




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )
    from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
    import torch





class 		A__    :






 def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase=14    ,					lowerCamelCase=7    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=False    ,					lowerCamelCase=True    ,					lowerCamelCase=99    ,					lowerCamelCase=32    ,					lowerCamelCase=5    ,					lowerCamelCase=4    ,					lowerCamelCase=37    ,					lowerCamelCase="gelu"    ,					lowerCamelCase=0.1    ,					lowerCamelCase=0.1    ,					lowerCamelCase=512    ,					lowerCamelCase=0.0_2    ,					)				->       List[Any]:




      """simple docstring"""






      __magic_name__ :							int		       =							parent
      __magic_name__ :							Optional[int]		       =							batch_size
      __magic_name__ :							Optional[int]		       =							seq_length
      __magic_name__ :							Tuple		       =							is_training
      __magic_name__ :							Any		       =							use_input_mask
      __magic_name__ :							Any		       =							use_token_type_ids
      __magic_name__ :							int		       =							use_labels
      __magic_name__ :							Dict		       =							vocab_size
      __magic_name__ :							Union[str, Any]		       =							hidden_size
      __magic_name__ :							List[Any]		       =							num_hidden_layers
      __magic_name__ :							Dict		       =							num_attention_heads
      __magic_name__ :							Optional[Any]		       =							intermediate_size
      __magic_name__ :							int		       =							hidden_act
      __magic_name__ :							List[Any]		       =							hidden_dropout_prob
      __magic_name__ :							List[str]		       =							attention_probs_dropout_prob
      __magic_name__ :							str		       =							max_position_embeddings
      __magic_name__ :							Optional[int]		       =							initializer_range
      __magic_name__ :							Union[str, Any]		       =							None
      __magic_name__ :							List[Any]		       =							vocab_size - 1
      __magic_name__ :							Union[str, Any]		       =							vocab_size - 1
      __magic_name__ :							str		       =							vocab_size - 1






 def 			lowercase				(     self						)				->       List[Any]:




      """simple docstring"""






      __magic_name__ :							List[Any]		       =							ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size						)

      __magic_name__ :							List[Any]		       =							None
      if self.use_input_mask:
           __magic_name__ :							int		       =							random_attention_mask([self.batch_size, self.seq_length]						)

      __magic_name__ :							Union[str, Any]		       =							GPTaConfig(
          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    ,					use_cache=_UpperCAmelCase    ,					bos_token_id=self.bos_token_id    ,					eos_token_id=self.eos_token_id    ,					pad_token_id=self.pad_token_id    ,					)

      return (config, input_ids, input_mask)






 def 			lowercase				(     self						)				->       List[str]:




      """simple docstring"""






      __magic_name__ :							str		       =							self.prepare_config_and_inputs()
      __magic_name__ :							List[str]		       =							config_and_inputs
      __magic_name__ :							Optional[Any]		       =							{"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict






 def 			lowercase				(     self						)				->       Dict:




      """simple docstring"""






      __magic_name__ :							Dict		       =							self.prepare_config_and_inputs()

      __magic_name__ :							int		       =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
      __magic_name__ :							Any		       =							ids_tensor([self.batch_size, self.seq_length]    ,					vocab_size=2						)

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )






 def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Optional[int]:




      """simple docstring"""






      __magic_name__ :							int		       =							20
      __magic_name__ :							int		       =							model_class_name(_UpperCAmelCase						)

      __magic_name__ :							Any		       =							model.init_cache(input_ids.shape[0]    ,					_UpperCAmelCase						)
      __magic_name__ :							Optional[Any]		       =							jnp.ones((input_ids.shape[0], max_decoder_length)    ,					dtype='''i4'''						)

      __magic_name__ :							List[Any]		       =							jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1						)[None, :]    ,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)
      __magic_name__ :							Tuple		       =							model(
          input_ids[:, :-1]    ,					attention_mask=_UpperCAmelCase    ,					past_key_values=_UpperCAmelCase    ,					position_ids=_UpperCAmelCase    ,					)

      __magic_name__ :							Tuple		       =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,					dtype='''i4'''						)
      __magic_name__ :							Optional[int]		       =							model(
          input_ids[:, -1:]    ,					attention_mask=_UpperCAmelCase    ,					past_key_values=outputs_cache.past_key_values    ,					position_ids=_UpperCAmelCase    ,					)

