



'''simple docstring'''




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
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[Any]							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : Tensor							,     lowerCAmelCase__     : Tensor						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		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     : Optional[Any]							,     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     : Dict						):





					"""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			    =		0
			_A      :		List			    =		field(default_factory=lowerCamelCase__    )
			_A      :		List			    =		field(default_factory=lowerCamelCase__    )

			def __call__(							self     : Any							,     lowerCAmelCase__     : Tensor						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		Tracker(self.dest						)(lowerCAmelCase__						).parametrized
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		Tracker(self.src						)(lowerCAmelCase__						).parametrized

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

					if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
							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}"						)


def 				lowerCAmelCase_   (	_lowerCamelCase:   str       ,      _lowerCamelCase:   ResNetConfig       ,      _lowerCamelCase:   Path       ,      _lowerCamelCase:   bool = True					):
		print(F"Converting {name}..."					)
		with torch.no_grad():
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		timm.create_model(_lowerCamelCase       ,      pretrained=_lowerCamelCase					).eval()
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		ResNetForImageClassification(_lowerCamelCase					).eval()
				__SCREAMING_SNAKE_CASE       :		Tuple           =		ModuleTransfer(src=_lowerCamelCase       ,      dest=_lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		Any           =		torch.randn((1, 3, 2_24, 2_24)					)
				module_transfer(_lowerCamelCase					)

		assert torch.allclose(from_model(_lowerCamelCase					)       ,      our_model(_lowerCamelCase					).logits					), "The model logits don't match the original one."

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		F"resnet{'-'.join(name.split('resnet'					)					)}"
		print(_lowerCamelCase					)

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name       ,      commit_message="""Add model"""       ,      use_temp_dir=_lowerCamelCase       ,      )

				# we can use the convnext one
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""					)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name       ,      commit_message="""Add image processor"""       ,      use_temp_dir=_lowerCamelCase       ,      )

				print(F"Pushed {checkpoint_name}"					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   Path       ,      _lowerCamelCase:   str = None       ,      _lowerCamelCase:   bool = True					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		"""imagenet-1k-id2label.json"""
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		10_00
		__SCREAMING_SNAKE_CASE       :		Tuple           =		(1, num_labels)

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

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

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		partial(_lowerCamelCase       ,      num_labels=_lowerCamelCase       ,      idalabel=_lowerCamelCase       ,      labelaid=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		{
		    """resnet18""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]       ,      hidden_sizes=[64, 1_28, 2_56, 5_12]       ,      layer_type="""basic"""					),
		    """resnet26""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]       ,      hidden_sizes=[2_56, 5_12, 10_24, 20_48]       ,      layer_type="""bottleneck"""					),
		    """resnet34""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]       ,      hidden_sizes=[64, 1_28, 2_56, 5_12]       ,      layer_type="""basic"""					),
		    """resnet50""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]       ,      hidden_sizes=[2_56, 5_12, 10_24, 20_48]       ,      layer_type="""bottleneck"""					),
		    """resnet101""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 23, 3]       ,      hidden_sizes=[2_56, 5_12, 10_24, 20_48]       ,      layer_type="""bottleneck"""					),
		    """resnet152""": ImageNetPreTrainedConfig(
		        depths=[3, 8, 36, 3]       ,      hidden_sizes=[2_56, 5_12, 10_24, 20_48]       ,      layer_type="""bottleneck"""					),
		}

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


if __name__ == "__main__":
						UpperCamelCase__       :	Optional[int]    = 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 resnet* architecture,'''
						        ''' currently: resnet18,26,34,50,101,152. 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__       :	Union[str, Any]    = 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)