



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


lowerCAmelCase__       =  "base_with_context"


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


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

								lowercase__     :    Union[str, Any]													=     ly_weight["attention"]
								lowercase__     :    List[Any]													=     nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
								lowercase__     :    str													=     nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
								lowercase__     :    Optional[Any]													=     nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
								lowercase__     :    List[str]													=     nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)

								lowercase__     :    int													=     nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

								lowercase__     :    Dict													=     nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
								lowercase__     :    Any													=     nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
								lowercase__     :    Any													=     nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

				lowercase__     :    Union[str, Any]													=     nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)
				return model


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


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

				lowercase__     :    Union[str, Any]													=     nn.Parameter(
				    torch.FloatTensor(weights["Embed_0"]["embedding"]						)     , requires_grad=_snake_case						)

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

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

								lowercase__     :    Dict													=     nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
								lowercase__     :    Optional[int]													=     nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
								lowercase__     :    Tuple													=     nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)
								lowercase__     :    str													=     nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

				lowercase__     :    List[str]													=     nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)

				return model


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


				lowercase__     :    Optional[Any]													=     nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T						)						)
				lowercase__     :    int													=     nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T						)						)

				lowercase__     :    List[str]													=     nn.Parameter(
				    torch.FloatTensor(weights["Embed_0"]["embedding"]						)     , requires_grad=_snake_case						)

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

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

								lowercase__     :    List[Any]													=     nn.Parameter(
								    torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T						)						)

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

								lowercase__     :    Tuple													=     ly_weight["MultiHeadDotProductAttention_0"]
								lowercase__     :    List[str]													=     nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
								lowercase__     :    Dict													=     nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
								lowercase__     :    Any													=     nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
								lowercase__     :    str													=     nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
								lowercase__     :    Tuple													=     nn.Parameter(
								    torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]						)						)

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

				lowercase__     :    Union[str, Any]													=     nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]						)						)

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

				return model


def 					__lowerCamelCase					(   lowerCamelCase__						):

				"""simple docstring"""


				lowercase__     :    str													=     checkpoints.load_tax_checkpoint(args.checkpoint_path						)
				lowercase__     :    Dict													=     jnp.tree_util.tree_map(onp.array     , _snake_case						)

				lowercase__     :    str													=     [
				    "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()",
				]

				lowercase__     :    Any													=     os.path.join(args.checkpoint_path     , ".."     , "config.gin"						)
				lowercase__     :    Union[str, Any]													=     inference.parse_training_gin_file(_snake_case     , _snake_case						)
				lowercase__     :    Optional[Any]													=     inference.InferenceModel(args.checkpoint_path     , _snake_case						)

				lowercase__     :    List[Any]													=     DDPMScheduler(beta_schedule="squaredcos_cap_v2"     , variance_type="fixed_large"						)

				lowercase__     :    Tuple													=     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"     , )

				lowercase__     :    List[str]													=     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"     , )

				lowercase__     :    str													=     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     , )

				lowercase__     :    Union[str, Any]													=     load_notes_encoder(ta_checkpoint["target"]["token_encoder"]     , _snake_case						)
				lowercase__     :    Dict													=     load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]     , _snake_case						)
				lowercase__     :    Tuple													=     load_decoder(ta_checkpoint["target"]["decoder"]     , _snake_case						)

				lowercase__     :    Any													=     OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"						)

				lowercase__     :    List[str]													=     SpectrogramDiffusionPipeline(
				    notes_encoder=_snake_case     , continuous_encoder=_snake_case     , decoder=_snake_case     , scheduler=_snake_case     , melgan=_snake_case     , )
				if args.save:
								pipe.save_pretrained(args.output_path						)


if __name__ == "__main__":
				lowerCAmelCase__       =  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.''',
				)
				lowerCAmelCase__       =  parser.parse_args()

				main(args)







import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def        UpperCamelCase_(							_snake_case   :  int       ,     _snake_case   :  int	):




			"""simple docstring"""






			__a        =old_name

			if "patch_embed" in old_name:
						__a      ,    __a      ,    __a        =old_name.split('.'	)

						if layer == "0":
									__a        =old_name.replace('0'       ,     'convolution1'	)
						elif layer == "1":
									__a        =old_name.replace('1'       ,     'batchnorm_before'	)
						elif layer == "3":
									__a        =old_name.replace('3'       ,     'convolution2'	)
						else:
									__a        =old_name.replace('4'       ,     'batchnorm_after'	)

			if "network" in old_name and re.search(r'\d\.\d'       ,     _snake_case	):
						__a        =r'\b\d{2}\b'
						if bool(re.search(_snake_case       ,     _snake_case	)	):
									__a        =re.search(r'\d\.\d\d.'       ,     _snake_case	).group()
						else:
									__a        =re.search(r'\d\.\d.'       ,     _snake_case	).group()
						if int(match[0]	) < 6:
									__a        =old_name.replace(_snake_case       ,     ''	)
									__a        =trimmed_name.replace('network'       ,     match[0] + '.meta4D_layers.blocks.' + match[2:-1]	)
									__a        ='intermediate_stages.' + trimmed_name
						else:
									__a        =old_name.replace(_snake_case       ,     ''	)
									if int(match[2]	) < num_meta4D_last_stage:
												__a        =trimmed_name.replace('network'       ,     'meta4D_layers.blocks.' + match[2]	)
									else:
												__a        =str(int(match[2]	) - num_meta4D_last_stage	)
												__a        =trimmed_name.replace('network'       ,     'meta3D_layers.blocks.' + layer_index	)
												if "norm1" in old_name:
															__a        =trimmed_name.replace('norm1'       ,     'layernorm1'	)
												elif "norm2" in old_name:
															__a        =trimmed_name.replace('norm2'       ,     'layernorm2'	)
												elif "fc1" in old_name:
															__a        =trimmed_name.replace('fc1'       ,     'linear_in'	)
												elif "fc2" in old_name:
															__a        =trimmed_name.replace('fc2'       ,     'linear_out'	)

