


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


snake_case							:       int										=	'''base_with_context'''
def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Optional[int]		):




      """simple docstring"""





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

            a		:str									=			ly_weight['''attention''']
            a		:Optional[Any]									=			nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
            a		:List[str]									=			nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T		)		)
            a		:Union[str, Any]									=			nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T		)		)
            a		:Union[str, Any]									=			nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T		)		)

            a		:Tuple									=			nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']		)		)

            a		:List[Any]									=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
            a		:Dict									=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T		)		)
            a		:Tuple									=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)

      a		:Optional[int]									=			nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']		)		)
      return model
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Optional[int]		):




      """simple docstring"""





      a		:Union[str, Any]									=			nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T		)		)

      a		:List[str]									=			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		:Tuple									=			ly_weight['''attention''']

            a		:Dict									=			nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
            a		:Tuple									=			nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T		)		)
            a		:Any									=			nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T		)		)
            a		:Dict									=			nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T		)		)
            a		:Any									=			nn.Parameter(
                torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']		)		)

            a		:List[str]									=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
            a		:List[str]									=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T		)		)
            a		:Tuple									=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)
            a		:Optional[Any]									=			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 							__lowerCamelCase		(					UpperCAmelCase_		:						List[Any]					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





      a		:Union[str, Any]									=			nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T		)		)
      a		:str									=			nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T		)		)

      a		:str									=			nn.Parameter(
          torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)					,    requires_grad=UpperCAmelCase_		)

      a		:int									=			nn.Parameter(
          torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T		)		)

      for lyr_num, lyr in enumerate(model.decoders		):
            a		:int									=			weights[F'''layers_{lyr_num}''']
            a		:int									=			nn.Parameter(
                torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']		)		)

            a		:Union[str, Any]									=			nn.Parameter(
                torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T		)		)

            a		:List[str]									=			ly_weight['''self_attention''']
            a		:int									=			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		:Union[str, Any]									=			nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T		)		)

            a		:Any									=			ly_weight['''MultiHeadDotProductAttention_0''']
            a		:Union[str, Any]									=			nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
            a		:List[str]									=			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		:Union[str, Any]									=			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		:Dict									=			nn.Parameter(
                torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T		)		)
            a		:List[str]									=			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		:Optional[Any]									=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)

      a		:Dict									=			nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']		)		)

      a		:Dict									=			nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T		)		)

      return model
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Union[str, Any]		):




      """simple docstring"""





      a		:Optional[int]									=			checkpoints.load_tax_checkpoint(args.checkpoint_path		)
      a		:List[Any]									=			jnp.tree_util.tree_map(onp.array					,    UpperCAmelCase_		)

      a		:Optional[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(UpperCAmelCase_					,    UpperCAmelCase_		)
      a		:str									=			inference.InferenceModel(args.checkpoint_path					,    UpperCAmelCase_		)

      a		:Union[str, Any]									=			DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''					,    variance_type='''fixed_large'''		)

      a		: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		:Tuple									=			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		: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					,    )

      a		:Dict									=			load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']					,    UpperCAmelCase_		)
      a		:List[str]									=			load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']					,    UpperCAmelCase_		)
      a		:List[str]									=			load_decoder(ta_checkpoint['''target''']['''decoder''']					,    UpperCAmelCase_		)

      a		:Dict									=			OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''		)

      a		: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__":
     snake_case							:       List[str]										=	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.''',
     )
     snake_case							:       int										=	parser.parse_args()

     main(args)












'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			from transformers.models.roberta.modeling_flax_roberta import (
			    FlaxRobertaForCausalLM,
			    FlaxRobertaForMaskedLM,
			    FlaxRobertaForMultipleChoice,
			    FlaxRobertaForQuestionAnswering,
			    FlaxRobertaForSequenceClassification,
			    FlaxRobertaForTokenClassification,
			    FlaxRobertaModel,
			)






class 				__snake_case     (					unittest.TestCase							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=7      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=99      ,       UpperCamelCase_=32      ,       UpperCamelCase_=5      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=512      ,       UpperCamelCase_=16      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=4      ,       )      ->		List[Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   seq_length
		snake_case__	       =   is_training
		snake_case__	       =   use_attention_mask
		snake_case__	       =   use_token_type_ids
		snake_case__	       =   use_labels
		snake_case__	       =   vocab_size
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   max_position_embeddings
		snake_case__	       =   type_vocab_size
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   num_choices





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size		)

		snake_case__	       =   None
		if self.use_attention_mask:
			snake_case__	       =   random_attention_mask([self.batch_size, self.seq_length]		)

		snake_case__	       =   None
		if self.use_token_type_ids:
			snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       self.type_vocab_size		)

		snake_case__	       =   RobertaConfig(
		    vocab_size=self.vocab_size      ,       hidden_size=self.hidden_size      ,       num_hidden_layers=self.num_hidden_layers      ,       num_attention_heads=self.num_attention_heads      ,       intermediate_size=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       max_position_embeddings=self.max_position_embeddings      ,       type_vocab_size=self.type_vocab_size      ,       is_decoder=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )

		return config, input_ids, token_type_ids, attention_mask





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
		return config, inputs_dict





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs

		snake_case__	       =   True
		snake_case__	       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
		snake_case__	       =   ids_tensor([self.batch_size, self.seq_length]      ,       vocab_size=2		)

		return (
		    config,
		    input_ids,
		    token_type_ids,
		    encoder_hidden_states,
		    encoder_attention_mask,
		)






@require_flax
class 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  True

	__lowerCAmelCase       						=  (
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   FlaxRobertaModelTester(self		)





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_class_name in self.all_model_classes:
			snake_case__	       =   model_class_name.from_pretrained('roberta-base'      ,       from_pt=UpperCamelCase_		)
			snake_case__	       =   model(np.ones((1, 1)		)		)
			self.assertIsNotNone(UpperCamelCase_		)



