






"""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()


_lowerCAmelCase     :     List[Any]            =	"""./tests/fixtures/elise_format0.mid"""




@unittest.skip('''The note-seq package currently throws an error on import'''					)
class        lowerCAmelCase__       (  __magic_name__				,		unittest.TestCase					):
   SCREAMING_SNAKE_CASE_					      =SpectrogramDiffusionPipeline
   SCREAMING_SNAKE_CASE_					      =PipelineTesterMixin.required_optional_params - {
       '''callback''',
       '''latents''',
       '''callback_steps''',
       '''output_type''',
       '''num_images_per_prompt''',
   }
   SCREAMING_SNAKE_CASE_					      =False
   SCREAMING_SNAKE_CASE_					      =False
   SCREAMING_SNAKE_CASE_					      =TOKENS_TO_AUDIO_GENERATION_PARAMS
   SCREAMING_SNAKE_CASE_					      =TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        torch.manual_seed(0				)
        UpperCAmelCase__		:  Optional[Any]				=				SpectrogramNotesEncoder(
            max_length=2_0_4_8				,       vocab_size=1_5_3_6				,       d_model=7_6_8				,       dropout_rate=0.1				,       num_layers=1				,       num_heads=1				,       d_kv=4				,       d_ff=2_0_4_8				,       feed_forward_proj="gated-gelu"				,       )

        UpperCAmelCase__		:  Any				=				SpectrogramContEncoder(
            input_dims=1_2_8				,       targets_context_length=2_5_6				,       d_model=7_6_8				,       dropout_rate=0.1				,       num_layers=1				,       num_heads=1				,       d_kv=4				,       d_ff=2_0_4_8				,       feed_forward_proj="gated-gelu"				,       )

        UpperCAmelCase__		:  List[Any]				=				TaFilmDecoder(
            input_dims=1_2_8				,       targets_length=2_5_6				,       max_decoder_noise_time=2_0000.0				,       d_model=7_6_8				,       num_layers=1				,       num_heads=1				,       d_kv=4				,       d_ff=2_0_4_8				,       dropout_rate=0.1				,       )

        UpperCAmelCase__		:  Any				=				DDPMScheduler()

        UpperCAmelCase__		:  Optional[int]				=				{
            "notes_encoder": notes_encoder.eval(),
            "continuous_encoder": continuous_encoder.eval(),
            "decoder": decoder.eval(),
            "scheduler": scheduler,
            "melgan": None,
        }
        return components
   def 	__a				(						self	:     str				,       snake_case__	:     Optional[int]				,       snake_case__	:     Optional[Any]=0				):


        '''simple docstring'''
        if str(snake_case__				).startswith("mps"				):
             UpperCAmelCase__		:  Any				=				torch.manual_seed(snake_case__				)
        else:
             UpperCAmelCase__		:  Optional[Any]				=				torch.Generator(device=snake_case__				).manual_seed(snake_case__				)
        UpperCAmelCase__		:  Any				=				{
            "input_tokens": [
                [1_1_3_4, 9_0, 1_1_3_5, 1_1_3_3, 1_0_8_0, 1_1_2, 1_1_3_2, 1_0_8_0, 1_1_3_3, 1_0_7_9, 1_3_3, 1_1_3_2, 1_0_7_9, 1_1_3_3, 1] + [0] * 2_0_3_3
            ],
            "generator": generator,
            "num_inference_steps": 4,
            "output_type": "mel",
        }
        return inputs
   def 	__a				(						self	:     str				):


        '''simple docstring'''
        UpperCAmelCase__		:  str				=				"cpu"  # ensure determinism for the device-dependent torch.Generator
        UpperCAmelCase__		:  Optional[Any]				=				self.get_dummy_components()
        UpperCAmelCase__		:  Dict				=				SpectrogramDiffusionPipeline(**snake_case__				)
        UpperCAmelCase__		:  Optional[Any]				=				pipe.to(snake_case__				)
        pipe.set_progress_bar_config(disable=snake_case__				)

        UpperCAmelCase__		:  int				=				self.get_dummy_inputs(snake_case__				)
        UpperCAmelCase__		:  int				=				pipe(**snake_case__				)
        UpperCAmelCase__		:  int				=				output.audios

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

        assert mel_slice.shape == (3, 3)
        UpperCAmelCase__		:  Optional[Any]				=				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	:     Union[str, Any]				):


        '''simple docstring'''
        return super().test_save_load_local()
   @skip_mps
   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        return super().test_dict_tuple_outputs_equivalent()
   @skip_mps
   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        return super().test_save_load_optional_components()
   @skip_mps
   def 	__a				(						self	:     int				):


