



'''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__       :	Optional[int]    = logging.get_logger()






@dataclass
class 						_UpperCamelCase  :


			'''simple docstring'''

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

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Tensor							,     lowerCAmelCase__     : Tensor						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		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     : Tuple							,     lowerCAmelCase__     : Tensor						):





					"""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     : Union[str, Any]						):





					"""simple docstring"""






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






@dataclass
class 						_UpperCamelCase  :


			'''simple docstring'''

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

			def __call__(							self     : Union[str, Any]							,     lowerCAmelCase__     : Tensor						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		Tracker(self.dest						)(lowerCAmelCase__						).parametrized
					__SCREAMING_SNAKE_CASE       :		Dict           =		Tracker(self.src						)(lowerCAmelCase__						).parametrized

					__SCREAMING_SNAKE_CASE       :		Any           =		list(filter(lambda lowerCAmelCase__						: type(lowerCAmelCase__						) not in self.src_skip							,     lowerCAmelCase__						)						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		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 						_UpperCamelCase  (							nn.Module    ):


			'''simple docstring'''

			def __init__(							self     : List[Any]							,     lowerCAmelCase__     : nn.Module						):





					"""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       :		Any           =		len(lowerCAmelCase__						) + 1
							feature_blocks.append((F"res{block_index}", v)						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.ModuleDict(lowerCAmelCase__						)



			def UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : Tensor						):





					"""simple docstring"""






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






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






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



			def __getitem__(							self     : Optional[Any]							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					if x not in self:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		self.convert_name_to_timm(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		int           =		partial(lambda: (timm.create_model(lowerCAmelCase__							,     pretrained=lowerCAmelCase__						).eval(), None)						)

					else:
							__SCREAMING_SNAKE_CASE       :		Dict           =		super().__getitem__(lowerCAmelCase__						)

					return val






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			def __getitem__(							self     : Optional[Any]							,     lowerCAmelCase__     : str						):





					"""simple docstring"""






					if "seer" in x and "in1k" not in x:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		RegNetModel
					else:
							__SCREAMING_SNAKE_CASE       :		int           =		RegNetForImageClassification
					return val


def 				lowerCAmelCase_   (	_lowerCamelCase:   Dict       ,      _lowerCamelCase:   int       ,      _lowerCamelCase:   List[Tuple[str, str]]					):
		for from_key, to_key in keys:
				__SCREAMING_SNAKE_CASE       :		Any           =		from_state_dict[from_key].clone()
				print(F"Copied key={from_key} to={to_key}"					)
		return to_state_dict


def 				lowerCAmelCase_   (	_lowerCamelCase:   str       ,      _lowerCamelCase:   Callable[[], nn.Module]       ,      _lowerCamelCase:   Callable[[], nn.Module]       ,      _lowerCamelCase:   RegNetConfig       ,      _lowerCamelCase:   Path       ,      _lowerCamelCase:   bool = True       ,      ):
		print(F"Converting {name}..."					)
		with torch.no_grad():
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		int           =		from_model_func()
				__SCREAMING_SNAKE_CASE       :		int           =		our_model_func(_lowerCamelCase					).eval()
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ModuleTransfer(src=_lowerCamelCase       ,      dest=_lowerCamelCase       ,      raise_if_mismatch=_lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		str           =		torch.randn((1, 3, 2_24, 2_24)					)
				module_transfer(_lowerCamelCase					)

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

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

		__SCREAMING_SNAKE_CASE       :		str           =		from_model(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		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:
				__SCREAMING_SNAKE_CASE       :		Dict           =		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       ,      )

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		2_24 if """seer""" not in name else 3_84
				# we can use the convnext one
				__SCREAMING_SNAKE_CASE       :		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 				lowerCAmelCase_   (	_lowerCamelCase:   Path       ,      _lowerCamelCase:   str = None       ,      _lowerCamelCase:   bool = True					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		"""imagenet-1k-id2label.json"""
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		10_00
		__SCREAMING_SNAKE_CASE       :		str           =		(1, num_labels)

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

		__SCREAMING_SNAKE_CASE       :		Dict           =		idalabel
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		{v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		partial(_lowerCamelCase       ,      num_labels=_lowerCamelCase       ,      idalabel=_lowerCamelCase       ,      labelaid=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		int           =		{
		    """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					),
		}

