






"""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


lowerCAmelCase_	:    List[Any]            =      '''base_with_context'''






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]		)		)
       UpperCAmelCase	    =			nn.Parameter(
           torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]		)	,      requires_grad=lowerCAmelCase		)
       for lyr_num, lyr in enumerate(model.encoders		):
              UpperCAmelCase	    =			weights[F'''layers_{lyr_num}''']
              UpperCAmelCase	    =			nn.Parameter(
                  torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]		)		)

              UpperCAmelCase	    =			ly_weight["""attention"""]
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T		)		)

              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]		)		)

              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T		)		)

       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]		)		)
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T		)		)

       UpperCAmelCase	    =			nn.Parameter(
           torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]		)	,      requires_grad=lowerCAmelCase		)

       for lyr_num, lyr in enumerate(model.encoders		):
              UpperCAmelCase	    =			weights[F'''layers_{lyr_num}''']
              UpperCAmelCase	    =			ly_weight["""attention"""]

              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(
                  torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]		)		)

              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]		)		)

       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]		)		)

       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T		)		)
       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T		)		)

       UpperCAmelCase	    =			nn.Parameter(
           torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]		)	,      requires_grad=lowerCAmelCase		)

       UpperCAmelCase	    =			nn.Parameter(
           torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T		)		)

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

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

              UpperCAmelCase	    =			ly_weight["""self_attention"""]
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T		)		)

              UpperCAmelCase	    =			ly_weight["""MultiHeadDotProductAttention_0"""]
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(
                  torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]		)		)

              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]		)		)
              UpperCAmelCase	    =			nn.Parameter(
                  torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T		)		)
              UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T		)		)

       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]		)		)

       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T		)		)

       return model






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			checkpoints.load_tax_checkpoint(args.checkpoint_path		)
       UpperCAmelCase	    =			jnp.tree_util.tree_map(onp.array	,      lowerCAmelCase		)

       UpperCAmelCase	    =			[
           """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()""",
       ]

       UpperCAmelCase	    =			os.path.join(args.checkpoint_path	,      """.."""	,      """config.gin"""		)
       UpperCAmelCase	    =			inference.parse_training_gin_file(lowerCAmelCase	,      lowerCAmelCase		)
       UpperCAmelCase	    =			inference.InferenceModel(args.checkpoint_path	,      lowerCAmelCase		)

       UpperCAmelCase	    =			DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""	,      variance_type="""fixed_large"""		)

       UpperCAmelCase	    =			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"""	,      )

       UpperCAmelCase	    =			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"""	,      )

       UpperCAmelCase	    =			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	,      )

       UpperCAmelCase	    =			load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]	,      lowerCAmelCase		)
       UpperCAmelCase	    =			load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]	,      lowerCAmelCase		)
       UpperCAmelCase	    =			load_decoder(ta_checkpoint["""target"""]["""decoder"""]	,      lowerCAmelCase		)

       UpperCAmelCase	    =			OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""		)

       UpperCAmelCase	    =			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_	:    Tuple            =      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_	:    Tuple            =      parser.parse_args()

 main(args)








"""simple docstring"""







import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class   UpperCamelCase_ :



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         torch.manual_seed(0				)
         UpperCAmelCase	    =			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			UNetaDConditionModel(
             sample_size=32 ,		layers_per_block=1 ,		block_out_channels=[32, 64] ,		down_block_types=[
                 """ResnetDownsampleBlock2D""",
                 """SimpleCrossAttnDownBlock2D""",
             ] ,		mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,		up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,		in_channels=3 ,		out_channels=6 ,		cross_attention_dim=32 ,		encoder_hid_dim=32 ,		attention_head_dim=8 ,		addition_embed_type="""text""" ,		addition_embed_type_num_heads=2 ,		cross_attention_norm="""group_norm""" ,		resnet_time_scale_shift="""scale_shift""" ,		act_fn="""gelu""" ,		)
         unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         torch.manual_seed(0				)
         UpperCAmelCase	    =			DDPMScheduler(
             num_train_timesteps=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		beta_end=0.02 ,		thresholding=snake_case__ ,		dynamic_thresholding_ratio=0.95 ,		sample_max_value=1.0 ,		prediction_type="""epsilon""" ,		variance_type="""learned_range""" ,		)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         torch.manual_seed(0				)
         UpperCAmelCase	    =			TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""				)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			UNetaDConditionModel(
             sample_size=32 ,		layers_per_block=[1, 2] ,		block_out_channels=[32, 64] ,		down_block_types=[
                 """ResnetDownsampleBlock2D""",
                 """SimpleCrossAttnDownBlock2D""",
             ] ,		mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,		up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,		in_channels=6 ,		out_channels=6 ,		cross_attention_dim=32 ,		encoder_hid_dim=32 ,		attention_head_dim=8 ,		addition_embed_type="""text""" ,		addition_embed_type_num_heads=2 ,		cross_attention_norm="""group_norm""" ,		resnet_time_scale_shift="""scale_shift""" ,		act_fn="""gelu""" ,		class_embed_type="""timestep""" ,		mid_block_scale_factor=1.414 ,		time_embedding_act_fn="""gelu""" ,		time_embedding_dim=32 ,		)
         unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         torch.manual_seed(0				)
         UpperCAmelCase	    =			DDPMScheduler(
             num_train_timesteps=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		beta_end=0.02 ,		thresholding=snake_case__ ,		dynamic_thresholding_ratio=0.95 ,		sample_max_value=1.0 ,		prediction_type="""epsilon""" ,		variance_type="""learned_range""" ,		)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			DDPMScheduler(
             num_train_timesteps=10_00 ,		beta_schedule="""squaredcos_cap_v2""" ,		beta_start=0.0_001 ,		beta_end=0.02 ,		)

