






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   UpperCamelCase  (       snake_case__     :   Optional[Any]	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]  )  )
     __snake_case			:Optional[int]          =						nn.Parameter(
         torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]  )	,requires_grad=_lowerCamelCase  )
     for lyr_num, lyr in enumerate(model.encoders  ):
          __snake_case			:Optional[int]          =						weights[f'''layers_{lyr_num}''']
          __snake_case			:Optional[Any]          =						nn.Parameter(
              torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]  )  )

          __snake_case			:List[str]          =						ly_weight["attention"]
          __snake_case			:Union[str, Any]          =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T  )  )
          __snake_case			:Optional[int]          =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T  )  )
          __snake_case			:Any          =						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_mlp_layer_norm"""]["""scale"""]  )  )

          __snake_case			:Tuple          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T  )  )
          __snake_case			:Optional[Any]          =						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			:int          =						nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]  )  )
     return model






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   Dict  ):




     '''simple docstring'''






     __snake_case			:int          =						nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T  )  )

     __snake_case			:Union[str, Any]          =						nn.Parameter(
         torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]  )	,requires_grad=_lowerCamelCase  )

     for lyr_num, lyr in enumerate(model.encoders  ):
          __snake_case			:Dict          =						weights[f'''layers_{lyr_num}''']
          __snake_case			:List[str]          =						ly_weight["attention"]

          __snake_case			:Optional[Any]          =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T  )  )
          __snake_case			:Optional[Any]          =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T  )  )
          __snake_case			:Optional[int]          =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T  )  )
          __snake_case			:Dict          =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T  )  )
          __snake_case			:Dict          =						nn.Parameter(
              torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]  )  )

          __snake_case			:Tuple          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T  )  )
          __snake_case			:List[str]          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T  )  )
          __snake_case			:Tuple          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T  )  )
          __snake_case			:List[str]          =						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   UpperCamelCase  (       snake_case__     :   Dict	,snake_case__     :   Any  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T  )  )
     __snake_case			:Optional[int]          =						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T  )  )

     __snake_case			:int          =						nn.Parameter(
         torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]  )	,requires_grad=_lowerCamelCase  )

     __snake_case			:Optional[int]          =						nn.Parameter(
         torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T  )  )

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

          __snake_case			:Union[str, Any]          =						nn.Parameter(
              torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T  )  )

          __snake_case			:List[Any]          =						ly_weight["self_attention"]
          __snake_case			:Tuple          =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T  )  )
          __snake_case			:List[Any]          =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T  )  )
          __snake_case			:Optional[int]          =						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			:Union[str, Any]          =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T  )  )
          __snake_case			:List[Any]          =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T  )  )
          __snake_case			:str          =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T  )  )
          __snake_case			:List[str]          =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T  )  )
          __snake_case			:List[Any]          =						nn.Parameter(
              torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]  )  )

          __snake_case			:Union[str, Any]          =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]  )  )
          __snake_case			:str          =						nn.Parameter(
              torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T  )  )
          __snake_case			:Union[str, Any]          =						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			:Union[str, Any]          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T  )  )

     __snake_case			:Tuple          =						nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]  )  )

     __snake_case			:Optional[Any]          =						nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T  )  )

     return model






def   UpperCamelCase  (       snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						checkpoints.load_tax_checkpoint(args.checkpoint_path  )
     __snake_case			:List[str]          =						jnp.tree_util.tree_map(onp.array	,_lowerCamelCase  )

     __snake_case			:Dict          =						[
         "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			:int          =						os.path.join(args.checkpoint_path	,""".."""	,"""config.gin"""  )
     __snake_case			:int          =						inference.parse_training_gin_file(_lowerCamelCase	,_lowerCamelCase  )
     __snake_case			:List[Any]          =						inference.InferenceModel(args.checkpoint_path	,_lowerCamelCase  )

     __snake_case			:Dict          =						DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""	,variance_type="""fixed_large"""  )

     __snake_case			:int          =						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			: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"""	,)

