
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					: List[Any]      =       logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase					: Optional[Any]      =       256


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       Union[str, Any]	     =				["melgan"]



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      )  ->					None:
				"""simple docstring"""
				super().__init__()

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

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



		def __UpperCamelCase ( self				,      A_				,      A_=(-1.0, 1.0)				,      A_=False       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     output_range
				if clip:
						UpperCamelCase		=     torch.clip(A_				,      self.min_value				,      self.max_value       )
				# Scale to [0, 1].
				UpperCamelCase		=     (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				,      A_				,      A_=(-1.0, 1.0)				,      A_=False       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     input_range
				UpperCamelCase		=     torch.clip(A_				,      A_				,      A_       ) if clip else outputs
				# Scale to [0, 1].
				UpperCamelCase		=     (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				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     input_tokens > 0
				UpperCamelCase	,  UpperCamelCase		=     self.notes_encoder(
				    encoder_input_tokens=A_				,      encoder_inputs_mask=A_       )

				UpperCamelCase	,  UpperCamelCase		=     self.continuous_encoder(
				    encoder_inputs=A_				,      encoder_inputs_mask=A_       )

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



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     noise_time
				if not torch.is_tensor(A_       ):
						UpperCamelCase		=     torch.tensor([timesteps]				,      dtype=torch.long				,      device=input_tokens.device       )
				elif torch.is_tensor(A_       ) and len(timesteps.shape       ) == 0:
						UpperCamelCase		=     timesteps[None].to(input_tokens.device       )

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

				UpperCamelCase		=     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				,      )  ->					Union[AudioPipelineOutput, Tuple]:
				"""simple docstring"""
				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_       )}.'''       )

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

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

						UpperCamelCase		=     self.scale_features(
						    A_				,      output_range=[-1.0, 1.0]				,      clip=A_       )

						UpperCamelCase		=     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
						UpperCamelCase		=     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       )       ):
								UpperCamelCase		=     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
								UpperCamelCase		=     self.scheduler.step(A_				,      A_				,      A_				,      generator=A_       ).prev_sample

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

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

				if not return_dict:
						return (output,)

				return AudioPipelineOutput(audios=A_       )


import requests


def       A       (					lowercase    ,       lowercase )   ->  None:
		'''simple docstring'''

		UpperCamelCase		=     {'Content-Type': 'application/json'}
		UpperCamelCase		=     requests.post(lowercase    ,       json={'text': message_body}    ,       headers=lowercase )
		if response.status_code != 200:
				UpperCamelCase		=     (
				    'Request to slack returned an error '
				    f'''{response.status_code}, the response is:\n{response.text}'''
				)
				raise ValueError(lowercase )


if __name__ == "__main__":
					# Set the slack url to the one provided by Slack when you create the webhook at
					# https://my.slack.com/services/new/incoming-webhook/
					send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")
