



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A       =						logging.get_logger(__name__)

__A       =						'''▁'''

__A       =						{'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__A       =						{
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

__A       =						{'''vinai/bartpho-syllable''': 1024}






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: List[str]		       =							VOCAB_FILES_NAMES
	a__				: Tuple		       =							PRETRAINED_VOCAB_FILES_MAP
	a__				: Optional[int]		       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__				: int		       =							["input_ids", "attention_mask"]




	def __init__(     self     :						Optional[int]						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Dict="<s>"						,  __UpperCAmelCase     :						List[str]="</s>"						,  __UpperCAmelCase     :						str="</s>"						,  __UpperCAmelCase     :						List[Any]="<s>"						,  __UpperCAmelCase     :						Union[str, Any]="<unk>"						,  __UpperCAmelCase     :						List[str]="<pad>"						,  __UpperCAmelCase     :						Any="<mask>"						,  __UpperCAmelCase     :						Optional[Dict[str, Any]] = None						,  **__UpperCAmelCase     :						str						,  )      ->			None:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_=       AddedToken(__UpperCAmelCase						,  lstrip=__UpperCAmelCase						,  rstrip=__UpperCAmelCase					) if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) else mask_token

							UpperCAmelCase_=       {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__UpperCAmelCase						,  eos_token=__UpperCAmelCase						,  unk_token=__UpperCAmelCase						,  sep_token=__UpperCAmelCase						,  cls_token=__UpperCAmelCase						,  pad_token=__UpperCAmelCase						,  mask_token=__UpperCAmelCase						,  sp_model_kwargs=self.sp_model_kwargs						,  **__UpperCAmelCase						,  )

							UpperCAmelCase_=       vocab_file
							UpperCAmelCase_=       monolingual_vocab_file
							UpperCAmelCase_=       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(__UpperCAmelCase					)					)

							# Load the reduced vocab

							# Keep order of special tokens for backward compatibility
							UpperCAmelCase_=       {}
							UpperCAmelCase_=       0
							for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
													if str(__UpperCAmelCase					) not in self.fairseq_tokens_to_ids:
																			UpperCAmelCase_=       cnt
																			cnt += 1
							with open(__UpperCAmelCase						,  """r"""						,  encoding="""utf-8"""					) as f:
													for line in f.readlines():
																			UpperCAmelCase_=       line.strip().split()[0]
																			UpperCAmelCase_=       len(self.fairseq_tokens_to_ids					)
							if str(__UpperCAmelCase					) not in self.fairseq_tokens_to_ids:
													UpperCAmelCase_=       len(self.fairseq_tokens_to_ids					)

							UpperCAmelCase_=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}




	def __getstate__(     self     :						Union[str, Any]					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.__dict__.copy()
							UpperCAmelCase_=       None
							UpperCAmelCase_=       self.sp_model.serialized_model_proto()
							return state




	def __setstate__(     self     :						Optional[int]						,  __UpperCAmelCase     :						Optional[Any]					)      ->			Optional[int]:
							UpperCAmelCase_=       d

							# for backward compatibility
							if not hasattr(self						,  """sp_model_kwargs"""					):
													UpperCAmelCase_=       {}

							UpperCAmelCase_=       spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:

							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_=       [self.cls_token_id]
							UpperCAmelCase_=       [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None						,  __UpperCAmelCase     :						bool = False					)      ->			List[int]:

							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__UpperCAmelCase						,  token_ids_a=__UpperCAmelCase						,  already_has_special_tokens=__UpperCAmelCase					)

							if token_ids_a is None:
													return [1] + ([0] * len(__UpperCAmelCase					)) + [1]
							return [1] + ([0] * len(__UpperCAmelCase					)) + [1, 1] + ([0] * len(__UpperCAmelCase					)) + [1]




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
							UpperCAmelCase_=       [self.sep_token_id]
							UpperCAmelCase_=       [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep					) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			str:
							return len(self.fairseq_ids_to_tokens					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Dict:
							UpperCAmelCase_=       {self.convert_ids_to_tokens(__UpperCAmelCase					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						str					)      ->			List[str]:
							return self.sp_model.encode(__UpperCAmelCase						,  out_type=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						str					)      ->			List[str]:
							if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
							else:
													return self.unk_token_id




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Any					)      ->			Tuple:
							return self.fairseq_ids_to_tokens[index]




