



"""simple docstring"""






from typing import Any

import numpy as np




class    __snake_case:




    def __init__(   self    ,							A_    ,							A_			):





      '''simple docstring'''



      if np.any(tableau[:, -1]    ,							where=tableau[:, -1] < 0			):
        raise ValueError('''RHS must be > 0'''			)

      _SCREAMING_SNAKE_CASE        =			tableau
      _SCREAMING_SNAKE_CASE        =			tableau.shape

      # Number of decision variables x1, x2, x3...
      _SCREAMING_SNAKE_CASE        =			n_vars

      # Number of artificial variables to be minimised
      _SCREAMING_SNAKE_CASE        =			len(np.where(tableau[self.n_vars : -1] == -1			)[0]			)

      # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
      _SCREAMING_SNAKE_CASE        =			(self.n_art_vars > 0) + 1

      # Number of slack variables added to make inequalities into equalities
      _SCREAMING_SNAKE_CASE        =			self.n_rows - self.n_stages

      # Objectives for each stage
      _SCREAMING_SNAKE_CASE        =			['max']

      # In two stage simplex, first minimise then maximise
      if self.n_art_vars:
        self.objectives.append('''min'''			)

      _SCREAMING_SNAKE_CASE        =			['']

      # Index of current pivot row and column
      _SCREAMING_SNAKE_CASE        =			None
      _SCREAMING_SNAKE_CASE        =			None

      # Does objective row only contain (non)-negative values?
      _SCREAMING_SNAKE_CASE        =			False




    @staticmethod
    def   A   (   *A_			):





      '''simple docstring'''



      if len(_UpperCAmelCase			) != 3:
        raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''			)

      if not all(x >= 0 and isinstance(_UpperCAmelCase    ,							_UpperCAmelCase			) for x in args			):
        raise ValueError('''All arguments must be non-negative integers'''			)

      # decision | slack | artificial
      _SCREAMING_SNAKE_CASE        =			['x', 's', 'a']
      _SCREAMING_SNAKE_CASE        =			[]
      for i in range(3			):
        for j in range(args[i]			):
          titles.append(string_starts[i] + str(j + 1			)			)
      titles.append('''RHS'''			)
      return titles




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.objectives[-1]

      # Find entries of highest magnitude in objective rows
      _SCREAMING_SNAKE_CASE        =			(objective == 'min') - (objective == 'max')
      _SCREAMING_SNAKE_CASE        =			np.argmax(sign * tableau[0, : self.n_vars]			)

      # Choice is only valid if below 0 for maximise, and above for minimise
      if sign * self.tableau[0, col_idx] <= 0:
        _SCREAMING_SNAKE_CASE        =			True
        return 0, 0

      # Pivot row is chosen as having the lowest quotient when elements of
      # the pivot column divide the right-hand side

      # Slice excluding the objective rows
      _SCREAMING_SNAKE_CASE        =			slice(self.n_stages    ,							self.n_rows			)

      # RHS
      _SCREAMING_SNAKE_CASE        =			tableau[s, -1]

      # Elements of pivot column within slice
      _SCREAMING_SNAKE_CASE        =			tableau[s, col_idx]

      # Array filled with nans
      _SCREAMING_SNAKE_CASE        =			np.full(self.n_rows - self.n_stages    ,							np.nan			)

      # If element in pivot column is greater than zeron_stages, return
      # quotient or nan otherwise
      _SCREAMING_SNAKE_CASE        =			np.divide(_UpperCAmelCase    ,							_UpperCAmelCase    ,							out=_UpperCAmelCase    ,							where=divisor > 0			)

      # Arg of minimum quotient excluding the nan values. n_stages is added
      # to compensate for earlier exclusion of objective columns
      _SCREAMING_SNAKE_CASE        =			np.nanargmin(_UpperCAmelCase			) + self.n_stages
      return row_idx, col_idx




    def   A   (   self    ,							A_    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			tableau[row_idx].copy()

      _SCREAMING_SNAKE_CASE        =			piv_row[col_idx]

      # Entry becomes 1
      piv_row *= 1 / piv_val

      # Variable in pivot column becomes basic, ie the only non-zero entry
      for idx, coeff in enumerate(tableau[:, col_idx]			):
        tableau[idx] += -coeff * piv_row
      _SCREAMING_SNAKE_CASE        =			piv_row
      return tableau




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      self.objectives.pop()

      if not self.objectives:
        return tableau

      # Slice containing ids for artificial columns
      _SCREAMING_SNAKE_CASE        =			slice(-self.n_art_vars - 1    ,							-1			)

      # Delete the artificial variable columns
      _SCREAMING_SNAKE_CASE        =			np.delete(_UpperCAmelCase    ,							_UpperCAmelCase    ,							axis=1			)

      # Delete the objective row of the first stage
      _SCREAMING_SNAKE_CASE        =			np.delete(_UpperCAmelCase    ,							0    ,							axis=0			)

      _SCREAMING_SNAKE_CASE        =			1
      self.n_rows -= 1
      _SCREAMING_SNAKE_CASE        =			0
      _SCREAMING_SNAKE_CASE        =			False
      return tableau




    def   A   (   self			):





