





'''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					    =	logging.get_logger()




@dataclass
class  _A :
		lowercase__:   nn.Module
		lowercase__:   List[nn.Module]				     =   field(default_factory=__lowercase		)
		lowercase__:   list				     =   field(default_factory=__lowercase		)

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Tensor  ,      __magic_name__					:   Tensor			)					->    List[Any]:


					"""simple docstring"""

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

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


					"""simple docstring"""

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

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


					"""simple docstring"""

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





@dataclass
class  _A :
		lowercase__:   nn.Module
		lowercase__:   nn.Module
		lowercase__:   int				     =   1
		lowercase__:   List				     =   field(default_factory=__lowercase		)
		lowercase__:   List				     =   field(default_factory=__lowercase		)
		lowercase__:   bool				     =   True

		def __call__(   self					:   Dict  ,      __magic_name__					:   Tensor			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Any							      =     Tracker(self.dest			)(__magic_name__			).parametrized
					__snake_case					:      Dict							      =     Tracker(self.src			)(__magic_name__			).parametrized

					__snake_case					:      List[str]							      =     list(filter(lambda __magic_name__			: type(__magic_name__			) not in self.src_skip  ,      __magic_name__			)			)
					__snake_case					:      List[str]							      =     list(filter(lambda __magic_name__			: type(__magic_name__			) not in self.dest_skip  ,      __magic_name__			)			)

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

					for dest_m, src_m in zip(__magic_name__  ,      __magic_name__			):
								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		):

		def __init__(   self					:   Dict  ,      __magic_name__					:   nn.Module			)					->    Any:


					"""simple docstring"""

					super().__init__()

					__snake_case					:      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}'''
								__snake_case					:      Optional[int]							      =     len(__magic_name__			) + 1
								feature_blocks.append((f'''res{block_index}''', v)			)

					__snake_case					:      str							      =     nn.ModuleDict(__magic_name__			)

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   Tensor			)					->    Tuple:


					"""simple docstring"""

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





class  _A (					__lowercase		):

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   str			)					->    str:


					"""simple docstring"""

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

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


					"""simple docstring"""

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

					else:
								__snake_case					:      Tuple							      =     super().__getitem__(__magic_name__			)

					return val





class  _A (					__lowercase		):

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


					"""simple docstring"""

					if "seer" in x and "in1k" not in x:
								__snake_case					:      Any							      =     RegNetModel
					else:
								__snake_case					:      Tuple							      =     RegNetForImageClassification
					return val







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = True    ,	) ->				Union[str, Any]:



			"""simple docstring"""


			print(F'''Converting {name}...''' )
			with torch.no_grad():
						__snake_case      ,     __snake_case					:      str							      =     from_model_func()
						__snake_case					:      Optional[Any]							      =     our_model_func(_lowerCamelCase ).eval()
						__snake_case					:      Dict							      =     ModuleTransfer(src=_lowerCamelCase    ,	dest=_lowerCamelCase    ,	raise_if_mismatch=_lowerCamelCase )
						__snake_case					:      Union[str, Any]							      =     torch.randn((1, 3, 224, 224) )
						module_transfer(_lowerCamelCase )

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

			__snake_case					:      str							      =     our_model(_lowerCamelCase    ,	output_hidden_states=_lowerCamelCase )
			__snake_case					:      Any							      =     (
			    our_outputs.logits if isinstance(_lowerCamelCase    ,	_lowerCamelCase ) else our_outputs.last_hidden_state
			)

			__snake_case					:      List[str]							      =     from_model(_lowerCamelCase )
			__snake_case					:      List[Any]							      =     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:
						__snake_case					:      Union[str, Any]							      =     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    ,	)

						__snake_case					:      Optional[int]							      =     224 if """seer""" not in name else 384
						# we can use the convnext one
						__snake_case					:      List[Any]							      =     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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = True ) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      int							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      int							      =     1000
			__snake_case					:      Any							      =     (1, num_labels)

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

			__snake_case					:      Optional[Any]							      =     idalabel
			__snake_case					:      Any							      =     {v: k for k, v in idalabel.items()}

