

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


UpperCamelCase__  :      Optional[int]			=    logging.get_logger(__name__)

UpperCamelCase__  :      Union[str, Any]			=    '▁'

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

UpperCamelCase__  :      Any			=    {
    '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',
    },
}

UpperCamelCase__  :      Any			=    {'vinai/bartpho-syllable': 1_024}



class       __snake_case  (    snake_case__       ):
  __lowerCAmelCase     :	Dict						 =  VOCAB_FILES_NAMES
  __lowerCAmelCase     :	str						 =  PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase     :	Optional[int]						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase     :	Tuple						 =  ["""input_ids""", """attention_mask"""]

  def __init__(					self						,		_A						,		_A						,		_A="<s>"						,		_A="</s>"						,		_A="</s>"						,		_A="<s>"						,		_A="<unk>"						,		_A="<pad>"						,		_A="<mask>"						,		_A = None						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						AddedToken(UpperCAmelCase_						,		lstrip=UpperCAmelCase_						,		rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_						,		UpperCAmelCase_) else mask_token

      SCREAMING_SNAKE_CASE_									=						{} 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_						,		)

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

      # Load the reduced vocab

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

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

  def __getstate__(					self):
      SCREAMING_SNAKE_CASE_									=						self.__dict__.copy()
      SCREAMING_SNAKE_CASE_									=						None
      SCREAMING_SNAKE_CASE_									=						self.sp_model.serialized_model_proto()
      return state

  def __setstate__(					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						d

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

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):



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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = False):



      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 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
      SCREAMING_SNAKE_CASE_									=						[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 						lowerCAmelCase__     (					self):
      return len(self.fairseq_ids_to_tokens)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						{self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.sp_model.encode(UpperCAmelCase_						,		out_type=UpperCAmelCase_)

  def 						lowerCAmelCase__     (					self						,		_A):



      if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
      else:
          return self.unk_token_id

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.fairseq_ids_to_tokens[index]

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						''.join(UpperCAmelCase_).replace(UpperCAmelCase_						,		' ').strip()
      return out_string


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):



      if not os.path.isdir(UpperCAmelCase_):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          UpperCAmelCase_						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE_									=						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:
              SCREAMING_SNAKE_CASE_									=						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 typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A						,		_A						,		_A = None						,		):
      super().__init__()
      self.register_modules(transformer=_A						,		vae=_A						,		scheduler=_A)

      # create a imagenet -> id dictionary for easier use
      SCREAMING_SNAKE_CASE_									=						{}
      if idalabel is not None:
          for key, value in idalabel.items():
              for label in value.split(','):
                  SCREAMING_SNAKE_CASE_									=						int(_A)
          SCREAMING_SNAKE_CASE_									=						dict(sorted(self.labels.items()))

  def 						lowerCAmelCase__     (					self						,		_A):

      if not isinstance(_A						,		_A):
          SCREAMING_SNAKE_CASE_									=						list(_A)

      for l in label:
          if l not in self.labels:
              raise ValueError(
                  f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""")

      return [self.labels[l] for l in label]


  @torch.no_grad()
  def __call__(					self						,		_A						,		_A = 4.0						,		_A = None						,		_A = 50						,		_A = "pil"						,		_A = True						,		):
      SCREAMING_SNAKE_CASE_									=						len(_A)
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.sample_size
      SCREAMING_SNAKE_CASE_									=						self.transformer.config.in_channels

      SCREAMING_SNAKE_CASE_									=						randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)						,		generator=_A						,		device=self.device						,		dtype=self.transformer.dtype						,		)
      SCREAMING_SNAKE_CASE_									=						torch.cat([latents] * 2) if guidance_scale > 1 else latents

      SCREAMING_SNAKE_CASE_									=						torch.tensor(_A						,		device=self.device).reshape(-1)
      SCREAMING_SNAKE_CASE_									=						torch.tensor([1000] * batch_size						,		device=self.device)
      SCREAMING_SNAKE_CASE_									=						torch.cat([class_labels, class_null]						,		0) if guidance_scale > 1 else class_labels

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

      for t in self.progress_bar(self.scheduler.timesteps):
          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_									=						latent_model_input[: len(_A) // 2]
              SCREAMING_SNAKE_CASE_									=						torch.cat([half, half]						,		dim=0)
          SCREAMING_SNAKE_CASE_									=						self.scheduler.scale_model_input(_A						,		_A)

          SCREAMING_SNAKE_CASE_									=						t
          if not torch.is_tensor(_A):
              # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
              # This would be a good case for the `match` statement (Python 3.10+)
              SCREAMING_SNAKE_CASE_									=						latent_model_input.device.type == 'mps'
              if isinstance(_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						torch.floataa if is_mps else torch.floataa
              else:
                  SCREAMING_SNAKE_CASE_									=						torch.intaa if is_mps else torch.intaa
              SCREAMING_SNAKE_CASE_									=						torch.tensor([timesteps]						,		dtype=_A						,		device=latent_model_input.device)
          elif len(timesteps.shape) == 0:
              SCREAMING_SNAKE_CASE_									=						timesteps[None].to(latent_model_input.device)
          # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
          SCREAMING_SNAKE_CASE_									=						timesteps.expand(latent_model_input.shape[0])
          # predict noise model_output
          SCREAMING_SNAKE_CASE_									=						self.transformer(
              _A						,		timestep=_A						,		class_labels=_A).sample

          # perform guidance
          if guidance_scale > 1:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		len(_A) // 2						,		dim=0)

              SCREAMING_SNAKE_CASE_									=						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
              SCREAMING_SNAKE_CASE_									=						torch.cat([half_eps, half_eps]						,		dim=0)

              SCREAMING_SNAKE_CASE_									=						torch.cat([eps, rest]						,		dim=1)

          # learned sigma
          if self.transformer.config.out_channels // 2 == latent_channels:
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						torch.split(_A						,		_A						,		dim=1)
          else:
              SCREAMING_SNAKE_CASE_									=						noise_pred

          # compute previous image: x_t -> x_t-1
          SCREAMING_SNAKE_CASE_									=						self.scheduler.step(_A						,		_A						,		_A).prev_sample

      if guidance_scale > 1:
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						latent_model_input.chunk(2						,		dim=0)
      else:
          SCREAMING_SNAKE_CASE_									=						latent_model_input

      SCREAMING_SNAKE_CASE_									=						1 / self.vae.config.scaling_factor * latents
      SCREAMING_SNAKE_CASE_									=						self.vae.decode(_A).sample

      SCREAMING_SNAKE_CASE_									=						(samples / 2 + 0.5).clamp(0						,		1)

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      SCREAMING_SNAKE_CASE_									=						samples.cpu().permute(0						,		2						,		3						,		1).float().numpy()

      if output_type == "pil":
          SCREAMING_SNAKE_CASE_									=						self.numpy_to_pil(_A)

      if not return_dict:
          return (samples,)

      return ImagePipelineOutput(images=_A)


