
'''simple docstring'''

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()


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


@unittest.skip('The note-seq package currently throws an error on import'  )
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= SpectrogramDiffusionPipeline
   A		= PipelineTesterMixin.required_optional_params - {
       'callback',
       'latents',
       'callback_steps',
       'output_type',
       'num_images_per_prompt',
   }
   A		= False
   A		= False
   A		= TOKENS_TO_AUDIO_GENERATION_PARAMS
   A		= TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






   def 					__a			(       self      ):
    torch.manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  int								=		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'    ,)

    SCREAMING_SNAKE_CASE  :  str								=		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'    ,)

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		TaFilmDecoder(
        input_dims=128    ,targets_length=256    ,max_decoder_noise_time=2_0000.0    ,d_model=768    ,num_layers=1    ,num_heads=1    ,d_kv=4    ,d_ff=2048    ,dropout_rate=0.1    ,)

    SCREAMING_SNAKE_CASE  :  Tuple								=		DDPMScheduler()

    SCREAMING_SNAKE_CASE  :  List[Any]								=		{
        'notes_encoder': notes_encoder.eval(),
        'continuous_encoder': continuous_encoder.eval(),
        'decoder': decoder.eval(),
        'scheduler': scheduler,
        'melgan': None,
    }
    return components






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=0      ):
    if str(__SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
     SCREAMING_SNAKE_CASE  :  List[Any]								=		torch.manual_seed(__SCREAMING_SNAKE_CASE      )
    else:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.Generator(device=__SCREAMING_SNAKE_CASE      ).manual_seed(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		{
        '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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		'cpu'  # ensure determinism for the device-dependent torch.Generator
    SCREAMING_SNAKE_CASE  :  Any								=		self.get_dummy_components()
    SCREAMING_SNAKE_CASE  :  Any								=		SpectrogramDiffusionPipeline(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		pipe.to(__SCREAMING_SNAKE_CASE      )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.get_dummy_inputs(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		pipe(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		output.audios

    SCREAMING_SNAKE_CASE  :  List[Any]								=		mel[0, -3:, -3:]

    assert mel_slice.shape == (3, 3)
    SCREAMING_SNAKE_CASE  :  Dict								=		np.array(
        [-11.51_2925, -4.78_8215, -0.4617_2905, -2.05_1715, -10.53_9147, -10.97_0963, -9.09_1634, 4.0, 4.0]      )
    assert np.abs(mel_slice.flatten() - expected_slice      ).max() < 1e-2






   @skip_mps
   def 					__a			(       self      ):
    return super().test_save_load_local()






   @skip_mps
   def 					__a			(       self      ):
    return super().test_dict_tuple_outputs_equivalent()






   @skip_mps
   def 					__a			(       self      ):
    return super().test_save_load_optional_components()






   @skip_mps
   def 					__a			(       self      ):
    return super().test_attention_slicing_forward_pass()






   def 					__a			(       self      ):
    pass






   def 					__a			(       self      ):
    pass






   @skip_mps
   def 					__a			(       self      ):
    return super().test_progress_bar()



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







   """simple docstring"""




   def 					__a			(       self      ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()






   def 					__a			(       self      ):
    # TODO - test that pipeline can decode tokens in a callback
    # so that music can be played live
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch_device

    SCREAMING_SNAKE_CASE  :  Dict								=		SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		pipe.melgan
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		None

    SCREAMING_SNAKE_CASE  :  Tuple								=		pipe.to(__SCREAMING_SNAKE_CASE      )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

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

    SCREAMING_SNAKE_CASE  :  List[Any]								=		MidiProcessor()
    SCREAMING_SNAKE_CASE  :  List[Any]								=		processor(__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		input_tokens[:3]
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch.manual_seed(0      )
    pipe(__SCREAMING_SNAKE_CASE    ,num_inference_steps=5    ,generator=__SCREAMING_SNAKE_CASE    ,callback=__SCREAMING_SNAKE_CASE    ,output_type='mel'      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		torch_device

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'      )
    SCREAMING_SNAKE_CASE  :  str								=		pipe.to(__SCREAMING_SNAKE_CASE      )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		MidiProcessor()

    SCREAMING_SNAKE_CASE  :  Any								=		processor(__SCREAMING_SNAKE_CASE      )
    # just run two denoising loops
    SCREAMING_SNAKE_CASE  :  int								=		input_tokens[:2]

    SCREAMING_SNAKE_CASE  :  Tuple								=		torch.manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Any								=		pipe(__SCREAMING_SNAKE_CASE    ,num_inference_steps=2    ,generator=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		output.audios[0]

    assert abs(np.abs(__SCREAMING_SNAKE_CASE      ).sum() - 3612.841      ) < 1e-1






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		torch_device

    SCREAMING_SNAKE_CASE  :  Tuple								=		SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion'      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		pipe.to(__SCREAMING_SNAKE_CASE      )
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		MidiProcessor()

    SCREAMING_SNAKE_CASE  :  Dict								=		processor(__SCREAMING_SNAKE_CASE      )

    # just run 4 denoising loops
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		input_tokens[:4]

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		torch.manual_seed(0      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		pipe(__SCREAMING_SNAKE_CASE    ,num_inference_steps=100    ,generator=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  int								=		output.audios[0]
    assert abs(np.abs(__SCREAMING_SNAKE_CASE      ).sum() - 9389.1111      ) < 5e-2




'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


