






"""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


_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						'''base_with_context'''







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		str )      ->				Dict:
  lowerCamelCase_         =						nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
  lowerCamelCase_         =						nn.Parameter(
      torch.FloatTensor(weights['Embed_0']['embedding'] )		,				requires_grad=_lowerCamelCase )
  for lyr_num, lyr in enumerate(model.encoders ):
    lowerCamelCase_         =						weights[F'''layers_{lyr_num}''']
    lowerCamelCase_         =						nn.Parameter(
        torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

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

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

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

  lowerCamelCase_         =						nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
  return model







def      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		str )      ->				Optional[int]:
  lowerCamelCase_         =						nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )

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

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

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

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

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

  return model







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		Dict )      ->				Optional[int]:
  lowerCamelCase_         =						nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
  lowerCamelCase_         =						nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )

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

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

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

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

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

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

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

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

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

  return model







def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any] )      ->				Union[str, Any]:
  lowerCamelCase_         =						checkpoints.load_tax_checkpoint(args.checkpoint_path )
  lowerCamelCase_         =						jnp.tree_util.tree_map(onp.array		,				_lowerCamelCase )

  lowerCamelCase_         =						[
      '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_         =						os.path.join(args.checkpoint_path		,				'..'		,				'config.gin' )
  lowerCamelCase_         =						inference.parse_training_gin_file(_lowerCamelCase		,				_lowerCamelCase )
  lowerCamelCase_         =						inference.InferenceModel(args.checkpoint_path		,				_lowerCamelCase )

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

  lowerCamelCase_         =						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_         =						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_         =						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_         =						load_notes_encoder(ta_checkpoint['target']['token_encoder']		,				_lowerCamelCase )
  lowerCamelCase_         =						load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']		,				_lowerCamelCase )
  lowerCamelCase_         =						load_decoder(ta_checkpoint['target']['decoder']		,				_lowerCamelCase )

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

  lowerCamelCase_         =						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__":
  _SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						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					:				Union[str, Any]					    =						parser.parse_args()

  main(args)












"""simple docstring"""





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_SCREAMING_SNAKE_CASE					:				Optional[int]					    =						'''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''






class      a						(					unittest.TestCase  ,      __snake_case		):






    def    UpperCamelCase       (       self     :							int					)     ->		Optional[int]:
      lowerCamelCase_         =						load_tool('text-question-answering'					)
      self.tool.setup()
      lowerCamelCase_         =						load_tool('text-question-answering'       ,						remote=__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Dict					)     ->		Tuple:
      lowerCamelCase_         =						self.tool(__SCREAMING_SNAKE_CASE       ,						'What did Hugging Face do in April 2021?'					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'launched the BigScience Research Workshop'					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Optional[int]:
      lowerCamelCase_         =						self.remote_tool(__SCREAMING_SNAKE_CASE       ,						'What did Hugging Face do in April 2021?'					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'launched the BigScience Research Workshop'					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Optional[int]:
      lowerCamelCase_         =						self.tool(text=__SCREAMING_SNAKE_CASE       ,						question='What did Hugging Face do in April 2021?'					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'launched the BigScience Research Workshop'					)






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		int:
      lowerCamelCase_         =						self.remote_tool(text=__SCREAMING_SNAKE_CASE       ,						question='What did Hugging Face do in April 2021?'					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'launched the BigScience Research Workshop'					)