			__snake_case					:      int							      =     partial(_lowerCamelCase    ,	num_labels=_lowerCamelCase    ,	idalabel=_lowerCamelCase    ,	labelaid=_lowerCamelCase )

			__snake_case					:      Optional[Any]							      =     {
			    """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, 1_1110, 2_8280]    ,	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, 1_1110, 2_8280]    ,	groups_width=1010 ),
			}

			__snake_case					:      List[str]							      =     NameToOurModelFuncMap()
			__snake_case					:      List[str]							      =     NameToFromModelFuncMap()
			# add seer weights logic

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

			# pretrained
			__snake_case					:      List[str]							      =     partial(
			    _lowerCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf() )    ,	)

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

			__snake_case					:      Tuple							      =     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() )    ,	)

			__snake_case					:      str							      =     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=6_20.83    ,	w_m=2.52 ) ) )    ,	)

			# IN1K finetuned
			__snake_case					:      Dict							      =     partial(
			    _lowerCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf() )    ,	)

			__snake_case					:      List[Any]							      =     partial(
			    _lowerCamelCase    ,	"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf() )    ,	)

			__snake_case					:      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() )    ,	)

			__snake_case					:      str							      =     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=6_20.83    ,	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__":
	__UpperCamelCase					    =	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					    =	parser.parse_args()

	__UpperCamelCase					    =	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'''






import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME







def 					_a						(       _lowerCamelCase ) ->				List[Any]:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(_lowerCamelCase    ,	_lowerCamelCase )







def 					_a						(       _lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			__snake_case      ,     __snake_case					:      Dict							      =     emb.weight.shape
			__snake_case					:      Optional[int]							      =     nn.Linear(_lowerCamelCase    ,	_lowerCamelCase    ,	bias=_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     emb.weight.data
			return lin_layer







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase=None ) ->				Union[str, Any]:



			"""simple docstring"""


			__snake_case					:      Any							      =     {}
			for old_key in state_dict.keys():
						__snake_case					:      Union[str, Any]							      =     old_key
						if "moe_layer.experts." in key:
									if expert_idx is not None:
												__snake_case					:      Tuple							      =     key.replace("""moe_layer.experts.0"""    ,	F'''ffn.experts.expert_{expert_idx}''' )
									else:
												__snake_case					:      Optional[int]							      =     key.replace("""moe_layer.experts."""    ,	"""ffn.experts.expert_""" )
						if "gate" in key:
									__snake_case					:      Dict							      =     key.replace(""".moe_layer.gate.wg"""    ,	""".ffn.router.classifier""" )
						if "fc2" and "experts" not in key:
									__snake_case					:      Union[str, Any]							      =     key.replace(""".fc2."""    ,	""".ffn.fc2.""" )
						if "fc1" and "experts" not in key:
									__snake_case					:      Optional[int]							      =     key.replace(""".fc1."""    ,	""".ffn.fc1.""" )
						if ".encoder_attn." in key:
									__snake_case					:      Tuple							      =     key.replace(""".encoder_attn."""    ,	""".cross_attention.""" )
						if "encoder_attn_layer_norm" in key:
									__snake_case					:      Union[str, Any]							      =     key.replace("""encoder_attn_layer_norm"""    ,	"""cross_attention_layer_norm""" )
						if "final_layer_norm" in key:
									__snake_case					:      str							      =     key.replace("""final_layer_norm"""    ,	"""ff_layer_norm""" )
						__snake_case					:      str							      =     state_dict[old_key]
			return new_dict