      __magic_name__ :							Optional[int]		       =							model(_UpperCAmelCase						)

      __magic_name__ :							Optional[Any]		       =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
      self.parent.assertTrue(diff < 1e-3    ,					msg=F'''Max diff is {diff}'''						)






 def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       int:




      """simple docstring"""






      __magic_name__ :							List[str]		       =							20
      __magic_name__ :							Any		       =							model_class_name(_UpperCAmelCase						)

      __magic_name__ :							Any		       =							jnp.concatenate(
          [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]    ,					axis=-1    ,					)

      __magic_name__ :							List[Any]		       =							model.init_cache(input_ids.shape[0]    ,					_UpperCAmelCase						)
      __magic_name__ :							List[Any]		       =							jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1						)[None, :]    ,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)

      __magic_name__ :							Any		       =							model(
          input_ids[:, :-1]    ,					attention_mask=_UpperCAmelCase    ,					past_key_values=_UpperCAmelCase    ,					position_ids=_UpperCAmelCase    ,					)
      __magic_name__ :							List[str]		       =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,					dtype='''i4'''						)
      __magic_name__ :							List[str]		       =							model(
          input_ids[:, -1:]    ,					past_key_values=outputs_cache.past_key_values    ,					attention_mask=_UpperCAmelCase    ,					position_ids=_UpperCAmelCase    ,					)

      __magic_name__ :							Optional[int]		       =							model(_UpperCAmelCase    ,					attention_mask=_UpperCAmelCase						)

      __magic_name__ :							Dict		       =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
      self.parent.assertTrue(diff < 1e-3    ,					msg=F'''Max diff is {diff}'''						)





@require_flax
class 		A__    (       SCREAMING_SNAKE_CASE__		,					SCREAMING_SNAKE_CASE__		,					unittest.TestCase			):
 lowerCamelCase__		:			Union[str, Any]       =(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
 lowerCamelCase__		:			Any       =(FlaxGPTaLMHeadModel,) if is_flax_available() else ()






 def 			lowercase				(     self						)				->       List[str]:




      """simple docstring"""






      __magic_name__ :							Optional[int]		       =							FlaxGPTaModelTester(self						)






 def 			lowercase				(     self						)				->       Dict:




      """simple docstring"""






      for model_class_name in self.all_model_classes:
           __magic_name__ :							Tuple		       =							self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase						)






 def 			lowercase				(     self						)				->       List[Any]:




      """simple docstring"""






      for model_class_name in self.all_model_classes:
           __magic_name__ :							Tuple		       =							self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               _UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase    ,					_UpperCAmelCase						)






 @slow
 def 			lowercase				(     self						)				->       int:




      """simple docstring"""






      __magic_name__ :							Optional[Any]		       =							GPTaTokenizer.from_pretrained('''gpt2'''    ,					pad_token='''</s>'''    ,					padding_side='''left'''						)
      __magic_name__ :							List[str]		       =							tokenizer(['''Hello this is a long string''', '''Hey''']    ,					return_tensors='''np'''    ,					padding=_UpperCAmelCase    ,					truncation=_UpperCAmelCase						)

      __magic_name__ :							Dict		       =							FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''						)
      __magic_name__ :							List[Any]		       =							False
      __magic_name__ :							Optional[int]		       =							model.config.eos_token_id

      __magic_name__ :							List[Any]		       =							jax.jit(model.generate						)

      __magic_name__ :							Any		       =							jit_generate(inputs['''input_ids''']    ,					attention_mask=inputs['''attention_mask''']						).sequences

      __magic_name__ :							Tuple		       =							tokenizer.batch_decode(_UpperCAmelCase    ,					skip_special_tokens=_UpperCAmelCase						)

      __magic_name__ :							Dict		       =							[
          "Hello this is a long string of words. I'm going to start with the first one.\n",
          "Hey, I'm not sure if I'm going to be able to do",
      ]

      self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase						)






 @is_pt_flax_cross_test
 def 			lowercase				(     self						)				->       Tuple:




      """simple docstring"""