      '''simple docstring'''



      for _ in range(100			):
        # Completion of each stage removes an objective. If both stages
        # are complete, then no objectives are left
        if not self.objectives:
          _SCREAMING_SNAKE_CASE        =			self.generate_col_titles(
              self.n_vars    ,							self.n_slack    ,							self.n_art_vars			)

          # Find the values of each variable at optimal solution
          return self.interpret_tableau(self.tableau    ,							self.col_titles			)

        _SCREAMING_SNAKE_CASE        =			self.find_pivot(self.tableau			)

        # If there are no more negative values in objective row
        if self.stop_iter:
          # Delete artificial variable columns and rows. Update attributes
          _SCREAMING_SNAKE_CASE        =			self.change_stage(self.tableau			)
        else:
          _SCREAMING_SNAKE_CASE        =			self.pivot(self.tableau    ,							_UpperCAmelCase    ,							_UpperCAmelCase			)
      return {}




    def   A   (   self    ,							A_    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{'P': abs(tableau[0, -1]			)}

      for i in range(self.n_vars			):
        # Gives ids of nonzero entries in the ith column
        _SCREAMING_SNAKE_CASE        =			np.nonzero(tableau[:, i]			)
        _SCREAMING_SNAKE_CASE        =			len(nonzero[0]			)

        # First entry in the nonzero ids
        _SCREAMING_SNAKE_CASE        =			nonzero[0][0]
        _SCREAMING_SNAKE_CASE        =			tableau[nonzero_rowidx, i]

        # If there is only one nonzero value in column, which is one
        if n_nonzero == nonzero_val == 1:
          _SCREAMING_SNAKE_CASE        =			tableau[nonzero_rowidx, -1]
          _SCREAMING_SNAKE_CASE        =			rhs_val

        # Check for basic variables
      for title in col_titles:
        # Don't add RHS or slack variables to output dict
        if title[0] not in "R-s-a":
          output_dict.setdefault(_UpperCAmelCase    ,							0			)
      return output_dict


if __name__ == "__main__":
    import doctest

    doctest.testmod()




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()


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



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




										"""simple docstring"""




										torch.manual_seed(0   )
										__magic_name__					:				Tuple				=   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__					:				Union[str, 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__					:				Optional[Any]				=   TaFilmDecoder(
										    input_dims=128  ,   targets_length=256  ,   max_decoder_noise_time=20000.0  ,   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__					:				Tuple				=   {
										    'notes_encoder': notes_encoder.eval(),
										    'continuous_encoder': continuous_encoder.eval(),
										    'decoder': decoder.eval(),
										    'scheduler': scheduler,
										    'melgan': None,
										}
										return components
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase=0   ):




										"""simple docstring"""




										if str(_UpperCAmelCase   ).startswith('mps'   ):
																__magic_name__					:				str				=   torch.manual_seed(_UpperCAmelCase   )
										else:
																__magic_name__					:				Optional[int]				=   torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )
										__magic_name__					:				List[str]				=   {
										    '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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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

										__magic_name__					:				List[Any]				=   self.get_dummy_inputs(_UpperCAmelCase   )
										__magic_name__					:				Dict				=   pipe(**_UpperCAmelCase   )
										__magic_name__					:				Optional[Any]				=   output.audios

										__magic_name__					:				Dict				=   mel[0, -3:, -3:]

										assert mel_slice.shape == (3, 3)
										__magic_name__					:				Union[str, Any]				=   np.array(
										    [-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0]   )
										assert np.abs(mel_slice.flatten() - expected_slice   ).max() < 1E-2
				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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




										"""simple docstring"""




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




										"""simple docstring"""




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




										"""simple docstring"""




										return super().test_attention_slicing_forward_pass()
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										pass
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										pass





				@skip_mps
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return super().test_progress_bar()






@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class      snake_case__	(   unittest.TestCase	):
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   torch_device

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

										__magic_name__					:				List[str]				=   pipe.to(_UpperCAmelCase   )
										pipe.set_progress_bar_config(disable=_UpperCAmelCase   )

										def callback(_UpperCAmelCase  ,   _UpperCAmelCase   ):
																# decode mel to audio
																__magic_name__					:				Any				=   melgan(input_features=mel_output.astype(np.floataa   )   )[0]
																assert len(audio[0]   ) == 8_1920 * (step + 1)
																# simulate that audio is played
																return audio

										__magic_name__					:				Optional[Any]				=   MidiProcessor()
										__magic_name__					:				str				=   processor(_UpperCAmelCase   )

										__magic_name__					:				Optional[int]				=   input_tokens[:3]
										__magic_name__					:				Optional[Any]				=   torch.manual_seed(0   )
										pipe(_UpperCAmelCase  ,   num_inference_steps=5  ,   generator=_UpperCAmelCase  ,   callback=_UpperCAmelCase  ,   output_type='mel'   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   torch_device

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

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

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

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

										assert abs(np.abs(_UpperCAmelCase   ).sum() - 3612.841   ) < 1E-1





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   torch_device

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

										__magic_name__					:				List[str]				=   MidiProcessor()

										__magic_name__					:				Union[str, Any]				=   processor(_UpperCAmelCase   )

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

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

										__magic_name__					:				Dict				=   output.audios[0]
										assert abs(np.abs(_UpperCAmelCase   ).sum() - 9389.1111   ) < 5E-2