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

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

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

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

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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()					)       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		partial(
		    _lowerCamelCase       ,      """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
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		partial(
		    _lowerCamelCase       ,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""       ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		partial(
		    _lowerCamelCase       ,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""       ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)       ,      )

		__SCREAMING_SNAKE_CASE       :		str           =		partial(
		    _lowerCamelCase       ,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""       ,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					)       ,      )

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		partial(
		    _lowerCamelCase       ,      """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""       ,      lambda: FakeRegNetVisslWrapper(
		        RegNet(RegNetParams(depth=27       ,      group_width=10_10       ,      w_a=17_44       ,      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__       :	Optional[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__       :	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'''




import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


UpperCamelCase__       :	List[Any]    = '''base_with_context'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple       ,      _lowerCamelCase:   Dict					):
		__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)
		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]					)					)
		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   List[str]					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]					)					)

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T					)					)
		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T					)					)

		for lyr_num, lyr in enumerate(model.decoders					):
				__SCREAMING_SNAKE_CASE       :		str           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		ly_weight["""self_attention"""]
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""MultiHeadDotProductAttention_0"""]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]					)					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T					)					)

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		int           =		checkpoints.load_tax_checkpoint(args.checkpoint_path					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		jnp.tree_util.tree_map(onp.array       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		int           =		[
		    """from __gin__ import dynamic_registration""",
		    """from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
		    """diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
		    """diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
		]

		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		os.path.join(args.checkpoint_path       ,      """.."""       ,      """config.gin"""					)
		__SCREAMING_SNAKE_CASE       :		Any           =		inference.parse_training_gin_file(_lowerCamelCase       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		inference.InferenceModel(args.checkpoint_path       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""       ,      variance_type="""fixed_large"""					)

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		SpectrogramNotesEncoder(
		    max_length=synth_model.sequence_length["""inputs"""]       ,      vocab_size=synth_model.model.module.config.vocab_size       ,      d_model=synth_model.model.module.config.emb_dim       ,      dropout_rate=synth_model.model.module.config.dropout_rate       ,      num_layers=synth_model.model.module.config.num_encoder_layers       ,      num_heads=synth_model.model.module.config.num_heads       ,      d_kv=synth_model.model.module.config.head_dim       ,      d_ff=synth_model.model.module.config.mlp_dim       ,      feed_forward_proj="""gated-gelu"""       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		SpectrogramContEncoder(
		    input_dims=synth_model.audio_codec.n_dims       ,      targets_context_length=synth_model.sequence_length["""targets_context"""]       ,      d_model=synth_model.model.module.config.emb_dim       ,      dropout_rate=synth_model.model.module.config.dropout_rate       ,      num_layers=synth_model.model.module.config.num_encoder_layers       ,      num_heads=synth_model.model.module.config.num_heads       ,      d_kv=synth_model.model.module.config.head_dim       ,      d_ff=synth_model.model.module.config.mlp_dim       ,      feed_forward_proj="""gated-gelu"""       ,      )

		__SCREAMING_SNAKE_CASE       :		Tuple           =		TaFilmDecoder(
		    input_dims=synth_model.audio_codec.n_dims       ,      targets_length=synth_model.sequence_length["""targets_context"""]       ,      max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time       ,      d_model=synth_model.model.module.config.emb_dim       ,      num_layers=synth_model.model.module.config.num_decoder_layers       ,      num_heads=synth_model.model.module.config.num_heads       ,      d_kv=synth_model.model.module.config.head_dim       ,      d_ff=synth_model.model.module.config.mlp_dim       ,      dropout_rate=synth_model.model.module.config.dropout_rate       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		load_decoder(ta_checkpoint["""target"""]["""decoder"""]       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		SpectrogramDiffusionPipeline(
		    notes_encoder=_lowerCamelCase       ,      continuous_encoder=_lowerCamelCase       ,      decoder=_lowerCamelCase       ,      scheduler=_lowerCamelCase       ,      melgan=_lowerCamelCase       ,      )
		if args.save:
				pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
						UpperCamelCase__       :	Tuple    = argparse.ArgumentParser()

						parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
						parser.add_argument(
						    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
						)
						parser.add_argument(
						    '''--checkpoint_path''',
						    default=f"{MODEL}/checkpoint_500000",
						    type=str,
						    required=False,
						    help='''Path to the original jax model checkpoint.''',
						)
						UpperCamelCase__       :	List[str]    = parser.parse_args()

						main(args)