


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase			=							get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

__lowerCAmelCase			=							25_00_04
__lowerCAmelCase			=							25_00_20





@require_sentencepiece
@require_tokenizers
class   __a      (   __snake_case	,	unittest.TestCase				):
 __lowercase			:							Optional[Any]       					=			MBartaaTokenizer
 __lowercase			:							str       					=			MBartaaTokenizerFast
 __lowercase			:							Tuple       					=			True
 __lowercase			:							List[str]       					=			True
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase__:       Union[str, Any]        =       MBartaaTokenizer(lowerCamelCase_    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO'    ,			keep_accents=lowerCamelCase_			)
        tokenizer.save_pretrained(self.tmpdirname			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Tuple        =       """<s>"""
        lowercase__:       str        =       0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase_			)    ,			lowerCamelCase_			)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase_			)    ,			lowerCamelCase_			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       List[Any]        =       list(self.get_tokenizer().get_vocab().keys()			)

        self.assertEqual(vocab_keys[0]    ,			'<s>'			)
        self.assertEqual(vocab_keys[1]    ,			'<pad>'			)
        self.assertEqual(vocab_keys[-1]    ,			'<mask>'			)
        self.assertEqual(len(lowerCamelCase_			)    ,			1_054			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        self.assertEqual(self.get_tokenizer().vocab_size    ,			1_054			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Any        =       MBartaaTokenizer(lowerCamelCase_    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO'    ,			keep_accents=lowerCamelCase_			)

        lowercase__:       Dict        =       tokenizer.tokenize('This is a test'			)
        self.assertListEqual(lowerCamelCase_    ,			['▁This', '▁is', '▁a', '▁t', 'est']			)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(lowerCamelCase_			)    ,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,			)

        lowercase__:       Tuple        =       tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
        self.assertListEqual(
            lowerCamelCase_    ,			[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', '.']    ,			)
        lowercase__:       Optional[Any]        =       tokenizer.convert_tokens_to_ids(lowerCamelCase_			)
        self.assertListEqual(
            lowerCamelCase_    ,			[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            ]    ,			)

        lowercase__:       Tuple        =       tokenizer.convert_ids_to_tokens(lowerCamelCase_			)
        self.assertListEqual(
            lowerCamelCase_    ,			[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>', '.']    ,			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        # fmt: off
        lowercase__:       Dict        =       {"""input_ids""": [[250_004, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [250_004, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 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], [250_004, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 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]], """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, 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], [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]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCamelCase_    ,			model_name='facebook/mbart-large-50'    ,			revision='d3913889c59cd5c9e456b269c376325eabad57e2'    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        if not self.test_slow_tokenizer:
               # as we don't have a slow version, we can't compare the outputs between slow and fast versions
               return

        lowercase__:       Optional[int]        =       (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       List[Any]        =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase_    ,			**lowerCamelCase_			)
                      lowercase__:       Dict        =       self.tokenizer_class.from_pretrained(lowerCamelCase_    ,			**lowerCamelCase_			)

                      lowercase__:       int        =       tempfile.mkdtemp()

                      lowercase__:       Any        =       tokenizer_r.save_pretrained(lowerCamelCase_			)
                      lowercase__:       Any        =       tokenizer_p.save_pretrained(lowerCamelCase_			)

                      # Checks it save with the same files + the tokenizer.json file for the fast one
                      self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files			)			)
                      lowercase__:       Any        =       tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f			)
                      self.assertSequenceEqual(lowerCamelCase_    ,			lowerCamelCase_			)

                      # Checks everything loads correctly in the same way
                      lowercase__:       List[Any]        =       tokenizer_r.from_pretrained(lowerCamelCase_			)
                      lowercase__:       Optional[Any]        =       tokenizer_p.from_pretrained(lowerCamelCase_			)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowerCamelCase_    ,			lowerCamelCase_			)			)
                             # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                             # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                      shutil.rmtree(lowerCamelCase_			)

                      # Save tokenizer rust, legacy_format=True
                      lowercase__:       Union[str, Any]        =       tempfile.mkdtemp()

                      lowercase__:       int        =       tokenizer_r.save_pretrained(lowerCamelCase_    ,			legacy_format=lowerCamelCase_			)
                      lowercase__:       str        =       tokenizer_p.save_pretrained(lowerCamelCase_			)

                      # Checks it save with the same files
                      self.assertSequenceEqual(lowerCamelCase_    ,			lowerCamelCase_			)

                      # Checks everything loads correctly in the same way
                      lowercase__:       List[str]        =       tokenizer_r.from_pretrained(lowerCamelCase_			)
                      lowercase__:       Tuple        =       tokenizer_p.from_pretrained(lowerCamelCase_			)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowerCamelCase_    ,			lowerCamelCase_			)			)

                      shutil.rmtree(lowerCamelCase_			)

                      # Save tokenizer rust, legacy_format=False
                      lowercase__:       Optional[Any]        =       tempfile.mkdtemp()

                      lowercase__:       Dict        =       tokenizer_r.save_pretrained(lowerCamelCase_    ,			legacy_format=lowerCamelCase_			)
                      lowercase__:       Tuple        =       tokenizer_p.save_pretrained(lowerCamelCase_			)

                      # Checks it saved the tokenizer.json file
                      self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files			)			)

                      # Checks everything loads correctly in the same way
                      lowercase__:       int        =       tokenizer_r.from_pretrained(lowerCamelCase_			)
                      lowercase__:       Union[str, Any]        =       tokenizer_p.from_pretrained(lowerCamelCase_			)

                      # Check special tokens are set accordingly on Rust and Python
                      for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(lowerCamelCase_    ,			lowerCamelCase_			)			)

                      shutil.rmtree(lowerCamelCase_			)





