






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_							=    256


class      lowercase__     (			_UpperCAmelCase	):
	a_         =["""melgan"""]

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







							'''simple docstring'''





							super().__init__()

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

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=(-1.0, 1.0)		,    __UpperCAmelCase=False       )->					List[str]:







							'''simple docstring'''





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







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						input_range
							lowerCAmelCase__       =						torch.clip(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ) if clip else outputs
							# Scale to [0, 1].
							lowerCAmelCase__       =						(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		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						input_tokens > 0
							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.notes_encoder(
							    encoder_input_tokens=__UpperCAmelCase		,    encoder_inputs_mask=__UpperCAmelCase       )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.continuous_encoder(
							    encoder_inputs=__UpperCAmelCase		,    encoder_inputs_mask=__UpperCAmelCase       )

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Union[str, Any]:







							'''simple docstring'''





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

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

							lowerCAmelCase__       =						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 = 100		,    __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       )}."       )

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

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

													lowerCAmelCase__       =						self.scale_features(
													    __UpperCAmelCase		,    output_range=[-1.0, 1.0]		,    clip=__UpperCAmelCase       )

													lowerCAmelCase__       =						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
													lowerCAmelCase__       =						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       )       ):
																			lowerCAmelCase__       =						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
																			lowerCAmelCase__       =						self.scheduler.step(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    generator=__UpperCAmelCase       ).prev_sample

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

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

							if not return_dict:
													return (output,)

							return AudioPipelineOutput(audios=__UpperCAmelCase       )














# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


a_							=    subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
a_							=    subprocess.check_output(F"git diff --name-only {fork_point_sha}".split()).decode('''utf-8''').split()

a_							=    '''|'''.join(sys.argv[1:])
a_							=    re.compile(rF"^({joined_dirs}).*?\.py$")

a_							=    [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')






