None:
						_UpperCamelCase					:Tuple					     =	[]
						# 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					:Dict					     =	manually_copy_vissl_head(UpperCamelCase_       ,					our_model.state_dict()       ,					UpperCamelCase_		)
						our_model.load_state_dict(UpperCamelCase_		)

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

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

						print(f"Pushed {name}"		)





def 				A_       ( snake_case__       ,					snake_case__ = None       ,					snake_case__ = True		)					->  List[str]:
			_UpperCamelCase					:int					     =	'''imagenet-1k-id2label.json'''
			_UpperCamelCase					:Dict					     =	10_00
			_UpperCamelCase					:Tuple					     =	(1, num_labels)

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

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

			_UpperCamelCase					:Dict					     =	partial(UpperCamelCase_       ,					num_labels=UpperCamelCase_       ,					idalabel=UpperCamelCase_       ,					labelaid=UpperCamelCase_		)

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

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

			def load_using_classy_vision(snake_case__       ,					snake_case__		) -> Tuple[nn.Module, Dict]:
						_UpperCamelCase					:Dict					     =	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					:List[Any]					     =	files['''classy_state_dict''']['''base_model''']['''model''']
						_UpperCamelCase					:List[Any]					     =	model_state_dict['''trunk''']
						model.load_state_dict(UpperCamelCase_		)
						return model.eval(), model_state_dict["heads"]

			# pretrained
			_UpperCamelCase					:Optional[Any]					     =	partial(
			    UpperCamelCase_       ,					'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''       ,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()		)       ,					)

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

			_UpperCamelCase					:List[str]					     =	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=10_10       ,					w_a=17_44       ,					w_a=6_20.83       ,					w_m=2.52		)		)		)       ,					)

			# IN1K finetuned
			_UpperCamelCase					:List[str]					     =	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					:List[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					:List[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=10_10       ,					w_a=17_44       ,					w_a=6_20.83       ,					w_m=2.52		)		)		)       ,					)

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


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

					UpperCamelCase__				:Tuple			   =	parser.parse_args()

					UpperCamelCase__				:Optional[Any]			   =	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 typing import List, Optional

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


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"autoformer"
 UpperCAmelCase				=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:List[str]      =      embedding_dimension
    else:
       UpperCAmelCase__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

    # Transformer architecture configuration
    UpperCAmelCase__	:Optional[Any]      =      input_size * len(self.lags_sequence ) + self._number_of_features
    UpperCAmelCase__	:Optional[Any]      =      d_model
    UpperCAmelCase__	:Any      =      encoder_attention_heads
    UpperCAmelCase__	:Any      =      decoder_attention_heads
    UpperCAmelCase__	:int      =      encoder_ffn_dim
    UpperCAmelCase__	:Union[str, Any]      =      decoder_ffn_dim
    UpperCAmelCase__	:Optional[Any]      =      encoder_layers
    UpperCAmelCase__	:str      =      decoder_layers

    UpperCAmelCase__	:Optional[Any]      =      dropout
    UpperCAmelCase__	:str      =      attention_dropout
    UpperCAmelCase__	:int      =      activation_dropout
    UpperCAmelCase__	:Optional[Any]      =      encoder_layerdrop
    UpperCAmelCase__	:Dict      =      decoder_layerdrop

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

    super().__init__(is_encoder_decoder=__lowerCamelCase			,       **__lowerCamelCase )





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return (
        sum(self.embedding_dimension )
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )



