


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import OwlViTImageProcessor, OwlViTProcessor





@require_vision
class   __a      (   unittest.TestCase				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       tempfile.mkdtemp()

        # fmt: off
        lowercase__:       List[str]        =       ['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
        # fmt: on
        lowercase__:       int        =       dict(zip(lowerCAmelCase__    ,			range(len(lowerCAmelCase__			)			)			)			)
        lowercase__:       Tuple        =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
        lowercase__:       Optional[int]        =       {'unk_token': '<unk>'}

        lowercase__:       List[Any]        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       Union[str, Any]        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['merges_file']			)
        with open(self.vocab_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write(json.dumps(lowerCAmelCase__			) + '\n'			)
        with open(self.merges_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write('\n'.join(lowerCAmelCase__			)			)

        lowercase__:       Tuple        =       {
            'do_resize': True,
            'size': 20,
            'do_center_crop': True,
            'crop_size': 18,
            'do_normalize': True,
            'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
        }
        lowercase__:       int        =       os.path.join(self.tmpdirname    ,			lowerCAmelCase__			)
        with open(self.image_processor_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               json.dump(lowerCAmelCase__    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        return CLIPTokenizer.from_pretrained(self.tmpdirname    ,			pad_token='!'    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,			pad_token='!'    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        shutil.rmtree(self.tmpdirname			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       [np.random.randint(255    ,			size=(3, 30, 400)    ,			dtype=np.uinta			)]

        lowercase__:       Optional[int]        =       [Image.fromarray(np.moveaxis(lowerCAmelCase__    ,			0    ,			-1			)			) for x in image_inputs]

        return image_inputs
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       List[str]        =       self.get_tokenizer()
        lowercase__:       int        =       self.get_rust_tokenizer()
        lowercase__:       Union[str, Any]        =       self.get_image_processor()

        lowercase__:       Tuple        =       OwlViTProcessor(tokenizer=lowerCAmelCase__    ,			image_processor=lowerCAmelCase__			)
        processor_slow.save_pretrained(self.tmpdirname			)
        lowercase__:       str        =       OwlViTProcessor.from_pretrained(self.tmpdirname    ,			use_fast=lowerCAmelCase__			)

        lowercase__:       int        =       OwlViTProcessor(tokenizer=lowerCAmelCase__    ,			image_processor=lowerCAmelCase__			)
        processor_fast.save_pretrained(self.tmpdirname			)
        lowercase__:       Dict        =       OwlViTProcessor.from_pretrained(self.tmpdirname			)

        self.assertEqual(processor_slow.tokenizer.get_vocab()    ,			tokenizer_slow.get_vocab()			)
        self.assertEqual(processor_fast.tokenizer.get_vocab()    ,			tokenizer_fast.get_vocab()			)
        self.assertEqual(tokenizer_slow.get_vocab()    ,			tokenizer_fast.get_vocab()			)
        self.assertIsInstance(processor_slow.tokenizer    ,			lowerCAmelCase__			)
        self.assertIsInstance(processor_fast.tokenizer    ,			lowerCAmelCase__			)

        self.assertEqual(processor_slow.image_processor.to_json_string()    ,			image_processor.to_json_string()			)
        self.assertEqual(processor_fast.image_processor.to_json_string()    ,			image_processor.to_json_string()			)
        self.assertIsInstance(processor_slow.image_processor    ,			lowerCAmelCase__			)
        self.assertIsInstance(processor_fast.image_processor    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       OwlViTProcessor(tokenizer=self.get_tokenizer()    ,			image_processor=self.get_image_processor()			)
        processor.save_pretrained(self.tmpdirname			)

        lowercase__:       Union[str, Any]        =       self.get_tokenizer(bos_token='(BOS)'    ,			eos_token='(EOS)'			)
        lowercase__:       Optional[Any]        =       self.get_image_processor(do_normalize=lowerCAmelCase__			)

        lowercase__:       Optional[int]        =       OwlViTProcessor.from_pretrained(
            self.tmpdirname    ,			bos_token='(BOS)'    ,			eos_token='(EOS)'    ,			do_normalize=lowerCAmelCase__			)

        self.assertEqual(processor.tokenizer.get_vocab()    ,			tokenizer_add_kwargs.get_vocab()			)
        self.assertIsInstance(processor.tokenizer    ,			lowerCAmelCase__			)

        self.assertEqual(processor.image_processor.to_json_string()    ,			image_processor_add_kwargs.to_json_string()			)
        self.assertIsInstance(processor.image_processor    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Any        =       self.get_image_processor()
        lowercase__:       Optional[int]        =       self.get_tokenizer()

        lowercase__:       int        =       OwlViTProcessor(tokenizer=lowerCAmelCase__    ,			image_processor=lowerCAmelCase__			)

        lowercase__:       Optional[int]        =       self.prepare_image_inputs()

        lowercase__:       Tuple        =       image_processor(lowerCAmelCase__    ,			return_tensors='np'			)
        lowercase__:       List[str]        =       processor(images=lowerCAmelCase__    ,			return_tensors='np'			)

        for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()    ,			input_processor[key].sum()    ,			delta=1E-2			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       self.get_image_processor()
        lowercase__:       List[str]        =       self.get_tokenizer()

        lowercase__:       Any        =       OwlViTProcessor(tokenizer=lowerCAmelCase__    ,			image_processor=lowerCAmelCase__			)

        lowercase__:       List[Any]        =       'lower newer'

        lowercase__:       List[Any]        =       processor(text=lowerCAmelCase__    ,			return_tensors='np'			)

