


'''simple docstring'''


import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
						from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


lowerCamelCase__    	=							logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase__    	=							256






class 	lowerCAmelCase__			(			UpperCamelCase__	):
			lowerCAmelCase :	Dict          =     ["melgan"]




			def __init__(		self  :     str	,  lowerCamelCase__  :     SpectrogramNotesEncoder	,  lowerCamelCase__  :     SpectrogramContEncoder	,  lowerCamelCase__  :     TaFilmDecoder	,  lowerCamelCase__  :     DDPMScheduler	,  lowerCamelCase__  :     OnnxRuntimeModel if is_onnx_available() else Any	,  )   ->None:



					'''simple docstring'''







					super().__init__()

					# From MELGAN
					_UpperCAmelCase		:       Optional[int]								=      math.log(1E-5 )  # Matches MelGAN training.
					_UpperCAmelCase		:       List[Any]								=      4.0  # Largest value for most examples
					_UpperCAmelCase		:       Optional[Any]								=      1_28

					self.register_modules(
					    notes_encoder=__A	,  continuous_encoder=__A	,  decoder=__A	,  scheduler=__A	,  melgan=__A	,  )




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Tuple=(-1.0, 1.0)	,  lowerCamelCase__  :     Tuple=False )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Any								=      output_range
					if clip:
							_UpperCAmelCase		:       List[Any]								=      torch.clip(__A	,  self.min_value	,  self.max_value )
					# Scale to [0, 1].
					_UpperCAmelCase		:       str								=      (features - self.min_value) / (self.max_value - self.min_value)
					# Scale to [min_out, max_out].
					return zero_one * (max_out - min_out) + min_out




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Any=(-1.0, 1.0)	,  lowerCamelCase__  :     Optional[int]=False )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      input_range
					_UpperCAmelCase		:       Optional[int]								=      torch.clip(__A	,  __A	,  __A ) if clip else outputs
					# Scale to [0, 1].
					_UpperCAmelCase		:       Optional[Any]								=      (outputs - min_out) / (max_out - min_out)
					# Scale to [self.min_value, self.max_value].
					return zero_one * (self.max_value - self.min_value) + self.min_value




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     str )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      input_tokens > 0
					_UpperCAmelCase					,    _UpperCAmelCase		:       List[Any]								=      self.notes_encoder(
					    encoder_input_tokens=__A	,  encoder_inputs_mask=__A )

					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      self.continuous_encoder(
					    encoder_inputs=__A	,  encoder_inputs_mask=__A )

					return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Any )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      noise_time
					if not torch.is_tensor(__A ):
							_UpperCAmelCase		:       List[Any]								=      torch.tensor([timesteps]	,  dtype=torch.long	,  device=input_tokens.device )
					elif torch.is_tensor(__A ) and len(timesteps.shape ) == 0:
							_UpperCAmelCase		:       Union[str, Any]								=      timesteps[None].to(input_tokens.device )

					# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
					_UpperCAmelCase		:       Any								=      timesteps * torch.ones(input_tokens.shape[0]	,  dtype=timesteps.dtype	,  device=timesteps.device )

					_UpperCAmelCase		:       int								=      self.decoder(
					    encodings_and_masks=__A	,  decoder_input_tokens=__A	,  decoder_noise_time=__A )
					return logits






			@torch.no_grad()
			def __call__(		self  :     Tuple	,  lowerCamelCase__  :     List[List[int]]	,  lowerCamelCase__  :     Optional[torch.Generator] = None	,  lowerCamelCase__  :     int = 1_00	,  lowerCamelCase__  :     bool = True	,  lowerCamelCase__  :     str = "numpy"	,  lowerCamelCase__  :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,  lowerCamelCase__  :     int = 1	,  )   ->Union[AudioPipelineOutput, Tuple]:



					'''simple docstring'''







					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(__A	,  __A ) or callback_steps <= 0)
					):
							raise ValueError(
							    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
							    F""" {type(__A )}.""" )

					_UpperCAmelCase		:       Dict								=      np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]	,  dtype=np.floataa )
					_UpperCAmelCase		:       str								=      np.zeros([1, 0, self.n_dims]	,  np.floataa )
					_UpperCAmelCase		:       int								=      torch.ones((1, TARGET_FEATURE_LENGTH)	,  dtype=__A	,  device=self.device )

					for i, encoder_input_tokens in enumerate(__A ):
							if i == 0:
									_UpperCAmelCase		:       Union[str, Any]								=      torch.from_numpy(pred_mel[:1].copy() ).to(
									    device=self.device	,  dtype=self.decoder.dtype )
									# The first chunk has no previous context.
									_UpperCAmelCase		:       int								=      torch.zeros((1, TARGET_FEATURE_LENGTH)	,  dtype=__A	,  device=self.device )
							else:
									# The full song pipeline does not feed in a context feature, so the mask
									# will be all 0s after the feature converter. Because we know we're
									# feeding in a full context chunk from the previous prediction, set it
									# to all 1s.
									_UpperCAmelCase		:       Any								=      ones