     __snake_case			: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	,)

     __snake_case			:int          =						load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]	,_lowerCamelCase  )
     __snake_case			:str          =						load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]	,_lowerCamelCase  )
     __snake_case			:int          =						load_decoder(ta_checkpoint["""target"""]["""decoder"""]	,_lowerCamelCase  )

     __snake_case			:List[Any]          =						OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""  )

     __snake_case			:Optional[Any]          =						SpectrogramDiffusionPipeline(
         notes_encoder=_lowerCamelCase	,continuous_encoder=_lowerCamelCase	,decoder=_lowerCamelCase	,scheduler=_lowerCamelCase	,melgan=_lowerCamelCase	,)
     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)










import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402


lowerCamelCase__							=     get_tests_dir("""fixtures/dummy-config.json""")




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						0



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            self.assertIsNotNone(transformers.models.auto.__spec__    )
            self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto"""    )    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						AutoConfig.from_pretrained("""bert-base-uncased"""    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[str]          =						AutoConfig.from_pretrained(a__    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Tuple          =						AutoConfig.from_pretrained(a__    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						AutoConfig.for_model("""roberta"""    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            with tempfile.TemporaryDirectory() as tmp_dir:
                 # This model name contains bert and roberta, but roberta ends up being picked.
                 __snake_case			:List[Any]          =						os.path.join(a__			,						"""fake-roberta"""    )
                 os.makedirs(a__			,						exist_ok=a__    )
                 with open(os.path.join(a__			,						"""config.json"""    )			,						"""w"""    ) as f:
                      f.write(json.dumps({}    )    )
                 __snake_case			:Optional[Any]          =						AutoConfig.from_pretrained(a__    )
                 self.assertEqual(type(a__    )			,						a__    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            try:
                 AutoConfig.register("""custom"""			,						a__    )
                 # Wrong model type will raise an error
                 with self.assertRaises(a__    ):
                      AutoConfig.register("""model"""			,						a__    )
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(a__    ):
                      AutoConfig.register("""bert"""			,						a__    )

                 # Now that the config is registered, it can be used as any other config with the auto-API
                 __snake_case			:Optional[int]          =						CustomConfig()
                 with tempfile.TemporaryDirectory() as tmp_dir:
                      config.save_pretrained(a__    )
                      __snake_case			:Tuple          =						AutoConfig.from_pretrained(a__    )
                      self.assertIsInstance(a__			,						a__    )

            finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["custom"]



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						"""bert-base is not a local folder and is not a valid model identifier"""    ):
                 __snake_case			:str          =						AutoConfig.from_pretrained("""bert-base"""    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""    ):
                 __snake_case			:Dict          =						AutoConfig.from_pretrained(a__			,						revision="""aaaaaa"""    )



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."""			,						):
                 __snake_case			:Optional[int]          =						AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo"""    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            with self.assertRaises(a__    ):
                 __snake_case			:Dict          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""    )
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(a__    ):
                 __snake_case			:int          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )

            __snake_case			:Tuple          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
            self.assertEqual(config.__class__.__name__			,						"""NewModelConfig"""    )

            # Test config can be reloaded.
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(a__    )
                 __snake_case			:Union[str, Any]          =						AutoConfig.from_pretrained(a__			,						trust_remote_code=a__    )
            self.assertEqual(reloaded_config.__class__.__name__			,						"""NewModelConfig"""    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            class 			snake_case__   (							lowercase_):

                   '''simple docstring'''
                   lowerCamelCase							:				Optional[Any] 				= "new-model"

            try:
                 AutoConfig.register("""new-model"""			,						a__    )
                 # If remote code is not set, the default is to use local
                 __snake_case			:List[Any]          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfigLocal"""    )

                 # If remote code is disabled, we load the local one.
                 __snake_case			:Tuple          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfigLocal"""    )

                 # If remote is enabled, we load from the Hub
                 __snake_case			:List[Any]          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfig"""    )

            finally:
                 if "new-model" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["new-model"]