        lowercase__:       List[Any]        =       tokenizer(lowerCAmelCase__    ,			return_tensors='np'			)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key][0].tolist()    ,			encoded_processor[key][0].tolist()			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Dict        =       self.get_image_processor()
        lowercase__:       str        =       self.get_tokenizer()

        lowercase__:       Any        =       OwlViTProcessor(tokenizer=lowerCAmelCase__    ,			image_processor=lowerCAmelCase__			)

        lowercase__:       Optional[int]        =       'lower newer'
        lowercase__:       List[Any]        =       self.prepare_image_inputs()

        lowercase__:       Union[str, Any]        =       processor(text=lowerCAmelCase__    ,			images=lowerCAmelCase__			)

        self.assertListEqual(list(inputs.keys()			)    ,			['input_ids', 'attention_mask', 'pixel_values']			)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__			):
               processor()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Any        =       'google/owlvit-base-patch32'
        lowercase__:       Tuple        =       OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

        lowercase__:       Dict        =       ['cat', 'nasa badge']
        lowercase__:       List[Any]        =       processor(text=lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       16
        self.assertListEqual(list(inputs.keys()			)    ,			['input_ids', 'attention_mask']			)
        self.assertEqual(inputs['input_ids'].shape    ,			(2, seq_length)			)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__			):
               processor()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       'google/owlvit-base-patch32'
        lowercase__:       List[Any]        =       OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

        lowercase__:       Tuple        =       [['cat', 'nasa badge'], ['person']]
        lowercase__:       Optional[Any]        =       processor(text=lowerCAmelCase__			)

        lowercase__:       Dict        =       16
        lowercase__:       List[Any]        =       len(lowerCAmelCase__			)
        lowercase__:       int        =       max([len(lowerCAmelCase__			) for texts in input_texts]			)

        self.assertListEqual(list(inputs.keys()			)    ,			['input_ids', 'attention_mask']			)
        self.assertEqual(inputs['input_ids'].shape    ,			(batch_size * num_max_text_queries, seq_length)			)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__			):
               processor()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Dict        =       'google/owlvit-base-patch32'
        lowercase__:       Any        =       OwlViTProcessor.from_pretrained(lowerCAmelCase__			)

        lowercase__:       List[Any]        =       ['cat', 'nasa badge']
        lowercase__:       Dict        =       processor(text=lowerCAmelCase__			)

        lowercase__:       Dict        =       16
        lowercase__:       List[str]        =       inputs['input_ids']
        lowercase__:       Tuple        =       [
            [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ]

        self.assertListEqual(list(inputs.keys()			)    ,			['input_ids', 'attention_mask']			)
        self.assertEqual(inputs['input_ids'].shape    ,			(2, seq_length)			)
        self.assertListEqual(list(input_ids[0]			)    ,			predicted_ids[0]			)
        self.assertListEqual(list(input_ids[1]			)    ,			predicted_ids[1]			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.get_image_processor()
        lowercase__:       int        =       self.get_tokenizer()

        lowercase__:       str        =       OwlViTProcessor(tokenizer=lowerCAmelCase__    ,			image_processor=lowerCAmelCase__			)

        lowercase__:       Optional[int]        =       self.prepare_image_inputs()
        lowercase__:       Optional[Any]        =       self.prepare_image_inputs()

        lowercase__:       Union[str, Any]        =       processor(images=lowerCAmelCase__    ,			query_images=lowerCAmelCase__			)

        self.assertListEqual(list(inputs.keys()			)    ,			['query_pixel_values', 'pixel_values']			)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase__			):
               processor()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[str]        =       self.get_image_processor()
        lowercase__:       str        =       self.get_tokenizer()

        lowercase__:       Tuple        =       OwlViTProcessor(tokenizer=lowerCAmelCase__    ,			image_processor=lowerCAmelCase__			)

        lowercase__:       List[Any]        =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        lowercase__:       Dict        =       processor.batch_decode(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       tokenizer.batch_decode(lowerCAmelCase__			)

        self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)






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        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       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__:       List[Any]        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

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

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

              lowercase__:       Optional[Any]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

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




def        snake_case_					(  snake_case      ,							snake_case					)  ->    List[str]:
       lowercase__:       str        =       nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

       lowercase__:       Dict        =       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[Any]        =       ly_weight['attention']

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

              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
              lowercase__:       Dict        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
              lowercase__:       Tuple        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)
              lowercase__:       Optional[int]        =       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        snake_case_					(  snake_case      ,							snake_case					)  ->    Any:
       lowercase__:       int        =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
       lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

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

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

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

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

              lowercase__:       List[str]        =       ly_weight['self_attention']
              lowercase__:       Optional[int]        =       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
              lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
              lowercase__:       Tuple        =       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
              lowercase__:       Union[str, Any]        =       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

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

              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)
              lowercase__:       Union[str, Any]        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T					)					)
              lowercase__:       List[str]        =       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__:       str        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

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

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

       return model




def        snake_case_					(  snake_case					)  ->    Any:
       lowercase__:       int        =       checkpoints.load_tax_checkpoint(args.checkpoint_path					)
       lowercase__:       Tuple        =       jnp.tree_util.tree_map(onp.array      ,							snake_case					)

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

       lowercase__:       Dict        =       DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,							variance_type='fixed_large'					)

       lowercase__:       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'      ,							)

       lowercase__:       Dict        =       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__:       Optional[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      ,							)

       lowercase__:       Dict        =       load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,							snake_case					)
       lowercase__:       int        =       load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,							snake_case					)
       lowercase__:       Optional[int]        =       load_decoder(ta_checkpoint['target']['decoder']      ,							snake_case					)

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

       lowercase__:       List[Any]        =       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)


