


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

              _A      :       Optional[int]				    = ly_weight["attention"]
              _A      :       str				    = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
              _A      :       Optional[int]				    = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
              _A      :       List[str]				    = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
              _A      :       List[Any]				    = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)

              _A      :       List[str]				    = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

              _A      :       Union[str, Any]				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
              _A      :       List[Any]				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
              _A      :       Optional[Any]				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

       _A      :       str				    = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)
       return model
def 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  Tuple						):
       _A      :       Any				    = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T						)						)

       _A      :       List[Any]				    = nn.Parameter(
           torch.FloatTensor(weights["Embed_0"]["embedding"]						)				,							requires_grad=UpperCamelCase__						)

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

              _A      :       Union[str, Any]				    = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
              _A      :       Union[str, Any]				    = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
              _A      :       Tuple				    = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
              _A      :       Dict				    = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
              _A      :       Optional[Any]				    = nn.Parameter(
                  torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]						)						)

              _A      :       Union[str, Any]				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
              _A      :       Tuple				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
              _A      :       str				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)
              _A      :       Dict				    = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

       _A      :       Optional[int]				    = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)

       return model
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]						):
       _A      :       Optional[int]				    = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T						)						)
       _A      :       Any				    = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T						)						)

       _A      :       List[str]				    = nn.Parameter(
           torch.FloatTensor(weights["Embed_0"]["embedding"]						)				,							requires_grad=UpperCamelCase__						)

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

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

              _A      :       Dict				    = nn.Parameter(
                  torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T						)						)

              _A      :       List[Any]				    = ly_weight["self_attention"]
              _A      :       List[Any]				    = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
              _A      :       List[str]				    = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
              _A      :       str				    = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
              _A      :       List[str]				    = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)

              _A      :       Union[str, Any]				    = ly_weight["MultiHeadDotProductAttention_0"]
              _A      :       Any				    = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
              _A      :       Any				    = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
              _A      :       Any				    = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
              _A      :       Any				    = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
              _A      :       List[Any]				    = nn.Parameter(
                  torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]						)						)

              _A      :       int				    = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)
              _A      :       Any				    = nn.Parameter(
                  torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T						)						)
              _A      :       List[Any]				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
              _A      :       Optional[int]				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
              _A      :       int				    = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

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

       _A      :       Tuple				    = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T						)						)

       return model
def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       _A      :       Dict				    = checkpoints.load_tax_checkpoint(args.checkpoint_path						)
       _A      :       Dict				    = jnp.tree_util.tree_map(onp.array				,							UpperCamelCase__						)

       _A      :       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()",
       ]

       _A      :       str				    = os.path.join(args.checkpoint_path				,							".."				,							"config.gin"						)
       _A      :       Tuple				    = inference.parse_training_gin_file(UpperCamelCase__				,							UpperCamelCase__						)
       _A      :       int				    = inference.InferenceModel(args.checkpoint_path				,							UpperCamelCase__						)

       _A      :       List[str]				    = DDPMScheduler(beta_schedule="squaredcos_cap_v2"				,							variance_type="fixed_large"						)

       _A      :       List[str]				    = 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"				,							)

       _A      :       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"				,							)

       _A      :       Dict				    = 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				,							)

       _A      :       List[Any]				    = load_notes_encoder(ta_checkpoint["target"]["token_encoder"]				,							UpperCamelCase__						)
       _A      :       List[Any]				    = load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]				,							UpperCamelCase__						)
       _A      :       int				    = load_decoder(ta_checkpoint["target"]["decoder"]				,							UpperCamelCase__						)

       _A      :       Tuple				    = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"						)

       _A      :       List[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__":
    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 copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test







class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @parameterized.expand([(None,), ("foo.json",)])
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				List[str]:
            _A      :       str				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						bad_words_ids=[[1, 2, 3], [4, 5]] ,						)
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(__lowerCamelCase ,						config_name=__lowerCamelCase)
                   _A      :       Tuple				    = GenerationConfig.from_pretrained(__lowerCamelCase ,						config_name=__lowerCamelCase)

            # Checks parameters that were specified
            self.assertEqual(loaded_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(loaded_config.temperature ,						0.7)
            self.assertEqual(loaded_config.length_penalty ,						1.0)
            self.assertEqual(loaded_config.bad_words_ids ,						[[1, 2, 3], [4, 5]])

            # Checks parameters that were not specified (defaults)
            self.assertEqual(loaded_config.top_k ,						5_0)
            self.assertEqual(loaded_config.max_length ,						2_0)
            self.assertEqual(loaded_config.max_time ,						__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       Optional[int]				    = AutoConfig.from_pretrained("gpt2")
            _A      :       int				    = GenerationConfig.from_model_config(__lowerCamelCase)
            _A      :       List[Any]				    = GenerationConfig()

