


'''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 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					["melgan"]


 def __init__(      self							:	Any			,       __lowerCamelCase							:	SpectrogramNotesEncoder			,       __lowerCamelCase							:	SpectrogramContEncoder			,       __lowerCamelCase							:	TaFilmDecoder			,       __lowerCamelCase							:	DDPMScheduler			,       __lowerCamelCase							:	OnnxRuntimeModel if is_onnx_available() else Any			,       ):
    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__	:List[Any]      =      1_2_8

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any]			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Optional[Any]=(-1.0, 1.0)			,       __lowerCamelCase							:	Tuple=False ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      output_range
    if clip:
       UpperCAmelCase__	:Any      =      torch.clip(__lowerCamelCase			,       self.min_value			,       self.max_value )
    # Scale to [0, 1].
    UpperCAmelCase__	:Tuple      =      (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							:	Optional[Any]			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	List[Any]=(-1.0, 1.0)			,       __lowerCamelCase							:	int=False ):
    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      input_range
    UpperCAmelCase__	:Optional[Any]      =      torch.clip(__lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase ) if clip else outputs
    # Scale to [0, 1].
    UpperCAmelCase__	:int      =      (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							:	Tuple			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	Optional[Any]			,       __lowerCamelCase							:	Any ):
    UpperCAmelCase__	:List[Any]      =      input_tokens > 0
    UpperCAmelCase__   ,						UpperCAmelCase__	:int      =      self.notes_encoder(
        encoder_input_tokens=__lowerCamelCase			,       encoder_inputs_mask=__lowerCamelCase )

    UpperCAmelCase__   ,						UpperCAmelCase__	:int      =      self.continuous_encoder(
        encoder_inputs=__lowerCamelCase			,       encoder_inputs_mask=__lowerCamelCase )

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


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

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

    UpperCAmelCase__	:Dict      =      self.decoder(
        encodings_and_masks=__lowerCamelCase			,       decoder_input_tokens=__lowerCamelCase			,       decoder_noise_time=__lowerCamelCase )
    return logits





 @torch.no_grad()
 def __call__(      self							:	List[str]			,       __lowerCamelCase							:	List[List[int]]			,       __lowerCamelCase							:	Optional[torch.Generator] = None			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	str = "numpy"			,       __lowerCamelCase							:	Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,       __lowerCamelCase							:	int = 1			,       ):
    if (callback_steps is None) or (
        callback_steps is not None and (not isinstance(__lowerCamelCase			,       __lowerCamelCase ) or callback_steps <= 0)
    ):
       raise ValueError(
           f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
           f''' {type(__lowerCamelCase )}.''' )

    UpperCAmelCase__	:Any      =      np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]			,       dtype=np.floataa )
    UpperCAmelCase__	:Optional[int]      =      np.zeros([1, 0, self.n_dims]			,       np.floataa )
    UpperCAmelCase__	:Dict      =      torch.ones((1, TARGET_FEATURE_LENGTH)			,       dtype=__lowerCamelCase			,       device=self.device )

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

       UpperCAmelCase__	:Dict      =      self.scale_features(
           __lowerCamelCase			,       output_range=[-1.0, 1.0]			,       clip=__lowerCamelCase )

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

       # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
       UpperCAmelCase__	:Union[str, Any]      =      randn_tensor(
           shape=encoder_continuous_inputs.shape			,       generator=__lowerCamelCase			,       device=self.device			,       dtype=self.decoder.dtype			,       )

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

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

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

       UpperCAmelCase__	:str      =      self.scale_to_features(__lowerCamelCase			,       input_range=[-1.0, 1.0] )
       UpperCAmelCase__	:int      =      mel[:1]
       UpperCAmelCase__	:Union[str, Any]      =      mel.cpu().float().numpy()

       UpperCAmelCase__	: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(__lowerCamelCase			,       __lowerCamelCase )

       logger.info('''Generated segment'''			,       __lowerCamelCase )

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

    if not return_dict:
       return (output,)

    return AudioPipelineOutput(audios=__lowerCamelCase )