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						int					)      ->			List[str]:
							UpperCAmelCase_=       """""".join(__UpperCAmelCase					).replace(__UpperCAmelCase						,  """ """					).strip()
							return out_string




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Optional[str] = None					)      ->			Tuple[str]:
							if not os.path.isdir(__UpperCAmelCase					):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
							UpperCAmelCase_=       os.path.join(
							    __UpperCAmelCase						,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
							UpperCAmelCase_=       os.path.join(
							    __UpperCAmelCase						,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]						,  )

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__UpperCAmelCase					) and os.path.isfile(self.vocab_file					):
													copyfile(self.vocab_file						,  __UpperCAmelCase					)
							elif not os.path.isfile(self.vocab_file					):
													with open(__UpperCAmelCase						,  """wb"""					) as fi:
																			UpperCAmelCase_=       self.sp_model.serialized_model_proto()
																			fi.write(__UpperCAmelCase					)

							if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
							    __UpperCAmelCase					) and os.path.isfile(self.monolingual_vocab_file					):
													copyfile(self.monolingual_vocab_file						,  __UpperCAmelCase					)
							elif not os.path.isfile(self.monolingual_vocab_file					):
													with open(__UpperCAmelCase						,  """w"""						,  encoding="""utf-8"""					) as fp:
																			for token in self.fairseq_tokens_to_ids:
																									if token not in self.all_special_tokens:
																															fp.write(F"""{str(__UpperCAmelCase					)} \n"""					)

							return out_vocab_file, out_monolingual_vocab_file









from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						np.ndarray = None						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = 0						,  __UpperCAmelCase     :						int = None						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Generator = None						,  __UpperCAmelCase     :						float = 0						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						torch.Tensor = None						,  __UpperCAmelCase     :						Union[str, Any]=True						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size					) == int:
													UpperCAmelCase_=       (self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													UpperCAmelCase_=       randn_tensor(
													    (
													        batch_size,
													        self.unet.config.in_channels,
													        self.unet.config.sample_size[0],
													        self.unet.config.sample_size[1],
													    )						,  generator=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 2 - 1
													UpperCAmelCase_=       torch.tensor(input_image[np.newaxis, :, :]						,  dtype=torch.float					).to(self.device					)

													if self.vqvae is not None:
																			UpperCAmelCase_=       self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  self.scheduler.timesteps[start_step - 1]					)

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]					)					):
													if isinstance(self.unet						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

													if isinstance(self.scheduler						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  eta=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]
													else:
																			UpperCAmelCase_=       self.scheduler.step(
																			    model_output=__UpperCAmelCase						,  timestep=__UpperCAmelCase						,  sample=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  )["""prev_sample"""]

													if mask is not None:
																			if mask_start > 0:
																									UpperCAmelCase_=       mask[:, step, :, :mask_start]
																			if mask_end > 0:
																									UpperCAmelCase_=       mask[:, step, :, -mask_end:]

							if self.vqvae is not None:
													# 0.18215 was scaling factor used in training to ensure unit variance
													UpperCAmelCase_=       1 / self.vqvae.config.scaling_factor * images
													UpperCAmelCase_=       self.vqvae.decode(__UpperCAmelCase					)["""sample"""]

							UpperCAmelCase_=       (images / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       images.cpu().permute(0						,  2						,  3						,  1					).numpy()
							UpperCAmelCase_=       (images * 255).round().astype("""uint8"""					)
							UpperCAmelCase_=       list(
							    (Image.fromarray(_[:, :, 0]					) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(__UpperCAmelCase						,  mode="""RGB"""					).convert("""L"""					) for _ in images)					)

							UpperCAmelCase_=       [self.mel.image_to_audio(__UpperCAmelCase					) for _ in images]
							if not return_dict:
													return images, (self.mel.get_sample_rate(), audios)

							return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase					)[:, np.newaxis, :]					)						,  **ImagePipelineOutput(__UpperCAmelCase					)					)




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps						,  (0,)					)					):
													UpperCAmelCase_=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
													UpperCAmelCase_=       self.scheduler.alphas_cumprod[t]
													UpperCAmelCase_=       (
													    self.scheduler.alphas_cumprod[prev_timestep]
													    if prev_timestep >= 0
													    else self.scheduler.final_alpha_cumprod
													)
													UpperCAmelCase_=       1 - alpha_prod_t
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													UpperCAmelCase_=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
													UpperCAmelCase_=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
													UpperCAmelCase_=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




