





from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel


@require_tf
class        __SCREAMING_SNAKE_CASE      :
       _UpperCAmelCase				:  List[Any]			   =   BlenderbotSmallConfig
       _UpperCAmelCase				:  Union[str, Any]			   =   {}
       _UpperCAmelCase				:  Tuple			   =   "gelu"







       def __init__(				self					:     List[str]		, A					:     List[str]		, A					:     Optional[Any]=1_3		, A					:     Any=7		, A					:     Optional[Any]=True		, A					:     List[Any]=False		, A					:     Tuple=9_9		, A					:     List[str]=3_2		, A					:     Tuple=2		, A					:     Tuple=4		, A					:     int=3_7		, A					:     Dict=0.1		, A					:     Optional[Any]=0.1		, A					:     str=2_0		, A					:     Optional[int]=2		, A					:     int=1		, A					:     Tuple=0		, )       ->str:
          lowerCamelCase__       :						Dict        =							parent
          lowerCamelCase__       :						Union[str, Any]        =							batch_size
          lowerCamelCase__       :						Dict        =							seq_length
          lowerCamelCase__       :						int        =							is_training
          lowerCamelCase__       :						Optional[Any]        =							use_labels
          lowerCamelCase__       :						List[str]        =							vocab_size
          lowerCamelCase__       :						int        =							hidden_size
          lowerCamelCase__       :						Union[str, Any]        =							num_hidden_layers
          lowerCamelCase__       :						Tuple        =							num_attention_heads
          lowerCamelCase__       :						str        =							intermediate_size

          lowerCamelCase__       :						Dict        =							hidden_dropout_prob
          lowerCamelCase__       :						List[str]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						int        =							max_position_embeddings
          lowerCamelCase__       :						List[Any]        =							eos_token_id
          lowerCamelCase__       :						Any        =							pad_token_id
          lowerCamelCase__       :						Dict        =							bos_token_id







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						Union[str, Any]        =							ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size      )
          lowerCamelCase__       :						Optional[int]        =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )		, 1      )
          lowerCamelCase__       :						List[str]        =							tf.concat([input_ids, eos_tensor]		, axis=1      )

          lowerCamelCase__       :						Optional[int]        =							ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size      )

          lowerCamelCase__       :						int        =							self.config_cls(
              vocab_size=self.vocab_size		, d_model=self.hidden_size		, encoder_layers=self.num_hidden_layers		, decoder_layers=self.num_hidden_layers		, encoder_attention_heads=self.num_attention_heads		, decoder_attention_heads=self.num_attention_heads		, encoder_ffn_dim=self.intermediate_size		, decoder_ffn_dim=self.intermediate_size		, dropout=self.hidden_dropout_prob		, attention_dropout=self.attention_probs_dropout_prob		, max_position_embeddings=self.max_position_embeddings		, eos_token_ids=[2]		, bos_token_id=self.bos_token_id		, pad_token_id=self.pad_token_id		, decoder_start_token_id=self.pad_token_id		, **self.config_updates		, )
          lowerCamelCase__       :						Any        =							prepare_blenderbot_small_inputs_dict(A		, A		, A      )
          return config, inputs_dict







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Union[str, Any]		, A					:     List[Any]      )       ->str:
          lowerCamelCase__       :						int        =							TFBlenderbotSmallModel(config=A      ).get_decoder()
          lowerCamelCase__       :						Dict        =							inputs_dict['''input_ids''']

          lowerCamelCase__       :						int        =							input_ids[:1, :]
          lowerCamelCase__       :						Union[str, Any]        =							inputs_dict['''attention_mask'''][:1, :]
          lowerCamelCase__       :						List[Any]        =							inputs_dict['''head_mask''']
          lowerCamelCase__       :						Optional[int]        =							1

          # first forward pass
          lowerCamelCase__       :						Tuple        =							model(A		, attention_mask=A		, head_mask=A		, use_cache=A      )

          lowerCamelCase__       :						Optional[Any]        =							outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          lowerCamelCase__       :						Optional[int]        =							ids_tensor((self.batch_size, 3)		, config.vocab_size      )
          lowerCamelCase__       :						Tuple        =							tf.cast(ids_tensor((self.batch_size, 3)		, 2      )		, tf.inta      )

          # append to next input_ids and
          lowerCamelCase__       :						Any        =							tf.concat([input_ids, next_tokens]		, axis=-1      )
          lowerCamelCase__       :						Optional[int]        =							tf.concat([attention_mask, next_attn_mask]		, axis=-1      )

          lowerCamelCase__       :						List[Any]        =							model(A		, attention_mask=A      )[0]
          lowerCamelCase__       :						Optional[Any]        =							model(A		, attention_mask=A		, past_key_values=A      )[0]

          self.parent.assertEqual(next_tokens.shape[1]		, output_from_past.shape[1]      )

