





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


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

__A     =    256



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["melgan"]

 def __init__(self    :       List[Any]	,			UpperCAmelCase_    :       SpectrogramNotesEncoder	,			UpperCAmelCase_    :       SpectrogramContEncoder	,			UpperCAmelCase_    :       TaFilmDecoder	,			UpperCAmelCase_    :       DDPMScheduler	,			UpperCAmelCase_    :       OnnxRuntimeModel if is_onnx_available() else Any	,			)	->None:



   '''simple docstring'''



   super().__init__()

   # From MELGAN
   lowerCamelCase__:				Any        =math.log(1E-5)  # Matches MelGAN training.
   lowerCamelCase__:				Tuple        =4.0  # Largest value for most examples
   lowerCamelCase__:				Optional[Any]        =128

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Any=(-1.0, 1.0)	,			UpperCAmelCase_    :       Optional[int]=False)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				int        =output_range
   if clip:
     lowerCamelCase__:				List[str]        =torch.clip(UpperCAmelCase_	,			self.min_value	,			self.max_value)
   # Scale to [0, 1].
   lowerCamelCase__:				List[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 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       str=(-1.0, 1.0)	,			UpperCAmelCase_    :       Union[str, Any]=False)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Optional[int]        =input_range
   lowerCamelCase__:				List[str]        =torch.clip(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_) if clip else outputs
   # Scale to [0, 1].
   lowerCamelCase__:				Dict        =(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 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       str)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =input_tokens > 0
   lowerCamelCase__  , lowerCamelCase__:				str        =self.notes_encoder(
       encoder_input_tokens=UpperCAmelCase_	,			encoder_inputs_mask=UpperCAmelCase_)

   lowerCamelCase__  , lowerCamelCase__:				Optional[Any]        =self.continuous_encoder(
       encoder_inputs=UpperCAmelCase_	,			encoder_inputs_mask=UpperCAmelCase_)

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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Optional[int])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =noise_time
   if not torch.is_tensor(UpperCAmelCase_):
     lowerCamelCase__:				int        =torch.tensor([timesteps]	,			dtype=torch.long	,			device=input_tokens.device)
   elif torch.is_tensor(UpperCAmelCase_) and len(timesteps.shape) == 0:
     lowerCamelCase__:				int        =timesteps[None].to(input_tokens.device)

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

   lowerCamelCase__:				Optional[Any]        =self.decoder(
       encodings_and_masks=UpperCAmelCase_	,			decoder_input_tokens=UpperCAmelCase_	,			decoder_noise_time=UpperCAmelCase_)
   return logits







 @torch.no_grad()
 def __call__(self    :       Optional[Any]	,			UpperCAmelCase_    :       List[List[int]]	,			UpperCAmelCase_    :       Optional[torch.Generator] = None	,			UpperCAmelCase_    :       int = 100	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       str = "numpy"	,			UpperCAmelCase_    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,			UpperCAmelCase_    :       int = 1	,			)	->Union[AudioPipelineOutput, Tuple]:



   '''simple docstring'''



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

   lowerCamelCase__:				int        =np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]	,			dtype=np.floataa)
   lowerCamelCase__:				int        =np.zeros([1, 0, self.n_dims]	,			np.floataa)
   lowerCamelCase__:				List[Any]        =torch.ones((1, TARGET_FEATURE_LENGTH)	,			dtype=UpperCAmelCase_	,			device=self.device)

   for i, encoder_input_tokens in enumerate(UpperCAmelCase_):
     if i == 0:
       lowerCamelCase__:				List[Any]        =torch.from_numpy(pred_mel[:1].copy()).to(
           device=self.device	,			dtype=self.decoder.dtype)
       # The first chunk has no previous context.
       lowerCamelCase__:				List[str]        =torch.zeros((1, TARGET_FEATURE_LENGTH)	,			dtype=UpperCAmelCase_	,			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.
       lowerCamelCase__:				List[Any]        =ones

     lowerCamelCase__:				List[str]        =self.scale_features(
         UpperCAmelCase_	,			output_range=[-1.0, 1.0]	,			clip=UpperCAmelCase_)

     lowerCamelCase__:				List[Any]        =self.encode(
         input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device)	,			continuous_inputs=UpperCAmelCase_	,			continuous_mask=UpperCAmelCase_	,			)

     # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
     lowerCamelCase__:				Optional[int]        =randn_tensor(
         shape=encoder_continuous_inputs.shape	,			generator=UpperCAmelCase_	,			device=self.device	,			dtype=self.decoder.dtype	,			)

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

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

       # Compute previous output: x_t -> x_t-1
       lowerCamelCase__:				str        =self.scheduler.step(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_	,			generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:				List[Any]        =self.scale_to_features(UpperCAmelCase_	,			input_range=[-1.0, 1.0])
     lowerCamelCase__:				Any        =mel[:1]
     lowerCamelCase__:				int        =mel.cpu().float().numpy()

     lowerCamelCase__:				List[Any]        =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(UpperCAmelCase_	,			UpperCAmelCase_)

     logger.info("Generated segment"	,			UpperCAmelCase_)

   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":
     lowerCamelCase__:				int        =self.melgan(input_features=full_pred_mel.astype(np.floataa))
   else:
     lowerCamelCase__:				Optional[int]        =full_pred_mel

   if not return_dict:
     return (output,)

   return AudioPipelineOutput(audios=UpperCAmelCase_)








import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__A     =    get_logger(__name__)



class        _SCREAMING_SNAKE_CASE						(     enum.Enum   ):







 '''simple docstring'''
 lowercase_		=      "all_checks"
 lowercase_		=      "basic_checks"
 lowercase_		=      "no_checks"



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None		)					->		Optional[int]:







  """simple docstring"""





  if expected_checksums is None:
    logger.info("Unable to verify checksums."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedDownloadedFile(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				List[Any]        =[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  lowerCamelCase__:				Union[str, Any]        =" for " + verification_name if verification_name is not None else ""
  if len(__a		) > 0:
    raise NonMatchingChecksumError(
        F"""Checksums didn't match{for_verification_name}:\n"""
        F"""{bad_urls}\n"""
        "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"		)
  logger.info("All the checksums matched successfully" + for_verification_name		)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







  """simple docstring"""





  if expected_splits is None:
    logger.info("Unable to verify splits sizes."		)
    return
  if len(set(__a		) - set(__a		)		) > 0:
    raise ExpectedMoreSplits(str(set(__a		) - set(__a		)		)		)
  if len(set(__a		) - set(__a		)		) > 0:
    raise UnexpectedSplits(str(set(__a		) - set(__a		)		)		)
  lowerCamelCase__:				Optional[int]        =[
      {"expected": expected_splits[name], "recorded": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(__a		) > 0:
    raise NonMatchingSplitsSizesError(str(__a		)		)
  logger.info("All the splits matched successfully."		)





def 					lowerCAmelCase_			(	__a     ,   __a = True		)					->		dict:







  """simple docstring"""





  if record_checksum:
    lowerCamelCase__:				str        =shaaaa()
    with open(__a     ,   "rb"		) as f:
      for chunk in iter(lambda: f.read(1 << 20		)     ,   b""		):
        m.update(__a		)
      lowerCamelCase__:				Dict        =m.hexdigest()
  else:
    lowerCamelCase__:				List[str]        =None
  return {"num_bytes": os.path.getsize(__a		), "checksum": checksum}






def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False

