





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 				_UpperCamelCase       (						lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       Dict       = SpectrogramDiffusionPipeline
       _UpperCamelCase					:       Dict       = PipelineTesterMixin.required_optional_params - {
           '''callback''',
           '''latents''',
           '''callback_steps''',
           '''output_type''',
           '''num_images_per_prompt''',
       }
       _UpperCamelCase					:       str       = False
       _UpperCamelCase					:       Optional[Any]       = False
       _UpperCamelCase					:       str       = TOKENS_TO_AUDIO_GENERATION_PARAMS
       _UpperCamelCase					:       Any       = TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

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




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_ 							=	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"       , )

              UpperCamelCase_ 							=	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"       , )

              UpperCamelCase_ 							=	TaFilmDecoder(
                  input_dims=128       , targets_length=256       , max_decoder_noise_time=2_00_00.0       , d_model=768       , num_layers=1       , num_heads=1       , d_kv=4       , d_ff=2048       , dropout_rate=0.1       , )

              UpperCamelCase_ 							=	DDPMScheduler()

              UpperCamelCase_ 							=	{
                  "notes_encoder": notes_encoder.eval(),
                  "continuous_encoder": continuous_encoder.eval(),
                  "decoder": decoder.eval(),
                  "scheduler": scheduler,
                  "melgan": None,
              }
              return components

       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			List[str]=0		)							->			List[Any]:




              """simple docstring"""


              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     UpperCamelCase_ 							=	torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "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:			Union[str, Any]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	"cpu"  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase_ 							=	self.get_dummy_components()
              UpperCamelCase_ 							=	SpectrogramDiffusionPipeline(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	pipe.to(_SCREAMING_SNAKE_CASE		)
              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	output.audios

              UpperCamelCase_ 							=	mel[0, -3:, -3:]

              assert mel_slice.shape == (3, 3)
              UpperCamelCase_ 							=	np.array(
                  [-11.51_29_25, -4.78_82_15, -0.46_17_29_05, -2.05_17_15, -10.53_91_47, -10.97_09_63, -9.09_16_34, 4.0, 4.0]		)
              assert np.abs(mel_slice.flatten() - expected_slice		).max() < 1e-2

       @skip_mps
       def lowercase			(		self:			int		)							->			Any:




              """simple docstring"""


              return super().test_save_load_local()

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




              """simple docstring"""


              return super().test_dict_tuple_outputs_equivalent()

       @skip_mps
       def lowercase			(		self:			List[str]		)							->			Optional[int]:




              """simple docstring"""


              return super().test_save_load_optional_components()

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




              """simple docstring"""


              return super().test_attention_slicing_forward_pass()

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




              """simple docstring"""


              pass

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




              """simple docstring"""


              pass



       @skip_mps
       def lowercase			(		self:			str		)							->			Dict:




              """simple docstring"""


              return super().test_progress_bar()



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

       def lowercase			(		self:			Optional[int]		)							->			Union[str, Any]:




              """simple docstring"""


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

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




              """simple docstring"""


              UpperCamelCase_ 							=	torch_device

              UpperCamelCase_ 							=	SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"		)
              UpperCamelCase_ 							=	pipe.melgan
              UpperCamelCase_ 							=	None

              UpperCamelCase_ 							=	pipe.to(_SCREAMING_SNAKE_CASE		)
              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              def callback(_SCREAMING_SNAKE_CASE:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			int		):
                     # decode mel to audio
                     UpperCamelCase_ 							=	melgan(input_features=mel_output.astype(np.floataa		)		)[0]
                     assert len(audio[0]		) == 81920 * (step + 1)
                     # simulate that audio is played
                     return audio

              UpperCamelCase_ 							=	MidiProcessor()
              UpperCamelCase_ 							=	processor(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	input_tokens[:3]
              UpperCamelCase_ 							=	torch.manual_seed(0		)
              pipe(_SCREAMING_SNAKE_CASE       , num_inference_steps=5       , generator=_SCREAMING_SNAKE_CASE       , callback=_SCREAMING_SNAKE_CASE       , output_type="mel"		)

       def lowercase			(		self:			Any		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	torch_device

              UpperCamelCase_ 							=	SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"		)
              UpperCamelCase_ 							=	pipe.to(_SCREAMING_SNAKE_CASE		)
              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	MidiProcessor()

              UpperCamelCase_ 							=	processor(_SCREAMING_SNAKE_CASE		)
              # just run two denoising loops
              UpperCamelCase_ 							=	input_tokens[:2]

              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	pipe(_SCREAMING_SNAKE_CASE       , num_inference_steps=2       , generator=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	output.audios[0]

              assert abs(np.abs(_SCREAMING_SNAKE_CASE		).sum() - 36_12.8_41		) < 1e-1



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




              """simple docstring"""


              UpperCamelCase_ 							=	torch_device

              UpperCamelCase_ 							=	SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion"		)
              UpperCamelCase_ 							=	pipe.to(_SCREAMING_SNAKE_CASE		)
              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	MidiProcessor()

              UpperCamelCase_ 							=	processor(_SCREAMING_SNAKE_CASE		)

              # just run 4 denoising loops
              UpperCamelCase_ 							=	input_tokens[:4]

              UpperCamelCase_ 							=	torch.manual_seed(0		)
              UpperCamelCase_ 							=	pipe(_SCREAMING_SNAKE_CASE       , num_inference_steps=100       , generator=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	output.audios[0]
              assert abs(np.abs(_SCREAMING_SNAKE_CASE		).sum() - 93_89.11_11		) < 5e-2




from typing import Any

import numpy as np

class 					__UpperCamelCase					:







	def __init__( self:			Union[str, Any]	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





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

						__magic_name__				=     tableau
						__magic_name__,						__magic_name__				=     tableau.shape

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

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

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

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

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

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

						__magic_name__				=     ['']

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

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







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( *__UpperCamelCase:			int   ):

						'''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
						__magic_name__				=     ['x', 's', 'a']
						__magic_name__				=     []
						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 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						__magic_name__				=     self.objectives[-1]

						# Find entries of highest magnitude in objective rows
						__magic_name__				=     (objective == 'min') - (objective == 'max')
						__magic_name__				=     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:
											__magic_name__				=     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
						__magic_name__				=     slice(self.n_stages	,			self.n_rows   )

						# RHS
						__magic_name__				=     tableau[s, -1]

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

						# Array filled with nans
						__magic_name__				=     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
						__magic_name__				=     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
						__magic_name__				=     np.nanargmin(__UpperCamelCase   ) + self.n_stages
						return row_idx, col_idx







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     tableau[row_idx].copy()

						__magic_name__				=     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
						__magic_name__				=     piv_row
						return tableau







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			np.ndarray   ):

						'''simple docstring'''





						self.objectives.pop()

						if not self.objectives:
											return tableau

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

						# Delete the artificial variable columns
						__magic_name__				=     np.delete(__UpperCamelCase	,			__UpperCamelCase	,			axis=1   )

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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						for _ in range(1_00   ):
											# Completion of each stage removes an objective. If both stages
											# are complete, then no objectives are left
											if not self.objectives:
																__magic_name__				=     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   )

											__magic_name__,						__magic_name__				=     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
																__magic_name__				=     self.change_stage(self.tableau   )
											else:
																__magic_name__				=     self.pivot(self.tableau	,			__UpperCamelCase	,			__UpperCamelCase   )
						return {}







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			np.ndarray	,			__UpperCamelCase:			list[str]   ):

						'''simple docstring'''





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

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

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

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