         torch.manual_seed(0				)
         UpperCAmelCase	    =			IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "image_noising_scheduler": image_noising_scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)

         UpperCAmelCase	    =			inputs["""prompt"""]
         UpperCAmelCase	    =			inputs["""generator"""]
         UpperCAmelCase	    =			inputs["""num_inference_steps"""]
         UpperCAmelCase	    =			inputs["""output_type"""]

         if "image" in inputs:
                UpperCAmelCase	    =			inputs["""image"""]
         else:
                UpperCAmelCase	    =			None

         if "mask_image" in inputs:
                UpperCAmelCase	    =			inputs["""mask_image"""]
         else:
                UpperCAmelCase	    =			None

         if "original_image" in inputs:
                UpperCAmelCase	    =			inputs["""original_image"""]
         else:
                UpperCAmelCase	    =			None

         UpperCAmelCase    ,       UpperCAmelCase	    =			pipe.encode_prompt(snake_case__				)

         # inputs with prompt converted to embeddings
         UpperCAmelCase	    =			{
             """prompt_embeds""": prompt_embeds,
             """negative_prompt_embeds""": negative_prompt_embeds,
             """generator""": generator,
             """num_inference_steps""": num_inference_steps,
             """output_type""": output_type,
         }

         if image is not None:
                UpperCAmelCase	    =			image

         if mask_image is not None:
                UpperCAmelCase	    =			mask_image

         if original_image is not None:
                UpperCAmelCase	    =			original_image

         # set all optional components to None
         for optional_component in pipe._optional_components:
                setattr(snake_case__ ,		snake_case__ ,		snake_case__				)

         UpperCAmelCase	    =			pipe(**snake_case__				)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
                pipe.save_pretrained(snake_case__				)
                UpperCAmelCase	    =			self.pipeline_class.from_pretrained(snake_case__				)
                pipe_loaded.to(snake_case__				)
                pipe_loaded.set_progress_bar_config(disable=snake_case__				)

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         for optional_component in pipe._optional_components:
                self.assertTrue(
                    getattr(snake_case__ ,		snake_case__				) is None ,		f'''`{optional_component}` did not stay set to None after loading.''' ,		)

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)

         UpperCAmelCase	    =			inputs["""generator"""]
         UpperCAmelCase	    =			inputs["""num_inference_steps"""]
         UpperCAmelCase	    =			inputs["""output_type"""]

         # inputs with prompt converted to embeddings
         UpperCAmelCase	    =			{
             """prompt_embeds""": prompt_embeds,
             """negative_prompt_embeds""": negative_prompt_embeds,
             """generator""": generator,
             """num_inference_steps""": num_inference_steps,
             """output_type""": output_type,
         }

         if image is not None:
                UpperCAmelCase	    =			image

         if mask_image is not None:
                UpperCAmelCase	    =			mask_image

         if original_image is not None:
                UpperCAmelCase	    =			original_image

         UpperCAmelCase	    =			pipe_loaded(**snake_case__				)[0]

         UpperCAmelCase	    =			np.abs(to_np(snake_case__				) - to_np(snake_case__				)				).max()
         self.assertLess(snake_case__ ,		1e-4				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_dummy_components()
         UpperCAmelCase	    =			self.pipeline_class(**snake_case__				)
         pipe.to(snake_case__				)
         pipe.set_progress_bar_config(disable=snake_case__				)

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)
         UpperCAmelCase	    =			pipe(**snake_case__				)[0]

         with tempfile.TemporaryDirectory() as tmpdir:
                pipe.save_pretrained(snake_case__				)
                UpperCAmelCase	    =			self.pipeline_class.from_pretrained(snake_case__				)
                pipe_loaded.to(snake_case__				)
                pipe_loaded.set_progress_bar_config(disable=snake_case__				)

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()				)  # For reproducibility tests

         UpperCAmelCase	    =			self.get_dummy_inputs(snake_case__				)
         UpperCAmelCase	    =			pipe_loaded(**snake_case__				)[0]

         UpperCAmelCase	    =			np.abs(to_np(snake_case__				) - to_np(snake_case__				)				).max()
         self.assertLess(snake_case__ ,		1e-4				)