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = WEIGHTS_NAME ) ->				Dict:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     []
			__snake_case					:      Dict							      =     0
			os.makedirs(_lowerCamelCase    ,	exist_ok=_lowerCamelCase )

			for expert in range(_lowerCamelCase ):
						__snake_case					:      Tuple							      =     switch_checkpoint_path + F'''-rank-{expert}.pt'''
						if os.path.isfile(_lowerCamelCase ):
									__snake_case					:      Dict							      =     torch.load(_lowerCamelCase )["""model"""]
									remove_ignore_keys_(_lowerCamelCase )
									__snake_case					:      Optional[Any]							      =     rename_fairseq_keys(_lowerCamelCase    ,	_lowerCamelCase )
									__snake_case					:      List[Any]							      =     os.path.join(
									    _lowerCamelCase    ,	weights_name.replace(""".bin"""    ,	F'''-{len(_lowerCamelCase )+1:05d}-of-???.bin''' ) )
									torch.save(_lowerCamelCase    ,	_lowerCamelCase )
									sharded_state_dicts.append(expert_state.keys() )
									total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
									    expert_state[list(_lowerCamelCase )[0]].dtype )

    # Add the last block
			__snake_case					:      Optional[Any]							      =     os.path.join(_lowerCamelCase    ,	weights_name.replace(""".bin"""    ,	F'''-{len(_lowerCamelCase )+1:05d}-of-???.bin''' ) )
			__snake_case					:      str							      =     torch.load(switch_checkpoint_path + """-shared.pt""" )["""model"""]
			remove_ignore_keys_(_lowerCamelCase )
			__snake_case					:      Optional[Any]							      =     rename_fairseq_keys(_lowerCamelCase    ,	_lowerCamelCase )
			__snake_case					:      List[str]							      =     shared_weights["""decoder.embed_tokens.weight"""]
			sharded_state_dicts.append(shared_weights.keys() )

			# If we only have the shared weights (dummy model/experts saved on the same file)
			if len(_lowerCamelCase ) == 1:
						__snake_case					:      Optional[Any]							      =     os.path.join(_lowerCamelCase    ,	_lowerCamelCase )
						torch.save(_lowerCamelCase    ,	_lowerCamelCase )
						return {weights_name: sharded_state_dicts[0]}, None
			else:
						torch.save(_lowerCamelCase    ,	_lowerCamelCase )
			# Otherwise, let's build the index
			__snake_case					:      Tuple							      =     {}
			for idx, shard in enumerate(_lowerCamelCase ):
						__snake_case					:      Any							      =     weights_name.replace(""".bin"""    ,	F'''-{idx+1:05d}-of-{len(_lowerCamelCase ):05d}.bin''' )
						__snake_case					:      int							      =     os.path.join(_lowerCamelCase    ,	weights_name.replace(""".bin"""    ,	F'''-{idx+1:05d}-of-???.bin''' ) )
						os.rename(_lowerCamelCase    ,	os.path.join(_lowerCamelCase    ,	_lowerCamelCase ) )
						for key in shard:
									__snake_case					:      str							      =     shard_file

    # Add the metadata
			__snake_case					:      Optional[Any]							      =     {"""total_size""": total_size}
			__snake_case					:      int							      =     {"""metadata""": metadata, """weight_map""": weight_map}

			with open(os.path.join(_lowerCamelCase    ,	_lowerCamelCase )    ,	"""w"""    ,	encoding="""utf-8""" ) as f:
						__snake_case					:      Union[str, Any]							      =     json.dumps(_lowerCamelCase    ,	indent=2    ,	sort_keys=_lowerCamelCase ) + """\n"""
						f.write(_lowerCamelCase )

			return metadata, index


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--nllb_moe_checkpoint_path",
	    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
	    type=str,
	    required=False,
	    help="Path to a directory containing a folder per layer. Follows the original Google format.",
	)
	parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
	    type=str,
	    required=False,
	    help="Path to the output pytorch model.",
	)
	__UpperCamelCase					    =	parser.parse_args()
	__UpperCamelCase    ,		__UpperCamelCase					    =	shard_on_the_fly(
	    args.nllb_moe_checkpoint_path,
	    args.pytorch_dump_folder_path,
	    128,
	    args.dtype,
	)

	__UpperCamelCase					    =	NllbMoeConfig.from_pretrained(
	    "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
	)
	config.save_pretrained(args.pytorch_dump_folder_path)
	__UpperCamelCase					    =	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
	print("Done")
	model.save_pretrained(args.pytorch_dump_folder_path)