							_UpperCAmelCase		:       Union[str, Any]								=      self.scale_features(
							    __A	,  output_range=[-1.0, 1.0]	,  clip=__A )

							_UpperCAmelCase		:       Any								=      self.encode(
							    input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device )	,  continuous_inputs=__A	,  continuous_mask=__A	,  )

							# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
							_UpperCAmelCase		:       Tuple								=      randn_tensor(
							    shape=encoder_continuous_inputs.shape	,  generator=__A	,  device=self.device	,  dtype=self.decoder.dtype	,  )

							# set step values
							self.scheduler.set_timesteps(__A )

							# Denoising diffusion loop
							for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
									_UpperCAmelCase		:       Optional[Any]								=      self.decode(
									    encodings_and_masks=__A	,  input_tokens=__A	,  noise_time=t / self.scheduler.config.num_train_timesteps	,  )

									# Compute previous output: x_t -> x_t-1
									_UpperCAmelCase		:       List[str]								=      self.scheduler.step(__A	,  __A	,  __A	,  generator=__A ).prev_sample

							_UpperCAmelCase		:       List[Any]								=      self.scale_to_features(__A	,  input_range=[-1.0, 1.0] )
							_UpperCAmelCase		:       Union[str, Any]								=      mel[:1]
							_UpperCAmelCase		:       Any								=      mel.cpu().float().numpy()

							_UpperCAmelCase		:       Tuple								=      np.concatenate([full_pred_mel, pred_mel[:1]]	,  axis=1 )

							# call the callback, if provided
							if callback is not None and i % callback_steps == 0:
									callback(__A	,  __A )

							logger.info("Generated segment"	,  __A )

					if output_type == "numpy" and not is_onnx_available():
							raise ValueError(
							    "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'." )
					elif output_type == "numpy" and self.melgan is None:
							raise ValueError(
							    "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'." )

					if output_type == "numpy":
							_UpperCAmelCase		:       Optional[Any]								=      self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
					else:
							_UpperCAmelCase		:       Tuple								=      full_pred_mel

					if not return_dict:
							return (output,)

					return AudioPipelineOutput(audios=__A )







'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase__    	=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase__    	=     '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Any )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      tempfile.mkdtemp()
					os.makedirs(os.path.join(self.diffusers_dir	,  "schedulers/" ) )
					_UpperCAmelCase		:       Optional[Any]								=      self.diffusers_dir
					shutil.copy(
					    os.path.join(lowerCamelCase__	,  "src/diffusers/schedulers/scheduling_ddpm.py" )	,  os.path.join(self.diffusers_dir	,  "schedulers/scheduling_ddpm.py" )	,  )




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



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      "src/diffusers"
					shutil.rmtree(self.diffusers_dir )




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Any=None )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
					if overwrite_result is not None:
							_UpperCAmelCase		:       Tuple								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
					_UpperCAmelCase		:       Optional[Any]								=      black.Mode(target_versions={black.TargetVersion.PYaa}	,  line_length=1_19 )
					_UpperCAmelCase		:       Tuple								=      black.format_str(lowerCamelCase__	,  mode=lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      os.path.join(self.diffusers_dir	,  "new_code.py" )
					with open(lowerCamelCase__	,  "w"	,  newline="\n" ) as f:
							f.write(lowerCamelCase__ )
					if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase__ ) ) == 0 )
					else:
							check_copies.is_copy_consistent(f.name	,  overwrite=lowerCamelCase__ )
							with open(lowerCamelCase__	,  "r" ) as f:
									self.assertTrue(f.read()	,  lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     str )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
					self.assertEqual(lowerCamelCase__	,  lowerCamelCase__ )






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



					'''simple docstring'''







					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"	,  "DDPMSchedulerOutput"	,  REFERENCE_CODE + "\n"	,  )

					# With no empty line at the end
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"	,  "DDPMSchedulerOutput"	,  lowerCamelCase__	,  )

					# Copy consistency with rename
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"	,  "TestSchedulerOutput"	,  re.sub("DDPM"	,  "Test"	,  lowerCamelCase__ )	,  )

					# Copy consistency with a really long name
					_UpperCAmelCase		:       int								=      "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
					self.check_copy_consistency(
					    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""	,  F"""{long_class_name}SchedulerOutput"""	,  re.sub("Bert"	,  lowerCamelCase__	,  lowerCamelCase__ )	,  )

					# Copy consistency with overwrite
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"	,  "TestSchedulerOutput"	,  lowerCamelCase__	,  overwrite_result=re.sub("DDPM"	,  "Test"	,  lowerCamelCase__ )	,  )



