'''simple docstring'''





import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_:	List[str]    =get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 			__A (					UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		Tuple			  =    XLNetTokenizer
    a__			:		str			  =    XLNetTokenizerFast
    a__			:		str			  =    True
    a__			:		str			  =    True



    def    _lowercase		(self       :       Optional[Any]					):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase_			       =  XLNetTokenizer(a_  ,     keep_accents=a_					)
         tokenizer.sanitize_special_tokens()
         tokenizer.save_pretrained(self.tmpdirname					)



    def    _lowercase		(self       :       str					):
         UpperCAmelCase_			       =  '<s>'
         UpperCAmelCase_			       =  1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_					)  ,     a_					)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_					)  ,     a_					)



    def    _lowercase		(self       :       Any					):
         UpperCAmelCase_			       =  list(self.get_tokenizer().get_vocab().keys()					)

         self.assertEqual(vocab_keys[0]  ,     "<unk>"					)
         self.assertEqual(vocab_keys[1]  ,     "<s>"					)
         self.assertEqual(vocab_keys[-1]  ,     "<eod>"					)
         self.assertEqual(len(a_					)  ,     1006					)



    def    _lowercase		(self       :       Optional[Any]					):
         self.assertEqual(self.get_tokenizer().vocab_size  ,     1000					)



    def    _lowercase		(self       :       Union[str, Any]					):
         UpperCAmelCase_			       =  XLNetTokenizer(a_  ,     keep_accents=a_					)

         UpperCAmelCase_			       =  tokenizer.tokenize("This is a test"					)
         self.assertListEqual(a_  ,     ["▁This", "▁is", "▁a", "▁t", "est"]					)

         self.assertListEqual(tokenizer.convert_tokens_to_ids(a_					)  ,     [285, 46, 10, 170, 382]					)

         UpperCAmelCase_			       =  tokenizer.tokenize("I was born in 92000, and this is falsé."					)
         self.assertListEqual(
             a_  ,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]  ,     )
         UpperCAmelCase_			       =  tokenizer.convert_tokens_to_ids(a_					)
         self.assertListEqual(a_  ,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]					)

         UpperCAmelCase_			       =  tokenizer.convert_ids_to_tokens(a_					)
         self.assertListEqual(
             a_  ,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]  ,     )



    def    _lowercase		(self       :       Dict					):
         UpperCAmelCase_			       =  XLNetTokenizer(a_  ,     do_lower_case=a_					)
         UpperCAmelCase_			       =  tokenizer.tokenize("I was born in 92000, and this is falsé."					)
         self.assertListEqual(
             a_  ,     [
                 SPIECE_UNDERLINE + "",
                 "i",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "se",
                 ".",
             ]  ,     )
         self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)  ,     ["▁he", "ll", "o"]					)



    def    _lowercase		(self       :       Tuple					):
         UpperCAmelCase_			       =  XLNetTokenizer(a_  ,     do_lower_case=a_					)
         UpperCAmelCase_			       =  tokenizer.tokenize("I was born in 92000, and this is falsé."					)
         self.assertListEqual(
             a_  ,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "se",
                 ".",
             ]  ,     )



    @slow
    def    _lowercase		(self       :       List[str]					):
         UpperCAmelCase_			       =  XLNetTokenizer.from_pretrained("xlnet-base-cased"					)

         UpperCAmelCase_			       =  tokenizer.encode("sequence builders"  ,     add_special_tokens=a_					)
         UpperCAmelCase_			       =  tokenizer.encode("multi-sequence build"  ,     add_special_tokens=a_					)

         UpperCAmelCase_			       =  tokenizer.build_inputs_with_special_tokens(a_					)
         UpperCAmelCase_			       =  tokenizer.build_inputs_with_special_tokens(a_  ,     a_					)

         assert encoded_sentence == text + [4, 3]
         assert encoded_pair == text + [4] + text_a + [4, 3]






    @slow
    def    _lowercase		(self       :       Dict					):
         # fmt: off
         UpperCAmelCase_			       =  {'input_ids': [[17, 21442, 270, 17, 10, 14645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 22018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 14431, 13, 5500, 11, 1176, 580, 13, 16819, 4797, 23, 17, 10, 17135, 658, 19, 457, 7932, 13, 184, 19, 3154, 17135, 6468, 19, 1404, 12269, 19, 4229, 5356, 16264, 46, 19, 17, 20545, 10395, 9, 9, 9, 11, 28, 6421, 9531, 20729, 17, 10, 353, 17022, 11, 21, 6421, 9531, 16949, 17, 10, 11509, 753, 11, 33, 95, 2421, 7385, 956, 14431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 24738, 19, 13203, 658, 218, 787, 21, 430, 18482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22178, 27, 1064, 22, 956, 13, 11101, 1429, 5854, 24313, 18953, 40, 422, 24366, 68, 1758, 37, 10483, 14257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 13894, 3380, 23, 95, 18, 17634, 2288, 9, 4, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=a_  ,     model_name="xlnet-base-cased"  ,     revision="c841166438c31ec7ca9a106dee7bb312b73ae511"  ,     )









'''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)