          # select random slice
          lowerCamelCase__       :						List[str]        =							int(ids_tensor((1,)		, output_from_past.shape[-1]      )      )
          lowerCamelCase__       :						str        =							output_from_no_past[:, -3:, random_slice_idx]
          lowerCamelCase__       :						Optional[int]        =							output_from_past[:, :, random_slice_idx]

          # test that outputs are equal for slice
          tf.debugging.assert_near(A		, A		, rtol=1e-3      )



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  UpperCAmelCase=None      ,  )    ->    int:







   """simple docstring"""
   if attention_mask is None:
      lowerCamelCase__       :						List[str]        =							tf.cast(tf.math.not_equal(UpperCAmelCase      ,  config.pad_token_id					)      ,  tf.inta					)
   if decoder_attention_mask is None:
      lowerCamelCase__       :						List[Any]        =							tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape      ,  dtype=tf.inta					),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]      ,  config.pad_token_id					)      ,  tf.inta					),
          ]      ,  axis=-1      ,  )
   if head_mask is None:
      lowerCamelCase__       :						Any        =							tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      lowerCamelCase__       :						Optional[Any]        =							tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      lowerCamelCase__       :						Any        =							tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


@require_tf
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Tuple			   =   (
           (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
       )
       _UpperCAmelCase				:  List[str]			   =   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
       _UpperCAmelCase				:  int			   =   (
           {
               "conversational": TFBlenderbotSmallForConditionalGeneration,
               "feature-extraction": TFBlenderbotSmallModel,
               "summarization": TFBlenderbotSmallForConditionalGeneration,
               "text2text-generation": TFBlenderbotSmallForConditionalGeneration,
               "translation": TFBlenderbotSmallForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       _UpperCAmelCase				:  Optional[int]			   =   True
       _UpperCAmelCase				:  Tuple			   =   False
       _UpperCAmelCase				:  int			   =   False







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[int]:
          lowerCamelCase__       :						Any        =							TFBlenderbotSmallModelTester(self      )
          lowerCamelCase__       :						Any        =							ConfigTester(self		, config_class=A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Any:
          self.config_tester.run_common_tests()







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[Any]:
          lowerCamelCase__       :						Any        =							self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*A      )


@require_tokenizers
@require_tf
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):
       _UpperCAmelCase				:  Dict			   =   [
           "Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like "
           "  i'm going to throw up.\nand why is that?"
       ]
       _UpperCAmelCase				:  str			   =   "facebook/blenderbot_small-90M"







       @cached_property
       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
          # use "old" tokenizer here because of bug when downloading new tokenizer
          return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''      )







       @cached_property
       def 						__lowerCamelCase   (				self					:     str      )       ->Union[str, Any]:
          lowerCamelCase__       :						Dict        =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
          return model







       @slow
       def 						__lowerCamelCase   (				self					:     str      )       ->Optional[Any]:
          lowerCamelCase__       :						Any        =							self.tokenizer(self.src_text		, return_tensors='''tf'''      )
          lowerCamelCase__       :						List[str]        =							self.model.generate(
              model_inputs.input_ids		, attention_mask=model_inputs.attention_mask		, num_beams=2		, use_cache=A		, )
          lowerCamelCase__       :						List[str]        =							self.tokenizer.batch_decode(generated_ids.numpy()		, skip_special_tokens=A      )[0]
          assert generated_words in (
              "i don't know. i just feel like i'm going to throw up. it's not fun.",
              "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
              "i'm not sure. i just feel like i've been in a bad situation.",
          )









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


_A :   List[Any]  			=  'base_with_context'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







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

      lowerCamelCase__       :						int        =							ly_weight['''attention''']
      lowerCamelCase__       :						List[str]        =							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__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						List[Any]        =							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__       :						Any        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







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

   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

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

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

      lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							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__       :						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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
   lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

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

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

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

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

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

      lowerCamelCase__       :						Dict        =							ly_weight['''MultiHeadDotProductAttention_0''']
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[str]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						str        =							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__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							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__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']					)					)

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

   return model



def 	_a						(   UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							checkpoints.load_tax_checkpoint(args.checkpoint_path					)
   lowerCamelCase__       :						Optional[int]        =							jnp.tree_util.tree_map(onp.array      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[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__       :						List[Any]        =							os.path.join(args.checkpoint_path      ,  '''..'''      ,  '''config.gin'''					)
   lowerCamelCase__       :						Optional[Any]        =							inference.parse_training_gin_file(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							inference.InferenceModel(args.checkpoint_path      ,  UpperCAmelCase					)

   lowerCamelCase__       :						int        =							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''      ,  variance_type='''fixed_large'''					)

   lowerCamelCase__       :						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__       :						int        =							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__       :						Optional[int]        =							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__       :						Optional[int]        =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						int        =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							load_decoder(ta_checkpoint['''target''']['''decoder''']      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''					)

   lowerCamelCase__       :						List[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__":
       _A :   int  			=  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.',
       )
       _A :   Tuple  			=  parser.parse_args()

       main(args)