      __magic_name__ :							int		       =							self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
                # prepare inputs
                __magic_name__ :							List[Any]		       =							self._prepare_for_class(_UpperCAmelCase    ,					_UpperCAmelCase						)
                __magic_name__ :							Tuple		       =							{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                __magic_name__ :							Optional[Any]		       =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
                __magic_name__ :							Tuple		       =							getattr(_UpperCAmelCase    ,					_UpperCAmelCase						)

                __magic_name__ :							Dict		       =							pt_inputs["input_ids"].shape
                __magic_name__ :							List[Any]		       =							np.random.randint(0    ,					seq_length - 1    ,					size=(batch_size,)						)
                for batch_idx, start_index in enumerate(_UpperCAmelCase						):
                     __magic_name__ :							List[Any]		       =							0
                     __magic_name__ :							Optional[int]		       =							1
                     __magic_name__ :							Tuple		       =							0
                     __magic_name__ :							Any		       =							1
                __magic_name__ :							List[str]		       =							pt_model_class(_UpperCAmelCase						).eval()
                __magic_name__ :							Optional[Any]		       =							model_class(_UpperCAmelCase    ,					dtype=jnp.floataa						)

                __magic_name__ :							List[str]		       =							convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,					_UpperCAmelCase						)
                __magic_name__ :							int		       =							fx_state

                with torch.no_grad():
                     __magic_name__ :							Any		       =							pt_model(**_UpperCAmelCase						).to_tuple()

                __magic_name__ :							str		       =							fx_model(**_UpperCAmelCase						).to_tuple()
                self.assertEqual(len(_UpperCAmelCase						)    ,					len(_UpperCAmelCase						)    ,					'''Output lengths differ between Flax and PyTorch'''						)
                for fx_output, pt_output in zip(_UpperCAmelCase    ,					_UpperCAmelCase						):
                     self.assert_almost_equals(fx_output[:, -1]    ,					pt_output[:, -1].numpy()    ,					4e-2						)

                with tempfile.TemporaryDirectory() as tmpdirname:
                     pt_model.save_pretrained(_UpperCAmelCase						)
                     __magic_name__ :							str		       =							model_class.from_pretrained(_UpperCAmelCase    ,					from_pt=_UpperCAmelCase						)

                __magic_name__ :							Any		       =							fx_model_loaded(**_UpperCAmelCase						).to_tuple()
                self.assertEqual(
                    len(_UpperCAmelCase						)    ,					len(_UpperCAmelCase						)    ,					'''Output lengths differ between Flax and PyTorch'''						)
                for fx_output_loaded, pt_output in zip(_UpperCAmelCase    ,					_UpperCAmelCase						):
                     self.assert_almost_equals(fx_output_loaded[:, -1]    ,					pt_output[:, -1].numpy()    ,					4e-2						)






 @is_pt_flax_cross_test
 def 			lowercase				(     self						)				->       int:




      """simple docstring"""






      __magic_name__ :							Dict		       =							self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
                # prepare inputs
                __magic_name__ :							Optional[Any]		       =							self._prepare_for_class(_UpperCAmelCase    ,					_UpperCAmelCase						)
                __magic_name__ :							List[str]		       =							{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                __magic_name__ :							List[Any]		       =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
                __magic_name__ :							Optional[Any]		       =							getattr(_UpperCAmelCase    ,					_UpperCAmelCase						)

                __magic_name__ :							Any		       =							pt_model_class(_UpperCAmelCase						).eval()
                __magic_name__ :							Optional[int]		       =							model_class(_UpperCAmelCase    ,					dtype=jnp.floataa						)

                __magic_name__ :							List[str]		       =							load_flax_weights_in_pytorch_model(_UpperCAmelCase    ,					fx_model.params						)
                __magic_name__ :							str		       =							pt_inputs["input_ids"].shape
                __magic_name__ :							int		       =							np.random.randint(0    ,					seq_length - 1    ,					size=(batch_size,)						)
                for batch_idx, start_index in enumerate(_UpperCAmelCase						):
                     __magic_name__ :							Union[str, Any]		       =							0
                     __magic_name__ :							Tuple		       =							1
                     __magic_name__ :							int		       =							0
                     __magic_name__ :							Optional[int]		       =							1

