



'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_UpperCamelCase											=						logging.get_logger(__name__)


_UpperCamelCase											=						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
_UpperCamelCase											=						{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase											=						{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=VOCAB_FILES_NAMES
  a_												=PRETRAINED_VOCAB_FILES_MAP
  a_												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_												=["""input_ids""", """attention_mask"""]
  a_												=BartTokenizer




  def __init__( self      :	Tuple			,	_a      :	Any=None			,	_a      :	Dict=None			,	_a      :	str=None			,	_a      :	Tuple="replace"			,	_a      :	str="<s>"			,	_a      :	Union[str, Any]="</s>"			,	_a      :	Optional[Any]="</s>"			,	_a      :	Union[str, Any]="<s>"			,	_a      :	Optional[int]="<unk>"			,	_a      :	List[str]="<pad>"			,	_a      :	int="<mask>"			,	_a      :	int=False			,	_a      :	Union[str, Any]=True			,	**_a      :	str			,	)					->					Any:
    super().__init__(
        _a			,	_a			,	tokenizer_file=_a			,	errors=_a			,	bos_token=_a			,	eos_token=_a			,	sep_token=_a			,	cls_token=_a			,	unk_token=_a			,	pad_token=_a			,	mask_token=_a			,	add_prefix_space=_a			,	trim_offsets=_a			,	**_a			,	)

    __lowerCamelCase       :       Optional[Any]           = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
    if pre_tok_state.get('add_prefix_space'			,	_a   ) != add_prefix_space:
      __lowerCamelCase       :       List[Any]           = getattr(_a			,	pre_tok_state.pop('type'   )   )
      __lowerCamelCase       :       Any           = add_prefix_space
      __lowerCamelCase       :       Any           = pre_tok_class(**_a   )

    __lowerCamelCase       :       Tuple           = add_prefix_space

    # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
    __lowerCamelCase       :       int           = 'post_processor'
    __lowerCamelCase       :       Tuple           = getattr(self.backend_tokenizer			,	_a			,	_a   )
    if tokenizer_component_instance:
      __lowerCamelCase       :       Optional[int]           = json.loads(tokenizer_component_instance.__getstate__()   )

      # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
      if "sep" in state:
        __lowerCamelCase       :       Tuple           = tuple(state['sep']   )
      if "cls" in state:
        __lowerCamelCase       :       Optional[int]           = tuple(state['cls']   )

      __lowerCamelCase       :       Dict           = False

      if state.get('add_prefix_space'			,	_a   ) != add_prefix_space:
        __lowerCamelCase       :       List[Any]           = add_prefix_space
        __lowerCamelCase       :       int           = True

      if state.get('trim_offsets'			,	_a   ) != trim_offsets:
        __lowerCamelCase       :       Tuple           = trim_offsets
        __lowerCamelCase       :       str           = True

      if changes_to_apply:
        __lowerCamelCase       :       Tuple           = getattr(_a			,	state.pop('type'   )   )
        __lowerCamelCase       :       Dict           = component_class(**_a   )
        setattr(self.backend_tokenizer			,	_a			,	_a   )




  @property
  def 						_lowercase     ( self      :	Optional[Any]   )					->					str:
    if self._mask_token is None:
      if self.verbose:
        logger.error('Using mask_token, but it is not set yet.'   )
      return None
    return str(self._mask_token   )




  @mask_token.setter
  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	Optional[Any]   )					->					List[Any]:
    __lowerCamelCase       :       Optional[Any]           = AddedToken(_a			,	lstrip=_a			,	rstrip=_a   ) if isinstance(_a			,	_a   ) else value
    __lowerCamelCase       :       Optional[Any]           = value




  def 						_lowercase     ( self      :	Dict			,	*_a      :	Optional[Any]			,	**_a      :	Tuple   )					->					BatchEncoding:
    __lowerCamelCase       :       Tuple           = kwargs.get('is_split_into_words'			,	_a   )

    if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          'to use it with pretokenized inputs.'   )

    return super()._batch_encode_plus(*_a			,	**_a   )




  def 						_lowercase     ( self      :	Optional[Any]			,	*_a      :	str			,	**_a      :	List[str]   )					->					BatchEncoding:
    __lowerCamelCase       :       Dict           = kwargs.get('is_split_into_words'			,	_a   )

    if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
          'to use it with pretokenized inputs.'   )

    return super()._encode_plus(*_a			,	**_a   )




