






"""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


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

_lowerCAmelCase            =	2_56


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Any												=		["""melgan"""]






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

										# From MELGAN
										lowercase    :      str       = math.log(1E-5				)  # Matches MelGAN training.
										lowercase    :      Dict       = 4.0  # Largest value for most examples
										lowercase    :      Optional[int]       = 128

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






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

										lowercase      ,      lowercase    :      List[Any]       = self.continuous_encoder(
										    encoder_inputs=__magic_name__			, encoder_inputs_mask=__magic_name__				)

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






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

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

										lowercase    :      Dict       = self.decoder(
										    encodings_and_masks=__magic_name__			, decoder_input_tokens=__magic_name__			, decoder_noise_time=__magic_name__				)
										return logits






						@torch.no_grad()
						def __call__(		self  :Optional[int]			, __magic_name__  :List[List[int]]			, __magic_name__  :Optional[torch.Generator] = None			, __magic_name__  :int = 100			, __magic_name__  :bool = True			, __magic_name__  :str = "numpy"			, __magic_name__  :Optional[Callable[[int, int, torch.FloatTensor], None]] = None			, __magic_name__  :int = 1			, )      ->Union[AudioPipelineOutput, Tuple]:
										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(__magic_name__			, __magic_name__				) or callback_steps <= 0)
										):
														raise ValueError(
														    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
														    f""" {type(__magic_name__				)}."""				)

										lowercase    :      Any       = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]			, dtype=np.floataa				)
										lowercase    :      int       = np.zeros([1, 0, self.n_dims]			, np.floataa				)
										lowercase    :      Union[str, Any]       = torch.ones((1, TARGET_FEATURE_LENGTH)			, dtype=__magic_name__			, device=self.device				)

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

														lowercase    :      List[str]       = self.scale_features(
														    __magic_name__			, output_range=[-1.0, 1.0]			, clip=__magic_name__				)

														lowercase    :      Dict       = self.encode(
														    input_tokens=torch.IntTensor([encoder_input_tokens]				).to(device=self.device				)			, continuous_inputs=__magic_name__			, continuous_mask=__magic_name__			, )

														# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
														lowercase    :      int       = randn_tensor(
														    shape=encoder_continuous_inputs.shape			, generator=__magic_name__			, device=self.device			, dtype=self.decoder.dtype			, )

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

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

																		# Compute previous output: x_t -> x_t-1
																		lowercase    :      Tuple       = self.scheduler.step(__magic_name__			, __magic_name__			, __magic_name__			, generator=__magic_name__				).prev_sample

														lowercase    :      Optional[Any]       = self.scale_to_features(__magic_name__			, input_range=[-1.0, 1.0]				)
														lowercase    :      Optional[int]       = mel[:1]
														lowercase    :      Dict       = mel.cpu().float().numpy()

														lowercase    :      Union[str, Any]       = 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(__magic_name__			, __magic_name__				)

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

										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":
														lowercase    :      List[str]       = self.melgan(input_features=full_pred_mel.astype(np.floataa				)				)
										else:
														lowercase    :      Tuple       = full_pred_mel

										if not return_dict:
														return (output,)

										return AudioPipelineOutput(audios=__magic_name__				)








"""simple docstring"""







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCAmelCase            =	logging.getLogger(__name__)

if __name__ == "__main__":
				_lowerCAmelCase            =	argparse.ArgumentParser(
				    description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
				)
				parser.add_argument(
				    '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
				)
				parser.add_argument(
				    '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
				)
				parser.add_argument('--vocab_size', default=3_05_22, type=int)
				_lowerCAmelCase            =	parser.parse_args()

				logger.info(F'Loading data from {args.data_file}')
				with open(args.data_file, 'rb') as fp:
								_lowerCAmelCase            =	pickle.load(fp)

				logger.info('Counting occurrences for MLM.')
				_lowerCAmelCase            =	Counter()
				for tk_ids in data:
								counter.update(tk_ids)
				_lowerCAmelCase            =	[0] * args.vocab_size
				for k, v in counter.items():
								_lowerCAmelCase            =	v

				logger.info(F'Dump to {args.token_counts_dump}')
				with open(args.token_counts_dump, 'wb') as handle:
								pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
