

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     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["melgan"]


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , )      ->							None:






              """simple docstring"""


              super().__init__()

              # From MELGAN
              __snake_case       	=							math.log(1E-5   )  # Matches MelGAN training.
              __snake_case       	=							4.0  # Largest value for most examples
              __snake_case       	=							1_28

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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=(-1.0, 1.0)      , _UpperCamelCase=False   )      ->							List[str]:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							output_range
              if clip:
                     __snake_case       	=							torch.clip(_UpperCamelCase      , self.min_value      , self.max_value   )
              # Scale to [0, 1].
              __snake_case       	=							(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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=(-1.0, 1.0)      , _UpperCamelCase=False   )      ->							List[Any]:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							input_range
              __snake_case       	=							torch.clip(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   ) if clip else outputs
              # Scale to [0, 1].
              __snake_case       	=							(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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							input_tokens > 0
              __snake_case	,    __snake_case       	=							self.notes_encoder(
                  encoder_input_tokens=_UpperCamelCase      , encoder_inputs_mask=_UpperCamelCase   )

              __snake_case	,    __snake_case       	=							self.continuous_encoder(
                  encoder_inputs=_UpperCamelCase      , encoder_inputs_mask=_UpperCamelCase   )

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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							Optional[int]:






              """simple docstring"""


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

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

              __snake_case       	=							self.decoder(
                  encodings_and_masks=_UpperCamelCase      , decoder_input_tokens=_UpperCamelCase      , decoder_noise_time=_UpperCamelCase   )
              return logits






       @torch.no_grad()
       def __call__(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = 1_00      , _UpperCamelCase = True      , _UpperCamelCase = "numpy"      , _UpperCamelCase = None      , _UpperCamelCase = 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   )}.'   )

              __snake_case       	=							np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]      , dtype=np.floataa   )
              __snake_case       	=							np.zeros([1, 0, self.n_dims]      , np.floataa   )
              __snake_case       	=							torch.ones((1, TARGET_FEATURE_LENGTH)      , dtype=_UpperCamelCase      , device=self.device   )

              for i, encoder_input_tokens in enumerate(_UpperCamelCase   ):
                     if i == 0:
                            __snake_case       	=							torch.from_numpy(pred_mel[:1].copy()   ).to(
                                device=self.device      , dtype=self.decoder.dtype   )
                            # The first chunk has no previous context.
                            __snake_case       	=							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.
                            __snake_case       	=							ones

                     __snake_case       	=							self.scale_features(
                         _UpperCamelCase      , output_range=[-1.0, 1.0]      , clip=_UpperCamelCase   )

                     __snake_case       	=							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
                     __snake_case       	=							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   )   ):
                            __snake_case       	=							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
                            __snake_case       	=							self.scheduler.step(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , generator=_UpperCamelCase   ).prev_sample

                     __snake_case       	=							self.scale_to_features(_UpperCamelCase      , input_range=[-1.0, 1.0]   )
                     __snake_case       	=							mel[:1]
                     __snake_case       	=							mel.cpu().float().numpy()

                     __snake_case       	=							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":
                     __snake_case       	=							self.melgan(input_features=full_pred_mel.astype(np.floataa   )   )
              else:
                     __snake_case       	=							full_pred_mel

              if not return_dict:
                     return (output,)

              return AudioPipelineOutput(audios=_UpperCamelCase   )




from PIL import Image







def 						lowerCamelCase__      ( __A    :Image		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case       	=							0
       __snake_case       	=							image.load()
       for i in range(__A		):
              for j in range(__A		):
                     __snake_case       	=							pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A		):
              for i in range(__A		):
                     __snake_case       	=							2_5_5 if pixels[i, j] > mean else 0
       return image


if __name__ == "__main__":
    UpperCamelCase__		 =     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')

