



'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a							   =				get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')






@require_sentencepiece
@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Any        =				GPTSwaTokenizer
							UpperCAmelCase			:		int        =				False
							UpperCAmelCase			:		Optional[int]        =				True
							UpperCAmelCase			:		Optional[Any]        =				False


							def    UpperCamelCase     (				self   :						Tuple			)			->					Tuple:
										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case:    Optional[Any]      							=GPTSwaTokenizer(a_      ,						eos_token='<unk>'      ,						bos_token='<unk>'      ,						pad_token='<unk>'			)

										tokenizer.save_pretrained(self.tmpdirname			)


							def    UpperCamelCase     (				self   :						Dict      ,						a_   :						List[Any]			)			->					Any:
										snake_case:    List[str]      							='This is a test'
										snake_case:    List[str]      							='This is a test'
										return input_text, output_text


							def    UpperCamelCase     (				self   :						List[str]			)			->					str:
										snake_case:    Optional[int]      							='<s>'
										snake_case:    Tuple      							=1

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


							def    UpperCamelCase     (				self   :						int			)			->					Optional[Any]:
										snake_case:    Tuple      							=list(self.get_tokenizer().get_vocab().keys()			)

										self.assertEqual(vocab_keys[0]      ,						'<unk>'			)
										self.assertEqual(vocab_keys[1]      ,						'<s>'			)
										self.assertEqual(vocab_keys[-1]      ,						'j'			)
										self.assertEqual(len(a_			)      ,						2_0_0_0			)


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					Dict:
										self.assertEqual(self.get_tokenizer().vocab_size      ,						2_0_0_0			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Optional[Any]:
										snake_case:    Any      							=GPTSwaTokenizer(a_			)

										snake_case:    List[Any]      							=tokenizer.tokenize('This is a test'			)
										self.assertListEqual(a_      ,						['▁This', '▁is', '▁a', '▁t', 'est']			)

										self.assertListEqual(tokenizer.convert_tokens_to_ids(a_			)      ,						[4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2]			)

										snake_case:    int      							=tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
										# fmt: off
										self.assertListEqual(
										    a_      ,						['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      ,						)
										# fmt: on

										snake_case:    Optional[int]      							=tokenizer.convert_tokens_to_ids(a_			)
										self.assertListEqual(
										    a_      ,						[2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0]      ,						)

										snake_case:    Optional[Any]      							=tokenizer.convert_ids_to_tokens(a_			)
										# fmt: off
										self.assertListEqual(
										    a_      ,						['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']			)
										# fmt: on


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										snake_case:    int      							=GPTSwaTokenizer(a_			)
										snake_case:    int      							=['This is a test', 'I was born in 92000, and this is falsé.']
										snake_case:    str      							=[
										    [4_6_5, 2_8_7, 2_6_5, 6_3_1, 8_4_2],
										    [2_6_2, 2_7_2, 1_5_2_5, 2_8_6, 2_7_1, 2_6_8, 6_0, 9_1_6, 6_3_3, 6_3_3, 6_3_3, 2_5_9, 2_6_6, 3_0_1, 2_8_7, 3_8_4, 3_6_7, 2_6_3, 1_9_8, 1_7_2, 2_6_0],
										]

										# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
										for text, expected_ids in zip(a_      ,						a_			):
													self.assertListEqual(tokenizer.encode_fast(a_			)      ,						a_			)

										# Test that decode_fast returns the input text
										for text, token_ids in zip(a_      ,						a_			):
													self.assertEqual(tokenizer.decode_fast(a_			)      ,						a_			)




							@slow
							def    UpperCamelCase     (				self   :						List[str]			)			->					int:
										snake_case:    str      							=[
										    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
										    'Hey there, how are you doing this fine day?',
										    'This is a text with a trailing spaces followed by a dot     .',
										    'Häj sväjs lillebrör! =)',
										    'Det är inget fel på Mr. Cool',
										]

										# fmt: off
										snake_case:    Any      							={'input_ids': [[6_3_4_2_3, 5, 6_8_1_1, 1_4_9_5_4, 2_8_2, 8_1_6, 3_8_2_1, 6_3_4_6_6, 6_3_4_2_5, 6_3_4_6_2, 1_8, 6_3_9_7_8, 6_7_8, 3_0_1, 1_3_2_0, 6_3_4_2_3, 6_3_4_5_5, 6_3_4_5_8, 1_8, 6_3_9_8_2, 4_2_4_6, 3_9_4_0, 1_9_0_1, 4_7_7_8_9, 5_5_4_7, 1_8_9_9_4], [1_9_6_3_0, 1_1_0_0, 6_3_4_4_6, 1_3_4_2, 6_3_3, 5_4_4, 4_4_8_8, 5_9_3, 5_1_0_2, 2_4_1_6, 6_3_4_9_5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_6_5_2, 4_2_8, 2_6_8, 1_9_3_6, 5_1_5, 2_6_8, 5_8_5_9_3, 2_2_4_1_3, 9_1_0_6, 5_4_6, 2_6_8, 3_3_2_1_3, 6_3_9_7_9, 6_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5_1_3_0, 6_3_4_5_0, 9_2_4, 6_3_4_4_9, 2_2_4_9, 4_0_6_2, 1_5_5_8, 3_1_8, 6_3_5_0_4, 2_1_4_9_8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_0_9, 3_7_7, 2_8_2_7, 2_5_5_9, 3_3_2, 6_5_7_5, 6_3_4_4_3, 2_6_8_0_1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
										# fmt: on
										self.tokenizer_integration_test_util(
										    expected_encoding=a_      ,						model_name='AI-Sweden/gpt-sw3-126m'      ,						sequences=a_      ,						)









'''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)




