




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_UpperCAmelCase		:  Dict	=   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase=8      ):
  lowercase			:List[str]							=  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  lowercase			:List[Any]							=  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor







class       __lowerCAmelCase				(							lowerCAmelCase):






       def __init__(      self:    Tuple				,    _lowerCAmelCase:    UNetaDConditionModel				,    _lowerCAmelCase:    DDPMScheduler				,    _lowerCAmelCase:    VQModel				,    ):
         super().__init__()

         self.register_modules(
             unet=_lowerCAmelCase				,    scheduler=_lowerCAmelCase				,    movq=_lowerCAmelCase				,    )
         lowercase			:List[Any]							=  2 ** (len(self.movq.config.block_out_channels ) - 1)






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Any				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Optional[int] ):
         if latents is None:
           lowercase			:int							=  randn_tensor(_lowerCAmelCase				,    generator=_lowerCAmelCase				,    device=_lowerCAmelCase				,    dtype=_lowerCAmelCase )
         else:
           if latents.shape != shape:
             raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}" )
           lowercase			:Optional[Any]							=  latents.to(_lowerCAmelCase )

         lowercase			:int							=  latents * scheduler.init_noise_sigma
         return latents






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    List[str]=0 ):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError("Please install accelerate via `pip install accelerate`" )

         lowercase			:List[Any]							=  torch.device(F"cuda:{gpu_id}" )

         lowercase			:List[str]							=  [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(_lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Dict=0 ):
         if is_accelerate_available() and is_accelerate_version(">="				,    "0.17.0.dev0" ):
           from accelerate import cpu_offload_with_hook
         else:
           raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )

         lowercase			:List[Any]							=  torch.device(F"cuda:{gpu_id}" )

         if self.device.type != "cpu":
           self.to("cpu"				,    silence_dtype_warnings=_lowerCAmelCase )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         lowercase			:List[Any]							=  None
         for cpu_offloaded_model in [self.unet, self.movq]:
           lowercase		,			lowercase			:Dict							=  cpu_offload_with_hook(_lowerCAmelCase				,    _lowerCAmelCase				,    prev_module_hook=_lowerCAmelCase )

         # We'll offload the last model manually.
         lowercase			:Dict							=  hook






       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         if not hasattr(self.unet				,    "_hf_hook" ):
           return self.device
         for module in self.unet.modules():
           if (
               hasattr(_lowerCAmelCase				,    "_hf_hook" )
               and hasattr(module._hf_hook				,    "execution_device" )
               and module._hf_hook.execution_device is not None
           ):
             return torch.device(module._hf_hook.execution_device )
         return self.device






       @torch.no_grad()
       @replace_example_docstring(_lowerCAmelCase )
       def __call__(      self:    str				,    _lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]				,    _lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]				,    _lowerCAmelCase:    int = 5_12				,    _lowerCAmelCase:    int = 5_12				,    _lowerCAmelCase:    int = 1_00				,    _lowerCAmelCase:    float = 4.0				,    _lowerCAmelCase:    int = 1				,    _lowerCAmelCase:    Optional[Union[torch.Generator, List[torch.Generator]]] = None				,    _lowerCAmelCase:    Optional[torch.FloatTensor] = None				,    _lowerCAmelCase:    Optional[str] = "pil"				,    _lowerCAmelCase:    bool = True				,    ):
         lowercase			:str							=  self._execution_device

         lowercase			:List[str]							=  guidance_scale > 1.0

         if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:Any							=  torch.cat(_lowerCAmelCase				,    dim=0 )
         lowercase			:Any							=  image_embeds.shape[0] * num_images_per_prompt
         if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:List[Any]							=  torch.cat(_lowerCAmelCase				,    dim=0 )

         if do_classifier_free_guidance:
           lowercase			:int							=  image_embeds.repeat_interleave(_lowerCAmelCase				,    dim=0 )
           lowercase			:Any							=  negative_image_embeds.repeat_interleave(_lowerCAmelCase				,    dim=0 )

         lowercase			:Optional[int]							=  torch.cat([negative_image_embeds, image_embeds]				,    dim=0 ).to(dtype=self.unet.dtype				,    device=_lowerCAmelCase )

         self.scheduler.set_timesteps(_lowerCAmelCase				,    device=_lowerCAmelCase )
         lowercase			:Optional[Any]							=  self.scheduler.timesteps

         lowercase			:Tuple							=  self.unet.config.in_channels

         lowercase		,			lowercase			:Optional[int]							=  downscale_height_and_width(_lowerCAmelCase				,    _lowerCAmelCase				,    self.movq_scale_factor )