'''simple docstring'''





from __future__ import annotations

from collections.abc import Sequence
from typing import Literal


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str						):
   UpperCAmelCase__	:Any      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      0
   for i in range(len(UpperCamelCase_						)						):
      if lista[i] != lista[i]:
         count += 1
         UpperCAmelCase__	:Union[str, Any]      =      '''_'''
   if count > 1:
      return False
   else:
      return "".join(UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:int      =      []
   while True:
      UpperCAmelCase__	:Dict      =      ['''$'''] * len(UpperCamelCase_						)
      UpperCAmelCase__	:List[str]      =      []
      for i in range(len(UpperCamelCase_						)						):
         for j in range(i + 1,   len(UpperCamelCase_						)						):
            UpperCAmelCase__	:Optional[Any]      =      compare_string(binary[i],   binary[j]						)
            if k is False:
               UpperCAmelCase__	:Any      =      '''*'''
               UpperCAmelCase__	:List[Any]      =      '''*'''
               temp.append('''X'''						)
      for i in range(len(UpperCamelCase_						)						):
         if checka[i] == "$":
            pi.append(binary[i]						)
      if len(UpperCamelCase_						) == 0:
         return pi
      UpperCAmelCase__	:Tuple      =      list(set(UpperCamelCase_						)						)


def        a__     (					UpperCamelCase_						:      int,   UpperCamelCase_						:      Sequence[float]						):
   UpperCAmelCase__	:int      =      []
   for minterm in minterms:
      UpperCAmelCase__	:int      =      ''''''
      for _ in range(UpperCamelCase_						):
         UpperCAmelCase__	:Optional[int]      =      str(minterm % 2						) + string
         minterm //= 2
      temp.append(UpperCamelCase_						)
   return temp


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      str,   UpperCamelCase_						:      int						):
   UpperCAmelCase__	:Dict      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      list(UpperCamelCase_						)
   UpperCAmelCase__	:str      =      0
   for i in range(len(UpperCamelCase_						)						):
      if lista[i] != lista[i]:
         count_n += 1
   return count_n == count


def        a__     (					UpperCamelCase_						:      list[list[int]],   UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:Optional[Any]      =      []
   UpperCAmelCase__	:List[Any]      =      [0] * len(UpperCamelCase_						)
   for i in range(len(chart[0]						)						):
      UpperCAmelCase__	:Optional[Any]      =      0
      UpperCAmelCase__	:Union[str, Any]      =      -1
      for j in range(len(UpperCamelCase_						)						):
         if chart[j][i] == 1:
            count += 1
            UpperCAmelCase__	:Any      =      j
      if count == 1:
         UpperCAmelCase__	:Any      =      1
   for i in range(len(UpperCamelCase_						)						):
      if select[i] == 1:
         for j in range(len(chart[0]						)						):
            if chart[i][j] == 1:
               for k in range(len(UpperCamelCase_						)						):
                  UpperCAmelCase__	:int      =      0
         temp.append(prime_implicants[i]						)
   while True:
      UpperCAmelCase__	:Optional[int]      =      0
      UpperCAmelCase__	:Dict      =      -1
      UpperCAmelCase__	:Optional[Any]      =      0
      for i in range(len(UpperCamelCase_						)						):
         UpperCAmelCase__	:str      =      chart[i].count(1						)
         if count_n > max_n:
            UpperCAmelCase__	:Any      =      count_n
            UpperCAmelCase__	:List[Any]      =      i

      if max_n == 0:
         return temp

      temp.append(prime_implicants[rem]						)

      for i in range(len(chart[0]						)						):
         if chart[rem][i] == 1:
            for j in range(len(UpperCamelCase_						)						):
               UpperCAmelCase__	:Optional[int]      =      0


def        a__     (					UpperCamelCase_						:      list[str],   UpperCamelCase_						:      list[str]						):
   UpperCAmelCase__	:List[str]      =      [[0 for x in range(len(UpperCamelCase_						)						)] for x in range(len(UpperCamelCase_						)						)]
   for i in range(len(UpperCamelCase_						)						):
      UpperCAmelCase__	:Tuple      =      prime_implicants[i].count('''_'''						)
      for j in range(len(UpperCamelCase_						)						):
         if is_for_table(prime_implicants[i],   binary[j],   UpperCamelCase_						):
            UpperCAmelCase__	:List[str]      =      1

   return chart


def        a__     (					):
   UpperCAmelCase__	:int      =      int(input('''Enter the no. of variables\n'''						)						)
   UpperCAmelCase__	:Tuple      =      [
       float(UpperCamelCase_						)
       for x in input(
           '''Enter the decimal representation of Minterms \'Spaces Separated\'\n'''						).split()
   ]
   UpperCAmelCase__	:Union[str, Any]      =      decimal_to_binary(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Optional[Any]      =      check(UpperCamelCase_						)
   print('''Prime Implicants are:'''						)
   print(UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      prime_implicant_chart(UpperCamelCase_,   UpperCamelCase_						)

   UpperCAmelCase__	:Dict      =      selection(UpperCamelCase_,   UpperCamelCase_						)
   print('''Essential Prime Implicants are:'''						)
   print(UpperCamelCase_						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