@require_torch
@require_sentencepiece
@require_tokenizers
class   __a      (   unittest.TestCase				):
 __lowercase			:							List[Any]       					=			"facebook/mbart-large-50-one-to-many-mmt"
 __lowercase			:							Tuple       					=			[
     " UN Chief Says There Is No Military Solution in Syria",
     " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
 ]
 __lowercase			:							int       					=			[
     "Şeful ONU declară că nu există o soluţie militară în Siria",
     "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
     " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
     " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
 ]
 __lowercase			:							Optional[int]       					=			[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]
 @classmethod
 def 		SCREAMING_SNAKE_CASE__       (       cls			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       MBartaaTokenizer        =       MBartaaTokenizer.from_pretrained(
            cls.checkpoint_name    ,			src_lang='en_XX'    ,			tgt_lang='ro_RO'			)
        lowercase__:       str        =       1
        return cls
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']    ,			250_001			)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']    ,			250_004			)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']    ,			250_020			)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']    ,			250_038			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       str        =       self.tokenizer.batch_encode_plus(self.src_text			).input_ids[0]
        self.assertListEqual(self.expected_src_tokens    ,			lowerCamelCase_			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        self.assertIn(lowerCamelCase_    ,			self.tokenizer.all_special_ids			)
        lowercase__:       List[str]        =       [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
        lowercase__:       Tuple        =       self.tokenizer.decode(lowerCamelCase_    ,			skip_special_tokens=lowerCamelCase_			)
        lowercase__:       int        =       self.tokenizer.decode(generated_ids[1:]    ,			skip_special_tokens=lowerCamelCase_			)
        self.assertEqual(lowerCamelCase_    ,			lowerCamelCase_			)
        self.assertNotIn(self.tokenizer.eos_token    ,			lowerCamelCase_			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       ["""this is gunna be a long sentence """ * 20]
        assert isinstance(src_text[0]    ,			lowerCamelCase_			)
        lowercase__:       Optional[Any]        =       10
        lowercase__:       Optional[int]        =       self.tokenizer(lowerCamelCase_    ,			max_length=lowerCamelCase_    ,			truncation=lowerCamelCase_			).input_ids[0]
        self.assertEqual(ids[0]    ,			lowerCamelCase_			)
        self.assertEqual(ids[-1]    ,			2			)
        self.assertEqual(len(lowerCamelCase_			)    ,			lowerCamelCase_			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']			)    ,			[250_053, 250_001]			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       List[str]        =       tempfile.mkdtemp()
        lowercase__:       Union[str, Any]        =       self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(lowerCamelCase_			)
        lowercase__:       Any        =       MBartaaTokenizer.from_pretrained(lowerCamelCase_			)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,			lowerCamelCase_			)
 @require_torch
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.tokenizer(self.src_text    ,			text_target=self.tgt_text    ,			padding=lowerCamelCase_    ,			return_tensors='pt'			)
        lowercase__:       Union[str, Any]        =       shift_tokens_right(batch['labels']    ,			self.tokenizer.pad_token_id			)

        # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
        assert batch.input_ids[1][0] == EN_CODE
        assert batch.input_ids[1][-1] == 2
        assert batch.labels[1][0] == RO_CODE
        assert batch.labels[1][-1] == 2
        assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
 @require_torch
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       int        =       self.tokenizer(
            self.src_text    ,			text_target=self.tgt_text    ,			padding=lowerCamelCase_    ,			truncation=lowerCamelCase_    ,			max_length=len(self.expected_src_tokens			)    ,			return_tensors='pt'    ,			)
        lowercase__:       Union[str, Any]        =       shift_tokens_right(batch['labels']    ,			self.tokenizer.pad_token_id			)

        self.assertIsInstance(lowerCamelCase_    ,			lowerCamelCase_			)

        self.assertEqual((2, 14)    ,			batch.input_ids.shape			)
        self.assertEqual((2, 14)    ,			batch.attention_mask.shape			)
        lowercase__:       Optional[Any]        =       batch.input_ids.tolist()[0]
        self.assertListEqual(self.expected_src_tokens    ,			lowerCamelCase_			)
        self.assertEqual(2    ,			batch.decoder_input_ids[0, 0]			)  # decoder_start_token_id
        # Test that special tokens are reset
        self.assertEqual(self.tokenizer.prefix_tokens    ,			[EN_CODE]			)
        self.assertEqual(self.tokenizer.suffix_tokens    ,			[self.tokenizer.eos_token_id]			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       str        =       self.tokenizer(self.src_text    ,			padding=lowerCamelCase_    ,			truncation=lowerCamelCase_    ,			max_length=3    ,			return_tensors='pt'			)
        lowercase__:       List[str]        =       self.tokenizer(
            text_target=self.tgt_text    ,			padding=lowerCamelCase_    ,			truncation=lowerCamelCase_    ,			max_length=10    ,			return_tensors='pt'			)
        lowercase__:       Optional[int]        =       targets["""input_ids"""]
        lowercase__:       str        =       shift_tokens_right(lowerCamelCase_    ,			self.tokenizer.pad_token_id			)

        self.assertEqual(batch.input_ids.shape[1]    ,			3			)
        self.assertEqual(batch.decoder_input_ids.shape[1]    ,			10			)
 @require_torch
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Any:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.tokenizer._build_translation_inputs(
            'A test'    ,			return_tensors='pt'    ,			src_lang='en_XX'    ,			tgt_lang='ar_AR'			)

        self.assertEqual(
            nested_simplify(lowerCamelCase_			)    ,			{
                # en_XX, A, test, EOS
                'input_ids': [[250_004, 62, 3_034, 2]],
                'attention_mask': [[1, 1, 1, 1]],
                # ar_AR
                'forced_bos_token_id': 250_001,
            }    ,			)






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)


