





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


UpperCAmelCase     :     Dict							=	"""base_with_context"""
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



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

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

    a__     : Any  =nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

    a__     : Dict  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
    a__     : Optional[Any]  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
    a__     : List[Any]  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

  a__     : int  =nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)
  return model
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  a__     : List[str]  =nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T						)						)

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

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

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

    a__     : Tuple  =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__     : Tuple  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)
    a__     : Tuple  =nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

  a__     : List[str]  =nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)

  return model
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



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

  a__     : Dict  =nn.Parameter(
      torch.FloatTensor(weights["Embed_0"]["embedding"]						)	,	requires_grad=SCREAMING_SNAKE_CASE						)

  a__     : str  =nn.Parameter(
      torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T						)						)

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

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

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

    a__     : Tuple  =ly_weight["MultiHeadDotProductAttention_0"]
    a__     : Tuple  =nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
    a__     : Optional[int]  =nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
    a__     : Any  =nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
    a__     : str  =nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
    a__     : List[str]  =nn.Parameter(
        torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]						)						)

    a__     : List[Any]  =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__     : str  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
    a__     : Any  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
    a__     : List[str]  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

  a__     : Union[str, Any]  =nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]						)						)

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

  return model
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  a__     : Union[str, Any]  =checkpoints.load_tax_checkpoint(args.checkpoint_path						)
  a__     : int  =jnp.tree_util.tree_map(onp.array	,	SCREAMING_SNAKE_CASE						)

  a__     : List[Any]  =[
      "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__     : Union[str, Any]  =os.path.join(args.checkpoint_path	,	".."	,	"config.gin"						)
  a__     : Tuple  =inference.parse_training_gin_file(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  a__     : Tuple  =inference.InferenceModel(args.checkpoint_path	,	SCREAMING_SNAKE_CASE						)

  a__     : Union[str, Any]  =DDPMScheduler(beta_schedule="squaredcos_cap_v2"	,	variance_type="fixed_large"						)

  a__     : 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"	,	)

  a__     : 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"	,	)

  a__     : 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	,	)

  a__     : Dict  =load_notes_encoder(ta_checkpoint["target"]["token_encoder"]	,	SCREAMING_SNAKE_CASE						)
  a__     : Tuple  =load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]	,	SCREAMING_SNAKE_CASE						)
  a__     : List[str]  =load_decoder(ta_checkpoint["target"]["decoder"]	,	SCREAMING_SNAKE_CASE						)

  a__     : Optional[int]  =OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"						)

  a__     : Optional[Any]  =SpectrogramDiffusionPipeline(
      notes_encoder=SCREAMING_SNAKE_CASE	,	continuous_encoder=SCREAMING_SNAKE_CASE	,	decoder=SCREAMING_SNAKE_CASE	,	scheduler=SCREAMING_SNAKE_CASE	,	melgan=SCREAMING_SNAKE_CASE	,	)
  if args.save:
    pipe.save_pretrained(args.output_path						)


if __name__ == "__main__":
     UpperCAmelCase     :     List[Any]							=	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.""",
     )
     UpperCAmelCase     :     Optional[Any]							=	parser.parse_args()

     main(args)













import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
UpperCAmelCase     :     List[Any]							=	{"""LayoutLMv2Config""", """LayoutLMv3Config"""}







@is_pipeline_test
class     __lowerCAmelCase	(   unittest.TestCase):
      _lowercase    : str            =      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      _lowercase    : Tuple            =      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            _lowercase    : Dict            =      {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            _lowercase    : int            =      {
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }







      @require_torch
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : str  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"							)

        a__     : Any  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : Any  =text_classifier("This is great !"		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]							)

        a__     : Tuple  =text_classifier(["This is great !", "This is bad"]		,				top_k=2							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : List[Any]  =text_classifier("This is great !"		,				top_k=1							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        # Legacy behavior
        a__     : Any  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)

        a__     : List[str]  =text_classifier("This is great !"		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

        a__     : Optional[int]  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
                [{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}],
            ]		,				)

        a__     : int  =text_classifier(["This is great !", "Something else"]		,				return_all_scores=lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[
                {"label": "LABEL_0", "score": 0.5_04},
                {"label": "LABEL_0", "score": 0.5_04},
            ]		,				)







      @require_torch
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        import torch

        a__     : Dict  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="pt"		,				device=torch.device("cpu"							)		,				)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @require_tf
      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Optional[int]  =pipeline(
            task="text-classification"		,				model="hf-internal-testing/tiny-random-distilbert"		,				framework="tf"							)

        a__     : Optional[Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "LABEL_0", "score": 0.5_04}]							)







      @slow
      @require_torch
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Union[str, Any]  =pipeline("text-classification"							)

        a__     : Union[str, Any]  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : Optional[Any]  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      @slow
      @require_tf
      def        _lowercase  (   self							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Tuple  =pipeline("text-classification"		,				framework="tf"							)

        a__     : str  =text_classifier("This is great !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 1.0}]							)
        a__     : str  =text_classifier("This is bad !"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "NEGATIVE", "score": 1.0}]							)
        a__     : Dict  =text_classifier("Birds are a type of animal"							)
        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": "POSITIVE", "score": 0.9_88}]							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : List[Any]  =TextClassificationPipeline(model=lowerCAmelCase__		,				tokenizer=lowerCAmelCase__							)
        return text_classifier, ["HuggingFace is in", "This is another test"]







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        a__     : Tuple  =text_classifier.model
        # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
        a__     : List[Any]  ="HuggingFace is in"
        a__     : int  =text_classifier(lowerCAmelCase__							)

        self.assertEqual(nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]							)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)

        a__     : Optional[int]  =["HuggingFace is in ", "Paris is in France"]
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}, {"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)
        self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()							)

        # Forcing to get all results with `top_k=None`
        # This is NOT the legacy format
        a__     : Union[str, Any]  =text_classifier(lowerCAmelCase__		,				top_k=lowerCAmelCase__							)
        a__     : Optional[Any]  =len(model.config.idalabel.values()							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N, [{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}] * N]		,				)

        a__     : List[str]  ={"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
        a__     : Optional[Any]  =text_classifier(lowerCAmelCase__							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}		,				)
        self.assertTrue(outputs["label"] in model.config.idalabel.values()							)

        # This might be used a text pair, but tokenizer + pipe interaction
        # makes it hard to understand that it's not using the pair properly
        # https://github.com/huggingface/transformers/issues/17305
        # We disabled this usage instead as it was outputting wrong outputs.
        a__     : Any  =[["HuggingFace is in ", "Paris is in France"]]
        with self.assertRaises(lowerCAmelCase__							):
          text_classifier(lowerCAmelCase__							)

        # This used to be valid for doing text pairs
        # We're keeping it working because of backward compatibility
        a__     : Optional[int]  =text_classifier([[["HuggingFace is in ", "Paris is in France"]]]							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase__							)		,				[{"label": ANY(lowerCAmelCase__							), "score": ANY(lowerCAmelCase__							)}]		,				)
        self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()							)






