
'''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


snake_case									=    """base_with_context"""


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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

  lowerCAmelCase__     :   Tuple     		=	ly_weight["attention"]
  lowerCAmelCase__     :   List[Any]     		=	nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T			)			)
  lowerCAmelCase__     :   Union[str, Any]     		=	nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T			)			)
  lowerCAmelCase__     :   Tuple     		=	nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T			)			)
  lowerCAmelCase__     :   Dict     		=	nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T			)			)

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

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

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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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

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

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

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

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

 lowerCAmelCase__     :   Any     		=	nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]			)			)

 return model


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T			)			)
 lowerCAmelCase__     :   Optional[Any]     		=	nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T			)			)

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

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

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

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

  lowerCAmelCase__     :   Optional[int]     		=	ly_weight["self_attention"]
  lowerCAmelCase__     :   Any     		=	nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T			)			)
  lowerCAmelCase__     :   Optional[int]     		=	nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T			)			)
  lowerCAmelCase__     :   Optional[int]     		=	nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T			)			)
  lowerCAmelCase__     :   Any     		=	nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T			)			)

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

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

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

 lowerCAmelCase__     :   Union[str, Any]     		=	nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T			)			)

 return model


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	checkpoints.load_tax_checkpoint(args.checkpoint_path			)
 lowerCAmelCase__     :   Any     		=	jnp.tree_util.tree_map(onp.array      ,			lowerCamelCase_			)

 lowerCAmelCase__     :   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()",
 ]

 lowerCAmelCase__     :   Any     		=	os.path.join(args.checkpoint_path      ,			".."      ,			"config.gin"			)
 lowerCAmelCase__     :   int     		=	inference.parse_training_gin_file(lowerCamelCase_      ,			lowerCamelCase_			)
 lowerCAmelCase__     :   str     		=	inference.InferenceModel(args.checkpoint_path      ,			lowerCamelCase_			)

 lowerCAmelCase__     :   List[Any]     		=	DDPMScheduler(beta_schedule="squaredcos_cap_v2"      ,			variance_type="fixed_large"			)

 lowerCAmelCase__     :   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"      ,			)

 lowerCAmelCase__     :   Union[str, 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"      ,			)

 lowerCAmelCase__     :   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      ,			)

 lowerCAmelCase__     :   int     		=	load_notes_encoder(ta_checkpoint["target"]["token_encoder"]      ,			lowerCamelCase_			)
 lowerCAmelCase__     :   Dict     		=	load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]      ,			lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[Any]     		=	load_decoder(ta_checkpoint["target"]["decoder"]      ,			lowerCamelCase_			)

 lowerCAmelCase__     :   str     		=	OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"			)

 lowerCAmelCase__     :   List[str]     		=	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__":
     snake_case									=    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									=    parser.parse_args()

     main(args)







'''simple docstring'''

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


snake_case									=    ["""text""", """image""", """audio"""]


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]

 for input_type in input_types:
  if input_type == "text":
   inputs.append("Text input"			)
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			).resize((5_1_2, 5_1_2)			)			)
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0			)			)
  elif isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
   inputs.append(create_inputs(lowerCamelCase_			)			)
  else:
   raise ValueError(f'''Invalid type requested: {input_type}'''			)

 return inputs


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]

 for output in outputs:
  if isinstance(lowerCamelCase_      ,			(str, AgentText)			):
   output_types.append("text"			)
  elif isinstance(lowerCamelCase_      ,			(Image.Image, AgentImage)			):
   output_types.append("image"			)
  elif isinstance(lowerCamelCase_      ,			(torch.Tensor, AgentAudio)			):
   output_types.append("audio"			)
  else:
   raise ValueError(f'''Invalid output: {output}'''			)

 return output_types







@is_tool_test
class    lowerCAmelCase       :


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.assertTrue(hasattr(self.tool			,							"inputs"    )    )
  self.assertTrue(hasattr(self.tool			,							"outputs"    )    )

  lowerCAmelCase__     :   Dict     		=	self.tool.inputs
  for _input in inputs:
   if isinstance(_input			,							a__    ):
    for __input in _input:
     self.assertTrue(__input in authorized_types    )
   else:
    self.assertTrue(_input in authorized_types    )

  lowerCAmelCase__     :   List[Any]     		=	self.tool.outputs
  for _output in outputs:
   self.assertTrue(_output in authorized_types    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	create_inputs(self.tool.inputs    )
  lowerCAmelCase__     :   str     		=	self.tool(*a__    )

  # There is a single output
  if len(self.tool.outputs    ) == 1:
   lowerCAmelCase__     :   Union[str, Any]     		=	[outputs]

  self.assertListEqual(output_types(a__    )			,							self.tool.outputs    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.assertTrue(hasattr(self.tool			,							"description"    )    )
  self.assertTrue(hasattr(self.tool			,							"default_checkpoint"    )    )
  self.assertTrue(self.tool.description.startswith("This is a tool that"    )    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	create_inputs(self.tool.inputs    )
  lowerCAmelCase__     :   str     		=	self.tool(*a__    )

  if not isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   List[str]     		=	[outputs]

  self.assertEqual(len(a__    )			,							len(self.tool.outputs    )    )

  for output, output_type in zip(a__			,							self.tool.outputs    ):
   lowerCAmelCase__     :   List[Any]     		=	AGENT_TYPE_MAPPING[output_type]
   self.assertTrue(isinstance(a__			,							a__    )    )


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	create_inputs(self.tool.inputs    )

  lowerCAmelCase__     :   Dict     		=	[]

  for _input, input_type in zip(a__			,							self.tool.inputs    ):
   if isinstance(a__			,							a__    ):
    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
   else:
    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
  lowerCAmelCase__     :   Any     		=	self.tool(*a__    )

  if not isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   Tuple     		=	[outputs]

  self.assertEqual(len(a__    )			,							len(self.tool.outputs    )    )





