

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
           OpenAIGPTConfig,
           OpenAIGPTDoubleHeadsModel,
           OpenAIGPTForSequenceClassification,
           OpenAIGPTLMHeadModel,
           OpenAIGPTModel,
       )






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		Any     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		List[Any]=13     ,       __lowerCamelCase						:		Optional[int]=7     ,       __lowerCamelCase						:		List[Any]=True     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		Any=True     ,       __lowerCamelCase						:		Any=99     ,       __lowerCamelCase						:		Union[str, Any]=32     ,       __lowerCamelCase						:		List[str]=5     ,       __lowerCamelCase						:		Any=4     ,       __lowerCamelCase						:		Union[str, Any]=37     ,       __lowerCamelCase						:		Union[str, Any]="gelu"     ,       __lowerCamelCase						:		Tuple=0.1     ,       __lowerCamelCase						:		Any=0.1     ,       __lowerCamelCase						:		List[Any]=512     ,       __lowerCamelCase						:		Optional[Any]=16     ,       __lowerCamelCase						:		str=2     ,       __lowerCamelCase						:		List[str]=0.02     ,       __lowerCamelCase						:		Dict=3     ,       __lowerCamelCase						:		Union[str, Any]=4     ,       __lowerCamelCase						:		Tuple=None     ,       )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   parent
       SCREAMING_SNAKE_CASE__   =   batch_size
       SCREAMING_SNAKE_CASE__   =   seq_length
       SCREAMING_SNAKE_CASE__   =   is_training
       SCREAMING_SNAKE_CASE__   =   use_token_type_ids
       SCREAMING_SNAKE_CASE__   =   use_labels
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   max_position_embeddings
       SCREAMING_SNAKE_CASE__   =   type_vocab_size
       SCREAMING_SNAKE_CASE__   =   type_sequence_label_size
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   num_labels
       SCREAMING_SNAKE_CASE__   =   num_choices
       SCREAMING_SNAKE_CASE__   =   scope
       SCREAMING_SNAKE_CASE__   =   self.vocab_size - 1






  def 				lowercase_		(							self						:		Any )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size )

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

       SCREAMING_SNAKE_CASE__   =   None
       SCREAMING_SNAKE_CASE__   =   None
       SCREAMING_SNAKE_CASE__   =   None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__   =   ids_tensor([self.batch_size]     ,       self.type_sequence_label_size )
            SCREAMING_SNAKE_CASE__   =   ids_tensor([self.batch_size, self.seq_length]     ,       self.num_labels )
            SCREAMING_SNAKE_CASE__   =   ids_tensor([self.batch_size]     ,       self.num_choices )

       SCREAMING_SNAKE_CASE__   =   OpenAIGPTConfig(
           vocab_size=self.vocab_size     ,       n_embd=self.hidden_size     ,       n_layer=self.num_hidden_layers     ,       n_head=self.num_attention_heads     ,       n_positions=self.max_position_embeddings     ,       pad_token_id=self.pad_token_id     ,       )

       SCREAMING_SNAKE_CASE__   =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,       2 )

       return (
           config,
           input_ids,
           head_mask,
           token_type_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )






  def 				lowercase_		(							self						:		Optional[int]     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		Optional[Any]     ,       *__lowerCamelCase						:		int )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   OpenAIGPTModel(config=_lowerCamelCase )
       model.to(_lowerCamelCase )
       model.eval()

       SCREAMING_SNAKE_CASE__   =   model(_lowerCamelCase     ,       token_type_ids=_lowerCamelCase     ,       head_mask=_lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   model(_lowerCamelCase     ,       token_type_ids=_lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   model(_lowerCamelCase )

       self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size) )






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		List[str]     ,       *__lowerCamelCase						:		Tuple )		->							Any:
       SCREAMING_SNAKE_CASE__   =   OpenAIGPTLMHeadModel(_lowerCamelCase )
       model.to(_lowerCamelCase )
       model.eval()

       SCREAMING_SNAKE_CASE__   =   model(_lowerCamelCase     ,       token_type_ids=_lowerCamelCase     ,       labels=_lowerCamelCase )
       self.parent.assertEqual(result.loss.shape     ,       () )
       self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size) )






  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		Any     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		int     ,       *__lowerCamelCase						:		str )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   OpenAIGPTDoubleHeadsModel(_lowerCamelCase )
       model.to(_lowerCamelCase )
       model.eval()

       SCREAMING_SNAKE_CASE__   =   model(_lowerCamelCase     ,       token_type_ids=_lowerCamelCase     ,       labels=_lowerCamelCase )
       self.parent.assertEqual(result.loss.shape     ,       () )
       self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size) )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		Union[str, Any]     ,       __lowerCamelCase						:		List[str]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		str     ,       *__lowerCamelCase						:		str )		->							Any:
       SCREAMING_SNAKE_CASE__   =   self.num_labels
       SCREAMING_SNAKE_CASE__   =   OpenAIGPTForSequenceClassification(_lowerCamelCase )
       model.to(_lowerCamelCase )
       model.eval()

       SCREAMING_SNAKE_CASE__   =   ids_tensor([self.batch_size]     ,       self.type_sequence_label_size )
       SCREAMING_SNAKE_CASE__   =   model(_lowerCamelCase     ,       token_type_ids=_lowerCamelCase     ,       labels=_lowerCamelCase )
       self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels) )







  def 				lowercase_		(							self						:		Tuple )		->							int:
       SCREAMING_SNAKE_CASE__   =   self.prepare_config_and_inputs()
       (
           SCREAMING_SNAKE_CASE__    
       )   =   config_and_inputs
       SCREAMING_SNAKE_CASE__   =   {
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''head_mask''': head_mask,
       }

       return config, inputs_dict






@require_torch
class 	UpperCAmelCase__		(	a__						,					a__						,					a__						,					unittest.TestCase	):