                # make sure weights are tied in PyTorch
                pt_model.tie_weights()

                with torch.no_grad():
                     __magic_name__ :							str		       =							pt_model(**_UpperCAmelCase						).to_tuple()

                __magic_name__ :							Dict		       =							fx_model(**_UpperCAmelCase						).to_tuple()
                self.assertEqual(len(_UpperCAmelCase						)    ,					len(_UpperCAmelCase						)    ,					'''Output lengths differ between Flax and PyTorch'''						)
                for fx_output, pt_output in zip(_UpperCAmelCase    ,					_UpperCAmelCase						):
                     self.assert_almost_equals(fx_output[:, -1]    ,					pt_output[:, -1].numpy()    ,					4e-2						)

                with tempfile.TemporaryDirectory() as tmpdirname:
                     fx_model.save_pretrained(_UpperCAmelCase						)
                     __magic_name__ :							str		       =							pt_model_class.from_pretrained(_UpperCAmelCase    ,					from_flax=_UpperCAmelCase						)

                with torch.no_grad():
                     __magic_name__ :							Any		       =							pt_model_loaded(**_UpperCAmelCase						).to_tuple()

                self.assertEqual(
                    len(_UpperCAmelCase						)    ,					len(_UpperCAmelCase						)    ,					'''Output lengths differ between Flax and PyTorch'''						)
                for fx_output, pt_output in zip(_UpperCAmelCase    ,					_UpperCAmelCase						):
                     self.assert_almost_equals(fx_output[:, -1]    ,					pt_output[:, -1].numpy()    ,					4e-2						)






 @slow
 def 			lowercase				(     self						)				->       List[str]:




      """simple docstring"""






      for model_class_name in self.all_model_classes:
           __magic_name__ :							Dict		       =							model_class_name.from_pretrained('''gpt2'''    ,					from_pt=_UpperCAmelCase						)
           __magic_name__ :							Union[str, Any]		       =							model(np.ones((1, 1)						)						)
           self.assertIsNotNone(_UpperCAmelCase						)









import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


lowercase_		=							'''./tests/fixtures/elise_format0.mid'''





@unittest.skip("The note-seq package currently throws an error on import"			)
class 		A__    (       __SCREAMING_SNAKE_CASE		,					unittest.TestCase			):
	lowerCamelCase__		:			Optional[int]       =SpectrogramDiffusionPipeline
	lowerCamelCase__		:			List[str]       =PipelineTesterMixin.required_optional_params - {
	    "callback",
	    "latents",
	    "callback_steps",
	    "output_type",
	    "num_images_per_prompt",
	}
	lowerCamelCase__		:			Union[str, Any]       =False
	lowerCamelCase__		:			List[Any]       =False
	lowerCamelCase__		:			List[str]       =TOKENS_TO_AUDIO_GENERATION_PARAMS
	lowerCamelCase__		:			str       =TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						torch.manual_seed(0						)
						__magic_name__ :							Optional[Any]		       =							SpectrogramNotesEncoder(
						    max_length=2048    ,					vocab_size=1536    ,					d_model=768    ,					dropout_rate=0.1    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					feed_forward_proj='''gated-gelu'''    ,					)

						__magic_name__ :							Any		       =							SpectrogramContEncoder(
						    input_dims=128    ,					targets_context_length=256    ,					d_model=768    ,					dropout_rate=0.1    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					feed_forward_proj='''gated-gelu'''    ,					)

						__magic_name__ :							List[Any]		       =							TaFilmDecoder(
						    input_dims=128    ,					targets_length=256    ,					max_decoder_noise_time=2_0_0_0_0.0    ,					d_model=768    ,					num_layers=1    ,					num_heads=1    ,					d_kv=4    ,					d_ff=2048    ,					dropout_rate=0.1    ,					)

						__magic_name__ :							Any		       =							DDPMScheduler()

						__magic_name__ :							Dict		       =							{
						    '''notes_encoder''': notes_encoder.eval(),
						    '''continuous_encoder''': continuous_encoder.eval(),
						    '''decoder''': decoder.eval(),
						    '''scheduler''': scheduler,
						    '''melgan''': None,
						}
						return components






