





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							:       Optional[Any]					 =	logging.get_logger()





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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   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			, _lowerCAmelCase					):

										"""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 			snake_case_    (  self					):

										"""simple docstring"""




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





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






				def __call__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   Tracker(self.dest					)(_lowerCAmelCase					).parametrized
										__SCREAMING_SNAKE_CASE:				Dict           =   Tracker(self.src					)(_lowerCAmelCase					).parametrized

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   list(filter(lambda _lowerCAmelCase					: type(_lowerCAmelCase					) not in self.src_skip			, _lowerCAmelCase					)					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   list(filter(lambda _lowerCAmelCase					: 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			):






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__()

										__SCREAMING_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}"""
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   len(_lowerCAmelCase					) + 1
																feature_blocks.append((f"""res{block_index}""", v)					)

										__SCREAMING_SNAKE_CASE:				int           =   nn.ModuleDict(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




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





class 				a			(      __lowercase			):






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




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






				def __getitem__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										if x not in self:
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.convert_name_to_timm(_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   partial(lambda: (timm.create_model(_lowerCAmelCase			, pretrained=_lowerCAmelCase					).eval(), None)					)

										else:
																__SCREAMING_SNAKE_CASE:				List[str]           =   super().__getitem__(_lowerCAmelCase					)

										return val





class 				a			(      __lowercase			):






				def __getitem__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										if "seer" in x and "in1k" not in x:
																__SCREAMING_SNAKE_CASE:				List[str]           =   RegNetModel
										else:
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   RegNetForImageClassification
										return val






def 		lowerCAmelCase  (	UpperCamelCase__   :   str       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   List[Tuple[str, str]]					)      ->    int:

						"""simple docstring"""





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






def 		lowerCAmelCase  (	UpperCamelCase__   :   str       ,			UpperCamelCase__   :   Callable[[], nn.Module]       ,			UpperCamelCase__   :   Callable[[], nn.Module]       ,			UpperCamelCase__   :   RegNetConfig       ,			UpperCamelCase__   :   Path       ,			UpperCamelCase__   :   bool = True       ,			)      ->    List[Any]:

						"""simple docstring"""





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

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

						__SCREAMING_SNAKE_CASE:				Any           =   our_model(UpperCamelCase__       ,			output_hidden_states=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Dict           =   (
						    our_outputs.logits if isinstance(UpperCamelCase__       ,			UpperCamelCase__					) else our_outputs.last_hidden_state
						)

						__SCREAMING_SNAKE_CASE:				Any           =   from_model(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				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:
												__SCREAMING_SNAKE_CASE:				List[str]           =   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__       ,			)

												__SCREAMING_SNAKE_CASE:				Optional[int]           =   224 if '''seer''' not in name else 384
												# we can use the convnext one
												__SCREAMING_SNAKE_CASE:				Tuple           =   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 		lowerCAmelCase  (	UpperCamelCase__   :   Path       ,			UpperCamelCase__   :   str = None       ,			UpperCamelCase__   :   bool = True					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[str]           =   '''imagenet-1k-id2label.json'''
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   1_000
						__SCREAMING_SNAKE_CASE:				List[Any]           =   (1, num_labels)

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

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

						__SCREAMING_SNAKE_CASE:				int           =   partial(UpperCamelCase__       ,			num_labels=UpperCamelCase__       ,			idalabel=UpperCamelCase__       ,			labelaid=UpperCamelCase__					)

						__SCREAMING_SNAKE_CASE:				Dict           =   {
						    '''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					),
						}

						__SCREAMING_SNAKE_CASE:				Dict           =   NameToOurModelFuncMap()
						__SCREAMING_SNAKE_CASE:				str           =   NameToFromModelFuncMap()
						# add seer weights logic

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

						# pretrained
						__SCREAMING_SNAKE_CASE:				Any           =   partial(
						    UpperCamelCase__       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)       ,			)

						__SCREAMING_SNAKE_CASE:				Tuple           =   partial(
						    UpperCamelCase__       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)       ,			)

						__SCREAMING_SNAKE_CASE:				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()					)       ,			)

						__SCREAMING_SNAKE_CASE:				Dict           =   partial(
						    UpperCamelCase__       ,			'''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=6_20.83       ,			w_m=2.52					)					)					)       ,			)

						# IN1K finetuned
						__SCREAMING_SNAKE_CASE:				List[Any]           =   partial(
						    UpperCamelCase__       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)       ,			)

						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   partial(
						    UpperCamelCase__       ,			'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''       ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)       ,			)

						__SCREAMING_SNAKE_CASE:				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()					)       ,			)

						__SCREAMING_SNAKE_CASE:				str           =   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=1_010       ,			w_a=1_744       ,			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__":
							lowerCAmelCase							:       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.""",
							)

							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)










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   emb.weight.shape
						__SCREAMING_SNAKE_CASE:				List[str]           =   nn.Linear(UpperCamelCase__       ,			UpperCamelCase__       ,			bias=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   emb.weight.data
						return lin_layer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Namespace(**checkpoint['''cfg''']['''model''']					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   checkpoint['''model''']
						remove_ignore_keys_(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__SCREAMING_SNAKE_CASE:				List[Any]           =   {key.replace('''decoder'''       ,			'''model'''					): val for key, val in state_dict.items()}

						__SCREAMING_SNAKE_CASE:				Dict           =   XGLMConfig(
						    vocab_size=UpperCamelCase__       ,			max_position_embeddings=args.max_target_positions       ,			num_layers=args.decoder_layers       ,			attention_heads=args.decoder_attention_heads       ,			ffn_dim=args.decoder_ffn_embed_dim       ,			d_model=args.decoder_embed_dim       ,			layerdrop=args.decoder_layerdrop       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function='''gelu'''       ,			scale_embedding=not args.no_scale_embedding       ,			tie_word_embeddings=args.share_decoder_input_output_embed       ,			)

						__SCREAMING_SNAKE_CASE:				int           =   XGLMForCausalLM(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model.load_state_dict(UpperCamelCase__       ,			strict=UpperCamelCase__					)
						print(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   make_linear_from_emb(model.model.embed_tokens					)

						return model


if __name__ == "__main__":
							lowerCAmelCase							:       Union[str, Any]					 =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
							parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							lowerCAmelCase							:       List[str]					 =	parser.parse_args()
							lowerCAmelCase							:       Optional[int]					 =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)



