


"""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


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

UpperCAmelCase				=						256




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					['''melgan''']





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   )  ->  None:
      super().__init__()

      # From MELGAN
      _lowerCamelCase	:					Dict   =		math.log(1e-5)  # Matches MelGAN training.
      _lowerCamelCase	:					List[Any]   =		4.0  # Largest value for most examples
      _lowerCamelCase	:					Dict   =		128

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





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

      _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		self.continuous_encoder(
          encoder_inputs=SCREAMING_SNAKE_CASE      ,   encoder_inputs_mask=SCREAMING_SNAKE_CASE)

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      _lowerCamelCase	:					Optional[int]   =		noise_time
      if not torch.is_tensor(SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					int   =		torch.tensor([timesteps]      ,   dtype=torch.long      ,   device=input_tokens.device)
      elif torch.is_tensor(SCREAMING_SNAKE_CASE) and len(timesteps.shape) == 0:
         _lowerCamelCase	:					Tuple   =		timesteps[None].to(input_tokens.device)

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

      _lowerCamelCase	:					List[str]   =		self.decoder(
          encodings_and_masks=SCREAMING_SNAKE_CASE      ,   decoder_input_tokens=SCREAMING_SNAKE_CASE      ,   decoder_noise_time=SCREAMING_SNAKE_CASE)
      return logits





   @torch.no_grad()
   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 100      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = "numpy"      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 1      ,   )  ->  Union[AudioPipelineOutput, Tuple]:
      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE) or callback_steps <= 0)
      ):
         raise ValueError(
             F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
             F' {type(SCREAMING_SNAKE_CASE)}.')

      _lowerCamelCase	:					Optional[Any]   =		np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]      ,   dtype=np.floataa)
      _lowerCamelCase	:					Dict   =		np.zeros([1, 0, self.n_dims]      ,   np.floataa)
      _lowerCamelCase	:					List[Any]   =		torch.ones((1, TARGET_FEATURE_LENGTH)      ,   dtype=SCREAMING_SNAKE_CASE      ,   device=self.device)

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

         _lowerCamelCase	:					List[Any]   =		self.scale_features(
             SCREAMING_SNAKE_CASE      ,   output_range=[-1.0, 1.0]      ,   clip=SCREAMING_SNAKE_CASE)

         _lowerCamelCase	:					Dict   =		self.encode(
             input_tokens=torch.IntTensor([encoder_input_tokens]).to(device=self.device)      ,   continuous_inputs=SCREAMING_SNAKE_CASE      ,   continuous_mask=SCREAMING_SNAKE_CASE      ,   )

         # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
         _lowerCamelCase	:					List[str]   =		randn_tensor(
             shape=encoder_continuous_inputs.shape      ,   generator=SCREAMING_SNAKE_CASE      ,   device=self.device      ,   dtype=self.decoder.dtype      ,   )

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

         # Denoising diffusion loop
         for j, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
            _lowerCamelCase	:					str   =		self.decode(
                encodings_and_masks=SCREAMING_SNAKE_CASE      ,   input_tokens=SCREAMING_SNAKE_CASE      ,   noise_time=t / self.scheduler.config.num_train_timesteps      ,   )

            # Compute previous output: x_t -> x_t-1
            _lowerCamelCase	:					Tuple   =		self.scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE).prev_sample

         _lowerCamelCase	:					Any   =		self.scale_to_features(SCREAMING_SNAKE_CASE      ,   input_range=[-1.0, 1.0])
         _lowerCamelCase	:					int   =		mel[:1]
         _lowerCamelCase	:					Optional[int]   =		mel.cpu().float().numpy()

         _lowerCamelCase	:					str   =		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(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         logger.info("""Generated segment"""      ,   SCREAMING_SNAKE_CASE)

      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	:					Union[str, Any]   =		self.melgan(input_features=full_pred_mel.astype(np.floataa))
      else:
         _lowerCamelCase	:					Optional[Any]   =		full_pred_mel

      if not return_dict:
         return (output,)

      return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["ConditionalDetrFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["ConditionalDetrImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Any				   =					[
     "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConditionalDetrForObjectDetection",
     "ConditionalDetrForSegmentation",
     "ConditionalDetrModel",
     "ConditionalDetrPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