  def 						_lowercase     ( self      :	int			,	_a      :	str			,	_a      :	Optional[str] = None   )					->					Tuple[str]:
    __lowerCamelCase       :       Dict           = self._tokenizer.model.save(_a			,	name=_a   )
    return tuple(_a   )




  def 						_lowercase     ( self      :	List[Any]			,	_a      :	Optional[int]			,	_a      :	str=None   )					->					Dict:
    __lowerCamelCase       :       Dict           = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
      return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




  def 						_lowercase     ( self      :	Dict			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    __lowerCamelCase       :       Union[str, Any]           = [self.sep_token_id]
    __lowerCamelCase       :       Optional[Any]           = [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]











'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

def        a_     (							_lowerCAmelCase       )      ->      str:
  __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = image.size
  __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  __lowerCamelCase       :       Optional[Any]           = image.resize((w, h)       ,resample=PIL_INTERPOLATION['lanczos']       )
  __lowerCamelCase       :       List[Any]           = np.array(_lowerCAmelCase       ).astype(np.floataa       ) / 255.0
  __lowerCamelCase       :       Optional[Any]           = image[None].transpose(0       ,3       ,1       ,2       )
  __lowerCamelCase       :       int           = torch.from_numpy(_lowerCAmelCase       )
  return 2.0 * image - 1.0






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	str			,	_a      :	VQModel			,	_a      :	UNetaDModel			,	_a      :	Union[
          DDIMScheduler,
          PNDMScheduler,
          LMSDiscreteScheduler,
          EulerDiscreteScheduler,
          EulerAncestralDiscreteScheduler,
          DPMSolverMultistepScheduler,
      ]			,	)					->					Optional[Any]:
    super().__init__()
    self.register_modules(vqvae=_a			,	unet=_a			,	scheduler=_a   )




  @torch.no_grad()
  def __call__( self      :	List[Any]			,	_a      :	Union[torch.Tensor, PIL.Image.Image] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[int] = 100			,	_a      :	Optional[float] = 0.0			,	_a      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	)					->					Union[Tuple, ImagePipelineOutput]:
    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       Any           = 1
    elif isinstance(_a			,	torch.Tensor   ):
      __lowerCamelCase       :       Any           = image.shape[0]
    else:
      raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a   )}'   )

    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       List[str]           = preprocess(_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = image.shape[-2:]

    # in_channels should be 6: 3 for latents, 3 for low resolution image
    __lowerCamelCase       :       Union[str, Any]           = (batch_size, self.unet.config.in_channels // 2, height, width)
    __lowerCamelCase       :       Tuple           = next(self.unet.parameters()   ).dtype

    __lowerCamelCase       :       Optional[int]           = randn_tensor(_a			,	generator=_a			,	device=self.device			,	dtype=_a   )

    __lowerCamelCase       :       Optional[int]           = image.to(device=self.device			,	dtype=_a   )

    # set timesteps and move to the correct device
    self.scheduler.set_timesteps(_a			,	device=self.device   )
    __lowerCamelCase       :       Union[str, Any]           = self.scheduler.timesteps

    # scale the initial noise by the standard deviation required by the scheduler
    __lowerCamelCase       :       List[Any]           = latents * self.scheduler.init_noise_sigma

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    __lowerCamelCase       :       int           = 'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
    __lowerCamelCase       :       List[str]           = {}
    if accepts_eta:
      __lowerCamelCase       :       str           = eta

    for t in self.progress_bar(_a   ):
      # concat latents and low resolution image in the channel dimension.
      __lowerCamelCase       :       str           = torch.cat([latents, image]			,	dim=1   )
      __lowerCamelCase       :       Union[str, Any]           = self.scheduler.scale_model_input(_a			,	_a   )
      # predict the noise residual
      __lowerCamelCase       :       Optional[int]           = self.unet(_a			,	_a   ).sample
      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a			,	**_a   ).prev_sample

    # decode the image latents with the VQVAE
    __lowerCamelCase       :       Union[str, Any]           = self.vqvae.decode(_a   ).sample
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_a			,	-1.0			,	1.0   )
    __lowerCamelCase       :       List[str]           = image / 2 + 0.5
    __lowerCamelCase       :       Optional[Any]           = image.cpu().permute(0			,	2			,	3			,	1   ).numpy()

    if output_type == "pil":
      __lowerCamelCase       :       Tuple           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