  """simple docstring"""


  a									= (
      (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
      if is_torch_available()
      else ()
  )
  a									= (
      (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
  )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
  a									= (
      {
          'feature-extraction': OpenAIGPTModel,
          'text-classification': OpenAIGPTForSequenceClassification,
          'text-generation': OpenAIGPTLMHeadModel,
          'zero-shot': OpenAIGPTForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )






  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		Optional[Any] )		->							Optional[Any]:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
            # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
            # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
            # tiny config could not be created.
            return True

       return False






  def 				lowercase_		(							self						:		List[str]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		Optional[Any]     ,       __lowerCamelCase						:		List[str]=False )		->							int:
       SCREAMING_SNAKE_CASE__   =   super()._prepare_for_class(_lowerCamelCase     ,       _lowerCamelCase     ,       return_labels=_lowerCamelCase )

       if return_labels:
            if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                 SCREAMING_SNAKE_CASE__   =   torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)     ,       dtype=torch.long     ,       device=_lowerCamelCase     ,       )
                 SCREAMING_SNAKE_CASE__   =   inputs_dict['''labels''']
                 SCREAMING_SNAKE_CASE__   =   inputs_dict['''labels''']
                 SCREAMING_SNAKE_CASE__   =   torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.num_choices)     ,       dtype=torch.long     ,       device=_lowerCamelCase     ,       )
                 SCREAMING_SNAKE_CASE__   =   torch.zeros(
                     self.model_tester.batch_size     ,       dtype=torch.long     ,       device=_lowerCamelCase )
       return inputs_dict






  def 				lowercase_		(							self						:		Tuple )		->							str:
       SCREAMING_SNAKE_CASE__   =   OpenAIGPTModelTester(self )
       SCREAMING_SNAKE_CASE__   =   ConfigTester(self     ,       config_class=_lowerCamelCase     ,       n_embd=37 )






  def 				lowercase_		(							self						:		Union[str, Any] )		->							Union[str, Any]:
       self.config_tester.run_common_tests()






  def 				lowercase_		(							self						:		Dict )		->							Any:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*_lowerCamelCase )






  def 				lowercase_		(							self						:		Any )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head_model(*_lowerCamelCase )






  def 				lowercase_		(							self						:		int )		->							Any:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_double_lm_head_model(*_lowerCamelCase )






  def 				lowercase_		(							self						:		str )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowerCamelCase )







  @slow
  def 				lowercase_		(							self						:		Union[str, Any] )		->							Optional[int]:
       for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__   =   OpenAIGPTModel.from_pretrained(_lowerCamelCase )
            self.assertIsNotNone(_lowerCamelCase )






@require_torch
class 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  @slow
  def 				lowercase_		(							self						:		Union[str, Any] )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt''' )
       model.to(_lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   torch.tensor([[481, 4735, 544]]     ,       dtype=torch.long     ,       device=_lowerCamelCase )  # the president is
       SCREAMING_SNAKE_CASE__   =   [
           481,
           4735,
           544,
           246,
           963,
           870,
           762,
           239,
           244,
           4_0477,
           244,
           249,
           719,
           881,
           487,
           544,
           240,
           244,
           603,
           481,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       SCREAMING_SNAKE_CASE__   =   model.generate(_lowerCamelCase     ,       do_sample=_lowerCamelCase )
       self.assertListEqual(output_ids[0].tolist()     ,       _lowerCamelCase )





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


_SCREAMING_SNAKE_CASE					:  Dict             =      '''base_with_context'''
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




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

          SCREAMING_SNAKE_CASE__   =   ly_weight['''attention''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )
     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )				,		requires_grad=_A    )

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

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )

     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T    )    )
     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )				,		requires_grad=_A    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T    )    )

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

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   ly_weight['''self_attention''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   ly_weight['''MultiHeadDotProductAttention_0''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T    )    )

     return model
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   checkpoints.load_tax_checkpoint(args.checkpoint_path    )
     SCREAMING_SNAKE_CASE__   =   jnp.tree_util.tree_map(onp.array				,		_A    )

     SCREAMING_SNAKE_CASE__   =   [
         '''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()''',
     ]

     SCREAMING_SNAKE_CASE__   =   os.path.join(args.checkpoint_path				,		'''..'''				,		'''config.gin'''    )
     SCREAMING_SNAKE_CASE__   =   inference.parse_training_gin_file(_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   inference.InferenceModel(args.checkpoint_path				,		_A    )

     SCREAMING_SNAKE_CASE__   =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''				,		variance_type='''fixed_large'''    )

     SCREAMING_SNAKE_CASE__   =   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'''				,		)

     SCREAMING_SNAKE_CASE__   =   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'''				,		)

     SCREAMING_SNAKE_CASE__   =   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				,		)

     SCREAMING_SNAKE_CASE__   =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_decoder(ta_checkpoint['''target''']['''decoder''']				,		_A    )

     SCREAMING_SNAKE_CASE__   =   OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''    )

     SCREAMING_SNAKE_CASE__   =   SpectrogramDiffusionPipeline(
         notes_encoder=_A				,		continuous_encoder=_A				,		decoder=_A				,		scheduler=_A				,		melgan=_A				,		)
     if args.save:
          pipe.save_pretrained(args.output_path    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Dict             =      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.''',
       )
       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()

       main(args)