        '''simple docstring'''
        return super().test_attention_slicing_forward_pass()
   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        pass
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        pass




   @skip_mps
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        return super().test_progress_bar()




@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class        lowerCAmelCase__       (  unittest.TestCase					):
   def 	__a				(						self	:     List[str]				):


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


        '''simple docstring'''
        # TODO - test that pipeline can decode tokens in a callback
        # so that music can be played live
        UpperCAmelCase__		:  int				=				torch_device

        UpperCAmelCase__		:  Union[str, Any]				=				SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
        UpperCAmelCase__		:  int				=				pipe.melgan
        UpperCAmelCase__		:  Optional[int]				=				None

        UpperCAmelCase__		:  Tuple				=				pipe.to(snake_case__				)
        pipe.set_progress_bar_config(disable=snake_case__				)

        def callback(snake_case__	:     Union[str, Any]				,       snake_case__	:     Any				):
             # decode mel to audio
             UpperCAmelCase__		:  Any				=				melgan(input_features=mel_output.astype(np.floataa				)				)[0]
             assert len(audio[0]				) == 8_1_9_2_0 * (step + 1)
             # simulate that audio is played
             return audio

        UpperCAmelCase__		:  str				=				MidiProcessor()
        UpperCAmelCase__		:  Any				=				processor(snake_case__				)

        UpperCAmelCase__		:  Dict				=				input_tokens[:3]
        UpperCAmelCase__		:  List[Any]				=				torch.manual_seed(0				)
        pipe(snake_case__				,       num_inference_steps=5				,       generator=snake_case__				,       callback=snake_case__				,       output_type="mel"				)
   def 	__a				(						self	:     str				):


        '''simple docstring'''
        UpperCAmelCase__		:  Optional[Any]				=				torch_device

        UpperCAmelCase__		:  int				=				SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
        UpperCAmelCase__		:  List[str]				=				pipe.to(snake_case__				)
        pipe.set_progress_bar_config(disable=snake_case__				)
        UpperCAmelCase__		:  Tuple				=				MidiProcessor()

        UpperCAmelCase__		:  Optional[Any]				=				processor(snake_case__				)
        # just run two denoising loops
        UpperCAmelCase__		:  Optional[int]				=				input_tokens[:2]

        UpperCAmelCase__		:  Optional[Any]				=				torch.manual_seed(0				)
        UpperCAmelCase__		:  Dict				=				pipe(snake_case__				,       num_inference_steps=2				,       generator=snake_case__				)

        UpperCAmelCase__		:  str				=				output.audios[0]

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




   def 	__a				(						self	:     Union[str, Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Tuple				=				torch_device

        UpperCAmelCase__		:  Optional[int]				=				SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"				)
        UpperCAmelCase__		:  Optional[int]				=				pipe.to(snake_case__				)
        pipe.set_progress_bar_config(disable=snake_case__				)

        UpperCAmelCase__		:  Optional[int]				=				MidiProcessor()

        UpperCAmelCase__		:  List[str]				=				processor(snake_case__				)

        # just run 4 denoising loops
        UpperCAmelCase__		:  Tuple				=				input_tokens[:4]

        UpperCAmelCase__		:  Optional[int]				=				torch.manual_seed(0				)
        UpperCAmelCase__		:  int				=				pipe(snake_case__				,       num_inference_steps=1_0_0				,       generator=snake_case__				)

        UpperCAmelCase__		:  Dict				=				output.audios[0]
        assert abs(np.abs(snake_case__				).sum() - 9389.1111				) < 5e-2










"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				word.upper()
     UpperCAmelCase__		:  Union[str, Any]				=				0
     UpperCAmelCase__		:  Optional[Any]				=				{}
     UpperCAmelCase__		:  List[Any]				=				[]

     for letter in word:
          if letter not in letter_nums:
               UpperCAmelCase__		:  Tuple				=				str(snake_case							)
               next_num += 1
          word_pattern.append(letter_nums[letter]							)
     return ".".join(snake_case							)


if __name__ == "__main__":
 import pprint
 import time

 _lowerCAmelCase     :     Optional[Any]            =	time.time()
 with open("""dictionary.txt""") as in_file:
  _lowerCAmelCase     :     Optional[int]            =	in_file.read().splitlines()

 _lowerCAmelCase     :     dict            =	{}
 for word in word_list:
  _lowerCAmelCase     :     int            =	get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   _lowerCAmelCase     :     Any            =	[word]

 with open("""word_patterns.txt""", """w""") as out_file:
  out_file.write(pprint.pformat(all_patterns))

 _lowerCAmelCase     :     Union[str, Any]            =	round(time.time() - start_time, 2)
 print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
 # Done!  9,581 word patterns found in 0.58 seconds.