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=0						)				->       Dict:




						"""simple docstring"""






						if str(lowerCamelCase						).startswith('''mps'''						):
											__magic_name__ :							List[str]		       =							torch.manual_seed(lowerCamelCase						)
						else:
											__magic_name__ :							Any		       =							torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
						__magic_name__ :							Any		       =							{
						    '''input_tokens''': [
						        [1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
						    ],
						    '''generator''': generator,
						    '''num_inference_steps''': 4,
						    '''output_type''': '''mel''',
						}
						return inputs






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Optional[Any]		       =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						__magic_name__ :							Union[str, Any]		       =							self.get_dummy_components()
						__magic_name__ :							str		       =							SpectrogramDiffusionPipeline(**lowerCamelCase						)
						__magic_name__ :							List[Any]		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__ :							Tuple		       =							self.get_dummy_inputs(lowerCamelCase						)
						__magic_name__ :							List[str]		       =							pipe(**lowerCamelCase						)
						__magic_name__ :							Any		       =							output.audios

						__magic_name__ :							Union[str, Any]		       =							mel[0, -3:, -3:]

						assert mel_slice.shape == (3, 3)
						__magic_name__ :							Dict		       =							np.array(
						    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0]						)
						assert np.abs(mel_slice.flatten() - expected_slice						).max() < 1e-2






	@skip_mps
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return super().test_save_load_local()






	@skip_mps
	def 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						return super().test_save_load_optional_components()






	@skip_mps
	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						return super().test_attention_slicing_forward_pass()






	def 			lowercase				(     self						)				->       Tuple:




						"""simple docstring"""






						pass






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						pass






	@skip_mps
	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						return super().test_progress_bar()





@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class 		A__    (       unittest.TestCase			):






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							torch_device

						__magic_name__ :							Any		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							Optional[int]		       =							pipe.melgan
						__magic_name__ :							List[str]		       =							None

						__magic_name__ :							Tuple		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						def callback(lowerCamelCase    ,					lowerCamelCase						):
											# decode mel to audio
											__magic_name__ :							Tuple		       =							melgan(input_features=mel_output.astype(np.floataa						)						)[0]
											assert len(audio[0]						) == 81920 * (step + 1)
											# simulate that audio is played
											return audio

						__magic_name__ :							int		       =							MidiProcessor()
						__magic_name__ :							List[str]		       =							processor(lowerCamelCase						)

						__magic_name__ :							Dict		       =							input_tokens[:3]
						__magic_name__ :							Any		       =							torch.manual_seed(0						)
						pipe(lowerCamelCase    ,					num_inference_steps=5    ,					generator=lowerCamelCase    ,					callback=lowerCamelCase    ,					output_type='''mel'''						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							torch_device

						__magic_name__ :							int		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							str		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)
						__magic_name__ :							Any		       =							MidiProcessor()

						__magic_name__ :							Optional[Any]		       =							processor(lowerCamelCase						)
						# just run two denoising loops
						__magic_name__ :							Any		       =							input_tokens[:2]

						__magic_name__ :							int		       =							torch.manual_seed(0						)
						__magic_name__ :							Any		       =							pipe(lowerCamelCase    ,					num_inference_steps=2    ,					generator=lowerCamelCase						)

						__magic_name__ :							Union[str, Any]		       =							output.audios[0]

						assert abs(np.abs(lowerCamelCase						).sum() - 3_6_1_2.8_4_1						) < 1e-1






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							torch_device

						__magic_name__ :							str		       =							SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''						)
						__magic_name__ :							List[Any]		       =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							MidiProcessor()

						__magic_name__ :							str		       =							processor(lowerCamelCase						)

						# just run 4 denoising loops
						__magic_name__ :							List[str]		       =							input_tokens[:4]

						__magic_name__ :							Union[str, Any]		       =							torch.manual_seed(0						)
						__magic_name__ :							Dict		       =							pipe(lowerCamelCase    ,					num_inference_steps=100    ,					generator=lowerCamelCase						)

						__magic_name__ :							Optional[int]		       =							output.audios[0]
						assert abs(np.abs(lowerCamelCase						).sum() - 9_3_8_9.1_1_1_1						) < 5e-2



