



'''simple docstring'''

from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching







def a_     (    )				->					str:





			"""simple docstring"""







			import os as original_os
			from os import path as original_path
			from os import rename as original_rename
			from os.path import dirname as original_dirname
			from os.path import join as original_join

			assert _test_patching.os is original_os
			assert _test_patching.path is original_path
			assert _test_patching.join is original_join

			assert _test_patching.renamed_os is original_os
			assert _test_patching.renamed_path is original_path
			assert _test_patching.renamed_join is original_join

			snake_case:    Any      							='__test_patch_submodule_mock__'
			with patch_submodule(_test_patching				,							'os.path.join'				,							__UpperCAmelCase					):
						# Every way to access os.path.join must be patched, and the rest must stay untouched

						# check os.path.join
						assert isinstance(_test_patching.os				,							_PatchedModuleObj					)
						assert isinstance(_test_patching.os.path				,							_PatchedModuleObj					)
						assert _test_patching.os.path.join is mock

						# check path.join
						assert isinstance(_test_patching.path				,							_PatchedModuleObj					)
						assert _test_patching.path.join is mock

						# check join
						assert _test_patching.join is mock

						# check that the other attributes are untouched
						assert _test_patching.os.rename is original_rename
						assert _test_patching.path.dirname is original_dirname
						assert _test_patching.os.path.dirname is original_dirname

						# Even renamed modules or objects must be patched

						# check renamed_os.path.join
						assert isinstance(_test_patching.renamed_os				,							_PatchedModuleObj					)
						assert isinstance(_test_patching.renamed_os.path				,							_PatchedModuleObj					)
						assert _test_patching.renamed_os.path.join is mock

						# check renamed_path.join
						assert isinstance(_test_patching.renamed_path				,							_PatchedModuleObj					)
						assert _test_patching.renamed_path.join is mock

						# check renamed_join
						assert _test_patching.renamed_join is mock

						# check that the other attributes are untouched
						assert _test_patching.renamed_os.rename is original_rename
						assert _test_patching.renamed_path.dirname is original_dirname
						assert _test_patching.renamed_os.path.dirname is original_dirname

			# check that everthing is back to normal when the patch is over

			assert _test_patching.os is original_os
			assert _test_patching.path is original_path
			assert _test_patching.join is original_join

			assert _test_patching.renamed_os is original_os
			assert _test_patching.renamed_path is original_path
			assert _test_patching.renamed_join is original_join







def a_     (    )				->					Optional[Any]:





			"""simple docstring"""







			assert _test_patching.open is open

			snake_case:    str      							='__test_patch_submodule_builtin_mock__'
			# _test_patching has "open" in its globals
			assert _test_patching.open is open
			with patch_submodule(_test_patching				,							'open'				,							__UpperCAmelCase					):
						assert _test_patching.open is mock

			# check that everthing is back to normal when the patch is over

			assert _test_patching.open is open







def a_     (    )				->					List[str]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							='__test_patch_submodule_missing_mock__'
			with patch_submodule(_test_patching				,							'pandas.read_csv'				,							__UpperCAmelCase					):
						pass







def a_     (    )				->					int:





			"""simple docstring"""







			snake_case:    int      							='__test_patch_submodule_missing_builtin_mock__'
			# _test_patching doesn't have "len" in its globals
			assert getattr(_test_patching				,							'len'				,							__UpperCAmelCase					) is None
			with patch_submodule(_test_patching				,							'len'				,							__UpperCAmelCase					):
						assert _test_patching.len is mock
			assert _test_patching.len is len







def a_     (    )				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							='__test_patch_submodule_start_and_stop_mock__'
			snake_case:    Dict      							=patch_submodule(_test_patching				,							'open'				,							__UpperCAmelCase					)
			assert _test_patching.open is open
			patch.start()
			assert _test_patching.open is mock
			patch.stop()
			assert _test_patching.open is open







def a_     (    )				->					int:





			"""simple docstring"""







			from os import rename as original_rename
			from os.path import dirname as original_dirname
			from os.path import join as original_join

			snake_case:    Union[str, Any]      							='__test_patch_submodule_successive_join__'
			snake_case:    int      							='__test_patch_submodule_successive_dirname__'
			snake_case:    Any      							='__test_patch_submodule_successive_rename__'
			assert _test_patching.os.path.join is original_join
			assert _test_patching.os.path.dirname is original_dirname
			assert _test_patching.os.rename is original_rename

			with patch_submodule(_test_patching				,							'os.path.join'				,							__UpperCAmelCase					):
						with patch_submodule(_test_patching				,							'os.rename'				,							__UpperCAmelCase					):
									with patch_submodule(_test_patching				,							'os.path.dirname'				,							__UpperCAmelCase					):
												assert _test_patching.os.path.join is mock_join
												assert _test_patching.os.path.dirname is mock_dirname
												assert _test_patching.os.rename is mock_rename

    # try another order
			with patch_submodule(_test_patching				,							'os.rename'				,							__UpperCAmelCase					):
						with patch_submodule(_test_patching				,							'os.path.join'				,							__UpperCAmelCase					):
									with patch_submodule(_test_patching				,							'os.path.dirname'				,							__UpperCAmelCase					):
												assert _test_patching.os.path.join is mock_join
												assert _test_patching.os.path.dirname is mock_dirname
												assert _test_patching.os.rename is mock_rename

			assert _test_patching.os.path.join is original_join
			assert _test_patching.os.path.dirname is original_dirname
			assert _test_patching.os.rename is original_rename







def a_     (    )				->					Any:





			"""simple docstring"""







			snake_case:    Tuple      							='__test_patch_submodule_doesnt_exist_mock__'
			with patch_submodule(_test_patching				,							'__module_that_doesn_exist__.__attribute_that_doesn_exist__'				,							__UpperCAmelCase					):
						pass
			with patch_submodule(_test_patching				,							'os.__attribute_that_doesn_exist__'				,							__UpperCAmelCase					):
						pass









'''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


a							   =				'base_with_context'







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					str:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']					)					)
			snake_case:    Tuple      							=nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']					)				,							requires_grad=__UpperCAmelCase					)
			for lyr_num, lyr in enumerate(model.encoders					):
						snake_case:    Dict      							=weights[f'''layers_{lyr_num}''']
						snake_case:    str      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

						snake_case:    Any      							=ly_weight['attention']
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

						snake_case:    List[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

			snake_case:    Any      							=nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)
			return model







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

			snake_case:    Dict      							=nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']					)				,							requires_grad=__UpperCAmelCase					)

			for lyr_num, lyr in enumerate(model.encoders					):
						snake_case:    List[Any]      							=weights[f'''layers_{lyr_num}''']
						snake_case:    Tuple      							=ly_weight['attention']

						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Tuple      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)
						snake_case:    Any      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

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

			return model







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
			snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

			snake_case:    Tuple      							=nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']					)				,							requires_grad=__UpperCAmelCase					)

			snake_case:    Any      							=nn.Parameter(
			    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T					)					)

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

						snake_case:    int      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T					)					)

						snake_case:    str      							=ly_weight['self_attention']
						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    List[str]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

						snake_case:    Optional[Any]      							=ly_weight['MultiHeadDotProductAttention_0']
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    List[str]      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
						snake_case:    Any      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']					)					)

						snake_case:    int      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T					)					)
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']					)					)

			snake_case:    int      							=nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T					)					)

			return model







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=checkpoints.load_tax_checkpoint(args.checkpoint_path					)
			snake_case:    Tuple      							=jnp.tree_util.tree_map(onp.array				,							__UpperCAmelCase					)

			snake_case:    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()',
			]

			snake_case:    List[Any]      							=os.path.join(args.checkpoint_path				,							'..'				,							'config.gin'					)
			snake_case:    Optional[Any]      							=inference.parse_training_gin_file(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[str]      							=inference.InferenceModel(args.checkpoint_path				,							__UpperCAmelCase					)

			snake_case:    List[Any]      							=DDPMScheduler(beta_schedule='squaredcos_cap_v2'				,							variance_type='fixed_large'					)

			snake_case:    Optional[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'				,							)

			snake_case:    Optional[Any]      							=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'				,							)

			snake_case:    List[Any]      							=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				,							)

			snake_case:    Optional[Any]      							=load_notes_encoder(ta_checkpoint['target']['token_encoder']				,							__UpperCAmelCase					)
			snake_case:    Optional[Any]      							=load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']				,							__UpperCAmelCase					)
			snake_case:    Union[str, Any]      							=load_decoder(ta_checkpoint['target']['decoder']				,							__UpperCAmelCase					)

			snake_case:    int      							=OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'					)

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


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

	main(args)