									__a        ='last_stage.' + trimmed_name

			elif "network" in old_name and re.search(r'.\d.'       ,     _snake_case	):
						__a        =old_name.replace('network'       ,     'intermediate_stages'	)

			if "fc" in new_name:
						__a        =new_name.replace('fc'       ,     'convolution'	)
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						__a        =new_name.replace('norm1'       ,     'batchnorm_before'	)
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						__a        =new_name.replace('norm2'       ,     'batchnorm_after'	)
			if "proj" in new_name:
						__a        =new_name.replace('proj'       ,     'projection'	)
			if "dist_head" in new_name:
						__a        =new_name.replace('dist_head'       ,     'distillation_classifier'	)
			elif "head" in new_name:
						__a        =new_name.replace('head'       ,     'classifier'	)
			elif "patch_embed" in new_name:
						__a        ='efficientformer.' + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						__a        =new_name.replace('norm'       ,     'layernorm'	)
						__a        ='efficientformer.' + new_name
			else:
						__a        ='efficientformer.encoder.' + new_name

			return new_name






def        UpperCamelCase_(							_snake_case   :  List[str]       ,     _snake_case   :  Dict	):




			"""simple docstring"""






			for key in checkpoint.copy().keys():
						__a        =checkpoint.pop(_snake_case	)
						__a        =val

			return checkpoint






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        ='http://images.cocodataset.org/val2017/000000039769.jpg'
			__a        =Image.open(requests.get(_snake_case       ,     stream=_snake_case	).raw	)

			return image






def        UpperCamelCase_(							_snake_case   :  Path       ,     _snake_case   :  Path       ,     _snake_case   :  Path       ,     _snake_case   :  bool	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)['model']
			__a        =EfficientFormerConfig.from_json_file(_snake_case	)
			__a        =EfficientFormerForImageClassificationWithTeacher(_snake_case	)
			__a        ='_'.join(checkpoint_path.split('/'	)[-1].split('.'	)[0].split('_'	)[:-1]	)

			__a        =config.depths[-1] - config.num_metaad_blocks + 1
			__a        =convert_torch_checkpoint(_snake_case       ,     _snake_case	)

			model.load_state_dict(_snake_case	)
			model.eval()

			__a        ={
			    'bilinear': PILImageResampling.BILINEAR,
			    'bicubic': PILImageResampling.BICUBIC,
			    'nearest': PILImageResampling.NEAREST,
			}

			# prepare image
			__a        =prepare_img()
			__a        =256
			__a        =224
			__a        =EfficientFormerImageProcessor(
			    size={'shortest_edge': image_size}       ,     crop_size={'height': crop_size, 'width': crop_size}       ,     resample=pillow_resamplings['bicubic']       ,     )
			__a        =processor(images=_snake_case       ,     return_tensors='pt'	).pixel_values

			# original processing pipeline
			__a        =Compose(
			    [
			        Resize(_snake_case       ,     interpolation=pillow_resamplings['bicubic']	),
			        CenterCrop(_snake_case	),
			        ToTensor(),
			        Normalize(_snake_case       ,     _snake_case	),
			    ]	)
			__a        =image_transforms(_snake_case	).unsqueeze(0	)

			assert torch.allclose(_snake_case       ,     _snake_case	)

			__a        =model(_snake_case	)
			__a        =outputs.logits

			__a        =(1, 1000)

			if "l1" in model_name:
						__a        =torch.Tensor(
						    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]	)
						assert torch.allclose(logits[0, :10]       ,     _snake_case       ,     atol=1e-3	)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						__a        =torch.Tensor(
						    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]	)
						assert torch.allclose(logits[0, :10]       ,     _snake_case       ,     atol=1e-3	)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						__a        =torch.Tensor(
						    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]	)
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    F'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'	)

			# Save Checkpoints
			Path(_snake_case	).mkdir(exist_ok=_snake_case	)
			model.save_pretrained(_snake_case	)
			print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'	)
			processor.save_pretrained(_snake_case	)
			print(F'Processor successfuly saved at {pytorch_dump_path}'	)

			if push_to_hub:
						print('Pushing model to the hub...'	)

						model.push_to_hub(
						    repo_id=F'Bearnardd/{pytorch_dump_path}'       ,     commit_message='Add model'       ,     use_temp_dir=_snake_case       ,     )
						processor.push_to_hub(
						    repo_id=F'Bearnardd/{pytorch_dump_path}'       ,     commit_message='Add image processor'       ,     use_temp_dir=_snake_case       ,     )


if __name__ == "__main__":
						_lowerCAmelCase					:			int  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--pytorch_model_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to EfficientFormer pytorch checkpoint.",
						)
						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help="The json file for EfficientFormer model config.",
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)

						parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
						parser.add_argument(
						    "--no-push_to_hub",
						    dest="push_to_hub",
						    action="store_false",
						    help="Do not push model and image processor to the hub",
						)
						parser.set_defaults(push_to_hub=True)

						_lowerCAmelCase					:			Union[str, Any]  				=			parser.parse_args()
						convert_efficientformer_checkpoint(
						    checkpoint_path=args.pytorch_model_path,
						    efficientformer_config_file=args.config_file,
						    pytorch_dump_path=args.pytorch_dump_path,
						    push_to_hub=args.push_to_hub,
						)






