


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


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

SCREAMING_SNAKE_CASE							    =							2_5_6


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      ["""melgan"""]


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       ):
        super().__init__()

        # From MELGAN
        __a					=  math.log(1E-5    )  # Matches MelGAN training.
        __a					=  4.0  # Largest value for most examples
        __a					=  128

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


     def 	snake_case_  (	self  ,       __A  ,       __A=(-1.0, 1.0)  ,       __A=False    ):
        __a	,    __a					=  output_range
        if clip:
           __a					=  torch.clip(__A  ,       self.min_value  ,       self.max_value    )
        # Scale to [0, 1].
        __a					=  (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 	snake_case_  (	self  ,       __A  ,       __A=(-1.0, 1.0)  ,       __A=False    ):
        __a	,    __a					=  input_range
        __a					=  torch.clip(__A  ,       __A  ,       __A    ) if clip else outputs
        # Scale to [0, 1].
        __a					=  (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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  input_tokens > 0
        __a	,    __a					=  self.notes_encoder(
            encoder_input_tokens=__A  ,       encoder_inputs_mask=__A    )

        __a	,    __a					=  self.continuous_encoder(
            encoder_inputs=__A  ,       encoder_inputs_mask=__A    )

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


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        __a					=  noise_time
        if not torch.is_tensor(__A    ):
           __a					=  torch.tensor([timesteps]  ,       dtype=torch.long  ,       device=input_tokens.device    )
        elif torch.is_tensor(__A    ) and len(timesteps.shape    ) == 0:
           __a					=  timesteps[None].to(input_tokens.device    )

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

        __a					=  self.decoder(
            encodings_and_masks=__A  ,       decoder_input_tokens=__A  ,       decoder_noise_time=__A    )
        return logits


     @torch.no_grad()
     def __call__(	self  ,       __A  ,       __A = None  ,       __A = 100  ,       __A = True  ,       __A = "numpy"  ,       __A = None  ,       __A = 1  ,       ):
        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(__A  ,       __A    ) or callback_steps <= 0)
        ):
           raise ValueError(
               f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
               f''' {type(__A    )}.'''    )

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

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

           __a					=  self.scale_features(
               __A  ,       output_range=[-1.0, 1.0]  ,       clip=__A    )

           __a					=  self.encode(
               input_tokens=torch.IntTensor([encoder_input_tokens]    ).to(device=self.device    )  ,       continuous_inputs=__A  ,       continuous_mask=__A  ,       )

           # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
           __a					=  randn_tensor(
               shape=encoder_continuous_inputs.shape  ,       generator=__A  ,       device=self.device  ,       dtype=self.decoder.dtype  ,       )

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

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

              # Compute previous output: x_t -> x_t-1
              __a					=  self.scheduler.step(__A  ,       __A  ,       __A  ,       generator=__A    ).prev_sample

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

           __a					=  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(__A  ,       __A    )

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

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

        if not return_dict:
           return (output,)

        return AudioPipelineOutput(audios=__A    )







def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						return 1 if input_a == input_a else 0




def     __SCREAMING_SNAKE_CASE							(       )		->     None:




						'''simple docstring'''







						assert xnor_gate(0      ,       0      ) == 1
						assert xnor_gate(0      ,       1      ) == 0
						assert xnor_gate(1      ,       0      ) == 0
						assert xnor_gate(1      ,       1      ) == 1


if __name__ == "__main__":
				print(xnor_gate(0, 0))
				print(xnor_gate(0, 1))
				print(xnor_gate(1, 0))
				print(xnor_gate(1, 1))