            # The generation config has loaded a few non-default parameters from the model config
            self.assertNotEqual(__lowerCamelCase ,						__lowerCamelCase)

            # One of those parameters is eos_token_id -- check if it matches
            self.assertNotEqual(generation_config_from_model.eos_token_id ,						default_generation_config.eos_token_id)
            self.assertEqual(generation_config_from_model.eos_token_id ,						model_config.eos_token_id)


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       Optional[Any]				    = GenerationConfig()
            _A      :       List[Any]				    = {
                "max_new_tokens": 1_0_2_4,
                "foo": "bar",
            }
            _A      :       List[str]				    = copy.deepcopy(__lowerCamelCase)
            _A      :       int				    = generation_config.update(**__lowerCamelCase)

            # update_kwargs was not modified (no side effects)
            self.assertEqual(__lowerCamelCase ,						__lowerCamelCase)

            # update_kwargs was used to update the config on valid attributes
            self.assertEqual(generation_config.max_new_tokens ,						1_0_2_4)

            # `.update()` returns a dictionary of unused kwargs
            self.assertEqual(__lowerCamelCase ,						{"foo": "bar"})


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       int				    = GenerationConfig()
            _A      :       int				    = "bar"

            with tempfile.TemporaryDirectory("test-generation-config") as tmp_dir:
                   generation_config.save_pretrained(__lowerCamelCase)

                   _A      :       Any				    = GenerationConfig.from_pretrained(__lowerCamelCase)
            # update_kwargs was used to update the config on valid attributes
            self.assertEqual(new_config.foo ,						"bar")

            _A      :       Optional[Any]				    = GenerationConfig.from_model_config(__lowerCamelCase)
            assert not hasattr(__lowerCamelCase ,						"foo")  # no new kwargs should be initialized if from config


     def  _lowerCamelCase (						self)			->				List[str]:
            _A      :       Union[str, Any]				    = GenerationConfig()
            self.assertEqual(default_config.temperature ,						1.0)
            self.assertEqual(default_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(default_config.num_beams ,						1)

            _A      :       Optional[int]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						bad_words_ids=[[1, 2, 3], [4, 5]] ,						)
            self.assertEqual(config.temperature ,						0.7)
            self.assertEqual(config.do_sample ,						__lowerCamelCase)
            self.assertEqual(config.num_beams ,						1)

            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(__lowerCamelCase)
                   _A      :       Optional[int]				    = GenerationConfig.from_pretrained(__lowerCamelCase ,						temperature=1.0)

            self.assertEqual(loaded_config.temperature ,						1.0)
            self.assertEqual(loaded_config.do_sample ,						__lowerCamelCase)
            self.assertEqual(loaded_config.num_beams ,						1)  # default value







@is_staging_test
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @classmethod
     def  _lowerCamelCase (						cls)			->				Optional[int]:
            _A      :       Dict				    = TOKEN
            HfFolder.save_token(__lowerCamelCase)


     @classmethod
     def  _lowerCamelCase (						cls)			->				List[Any]:
            try:
                   delete_repo(token=cls._token ,						repo_id="test-generation-config")
            except HTTPError:
                   pass

            try:
                   delete_repo(token=cls._token ,						repo_id="valid_org/test-generation-config-org")
            except HTTPError:
                   pass


     def  _lowerCamelCase (						self)			->				Any:
            _A      :       Optional[int]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						)
            config.push_to_hub("test-generation-config" ,						use_auth_token=self._token)

            _A      :       Union[str, Any]				    = GenerationConfig.from_pretrained(F"{USER}/test-generation-config")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))

        # Reset repo
            delete_repo(token=self._token ,						repo_id="test-generation-config")

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(
                       __lowerCamelCase ,						repo_id="test-generation-config" ,						push_to_hub=__lowerCamelCase ,						use_auth_token=self._token)

            _A      :       Optional[Any]				    = GenerationConfig.from_pretrained(F"{USER}/test-generation-config")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Union[str, Any]				    = GenerationConfig(
                do_sample=__lowerCamelCase ,						temperature=0.7 ,						length_penalty=1.0 ,						)
            config.push_to_hub("valid_org/test-generation-config-org" ,						use_auth_token=self._token)

            _A      :       int				    = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))

        # Reset repo
            delete_repo(token=self._token ,						repo_id="valid_org/test-generation-config-org")

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                   config.save_pretrained(
                       __lowerCamelCase ,						repo_id="valid_org/test-generation-config-org" ,						push_to_hub=__lowerCamelCase ,						use_auth_token=self._token)

            _A      :       Optional[int]				    = GenerationConfig.from_pretrained("valid_org/test-generation-config-org")
            for k, v in config.to_dict().items():
                   if k != "transformers_version":
                          self.assertEqual(__lowerCamelCase ,						getattr(__lowerCamelCase ,						__lowerCamelCase))
