






'''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


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

a_					=	2_5_6






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):
 snake_case_												=							["""melgan"""]




 def __init__( self		:							Optional[Any]   , __lowercase		:							SpectrogramNotesEncoder   , __lowercase		:							SpectrogramContEncoder   , __lowercase		:							TaFilmDecoder   , __lowercase		:							DDPMScheduler   , __lowercase		:							OnnxRuntimeModel if is_onnx_available() else Any   , )		->  None:
       super().__init__()

       # From MELGAN
       SCREAMING_SNAKE_CASE__       :			Dict													=math.log(1e-5 )  # Matches MelGAN training.
       SCREAMING_SNAKE_CASE__       :			Any													=4.0  # Largest value for most examples
       SCREAMING_SNAKE_CASE__       :			Tuple													=1_28

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




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

       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Dict													=self.continuous_encoder(
           encoder_inputs=__lowercase   , encoder_inputs_mask=__lowercase )

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




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							Any   , __lowercase		:							Optional[int]   , __lowercase		:							List[str] )		->  List[str]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=noise_time
       if not torch.is_tensor(__lowercase ):
             SCREAMING_SNAKE_CASE__       :			int													=torch.tensor([timesteps]   , dtype=torch.long   , device=input_tokens.device )
       elif torch.is_tensor(__lowercase ) and len(timesteps.shape ) == 0:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=timesteps[None].to(input_tokens.device )

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

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




 @torch.no_grad()
 def __call__( self		:							Union[str, Any]   , __lowercase		:							List[List[int]]   , __lowercase		:							Optional[torch.Generator] = None   , __lowercase		:							int = 1_00   , __lowercase		:							bool = True   , __lowercase		:							str = "numpy"   , __lowercase		:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None   , __lowercase		:							int = 1   , )		->  Union[AudioPipelineOutput, Tuple]:
       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__lowercase   , __lowercase ) or callback_steps <= 0)
       ):
             raise ValueError(
                 F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 F" {type(__lowercase )}." )

       SCREAMING_SNAKE_CASE__       :			str													=np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]   , dtype=np.floataa )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=np.zeros([1, 0, self.n_dims]   , np.floataa )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.ones((1, TARGET_FEATURE_LENGTH)   , dtype=__lowercase   , device=self.device )

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

             SCREAMING_SNAKE_CASE__       :			Dict													=self.scale_features(
                 __lowercase   , output_range=[-1.0, 1.0]   , clip=__lowercase )

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

             # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
             SCREAMING_SNAKE_CASE__       :			Dict													=randn_tensor(
                 shape=encoder_continuous_inputs.shape   , generator=__lowercase   , device=self.device   , dtype=self.decoder.dtype   , )

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

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

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

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

             SCREAMING_SNAKE_CASE__       :			List[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(__lowercase   , __lowercase )

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

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

       if not return_dict:
             return (output,)

       return AudioPipelineOutput(audios=__lowercase )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_					=	{
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
      'InstructBlipQFormerModel',
      'InstructBlipPreTrainedModel',
      'InstructBlipForConditionalGeneration',
      'InstructBlipVisionModel',
  ]

if TYPE_CHECKING:
  from .configuration_instructblip import (
      INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      InstructBlipConfig,
      InstructBlipQFormerConfig,
      InstructBlipVisionConfig,
  )
  from .processing_instructblip import InstructBlipProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_instructblip import (
        INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        InstructBlipForConditionalGeneration,
        InstructBlipPreTrainedModel,
        InstructBlipQFormerModel,
        InstructBlipVisionModel,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)