         # create initial latent
         lowercase			:List[Any]							=  self.prepare_latents(
             (batch_size, num_channels_latents, height, width)				,    image_embeds.dtype				,    _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    self.scheduler				,    )

         for i, t in enumerate(self.progress_bar(_lowerCAmelCase ) ):
           # expand the latents if we are doing classifier free guidance
           lowercase			:Tuple							=  torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

           lowercase			:List[str]							=  {"image_embeds": image_embeds}
           lowercase			:List[Any]							=  self.unet(
               sample=_lowerCAmelCase				,    timestep=_lowerCAmelCase				,    encoder_hidden_states=_lowerCAmelCase				,    added_cond_kwargs=_lowerCAmelCase				,    return_dict=_lowerCAmelCase				,    )[0]

           if do_classifier_free_guidance:
             lowercase		,			lowercase			:List[Any]							=  noise_pred.split(latents.shape[1]				,    dim=1 )
             lowercase		,			lowercase			:Any							=  noise_pred.chunk(2 )
             lowercase		,			lowercase			:Dict							=  variance_pred.chunk(2 )
             lowercase			:str							=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             lowercase			:Any							=  torch.cat([noise_pred, variance_pred_text]				,    dim=1 )

           if not (
               hasattr(self.scheduler.config				,    "variance_type" )
               and self.scheduler.config.variance_type in ["learned", "learned_range"]
           ):
             lowercase		,			lowercase			:str							=  noise_pred.split(latents.shape[1]				,    dim=1 )

           # compute the previous noisy sample x_t -> x_t-1
           lowercase			:Tuple							=  self.scheduler.step(
               _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    generator=_lowerCAmelCase				,    )[0]
         # post-processing
         lowercase			:Dict							=  self.movq.decode(_lowerCAmelCase				,    force_not_quantize=_lowerCAmelCase )["sample"]

         if output_type not in ["pt", "np", "pil"]:
           raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )

         if output_type in ["np", "pil"]:
           lowercase			:Any							=  image * 0.5 + 0.5
           lowercase			:Tuple							=  image.clamp(0				,    1 )
           lowercase			:List[str]							=  image.cpu().permute(0				,    2				,    3				,    1 ).float().numpy()

         if output_type == "pil":
           lowercase			:List[Any]							=  self.numpy_to_pil(_lowerCAmelCase )

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase )









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		:  Any	=   logging.get_logger(__name__)

_UpperCAmelCase		:  List[Any]	=   {"vocab_file": "sentencepiece.bpe.model"}

_UpperCAmelCase		:  List[str]	=   {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

_UpperCAmelCase		:  Any	=   {
    "camembert-base": 512,
}

_UpperCAmelCase		:  List[Any]	=   "▁"




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  VOCAB_FILES_NAMES
       _a           =  PRETRAINED_VOCAB_FILES_MAP
       _a           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a           =  ['''input_ids''', '''attention_mask''']






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

         lowercase			:Optional[int]							=  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_lowerCAmelCase				,    eos_token=_lowerCAmelCase				,    unk_token=_lowerCAmelCase				,    sep_token=_lowerCAmelCase				,    cls_token=_lowerCAmelCase				,    pad_token=_lowerCAmelCase				,    mask_token=_lowerCAmelCase				,    additional_special_tokens=_lowerCAmelCase				,    sp_model_kwargs=self.sp_model_kwargs				,    **_lowerCAmelCase				,    )
         lowercase			:Dict							=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(str(_lowerCAmelCase ) )
         lowercase			:Tuple							=  vocab_file
         # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
         # sentencepiece vocabulary (this is the case for <s> and </s>
         lowercase			:int							=  {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
         lowercase			:Tuple							=  len(self.fairseq_tokens_to_ids )
         lowercase			:int							=  len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
         lowercase			:Any							=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None ):

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase			:List[Any]							=  [self.cls_token_id]
         lowercase			:List[str]							=  [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None				,    _lowerCAmelCase:    bool = False ):
         if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_lowerCAmelCase				,    token_ids_a=_lowerCAmelCase				,    already_has_special_tokens=_lowerCAmelCase )

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






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    List[int]				,    _lowerCAmelCase:    Optional[List[int]] = None ):
         lowercase			:Any							=  [self.sep_token_id]
         lowercase			:str							=  [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:    Union[str, Any] ):
         return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:Any							=  {self.convert_ids_to_tokens(_lowerCAmelCase ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str ):
         return self.sp_model.encode(_lowerCAmelCase				,    out_type=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[str] ):
         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         elif self.sp_model.PieceToId(_lowerCAmelCase ) == 0:
           # Convert sentence piece unk token to fairseq unk token index
           return self.unk_token_id
         return self.fairseq_offset + self.sp_model.PieceToId(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    Tuple ):
         if index in self.fairseq_ids_to_tokens:
           return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    List[str] ):
         lowercase			:Tuple							=  []
         lowercase			:Any							=  ""
         lowercase			:str							=  False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             if not prev_is_special:
               out_string += " "
             out_string += self.sp_model.decode(_lowerCAmelCase ) + token
             lowercase			:Optional[int]							=  True
             lowercase			:Any							=  []
           else:
             current_sub_tokens.append(_lowerCAmelCase )
             lowercase			:str							=  False
         out_string += self.sp_model.decode(_lowerCAmelCase )
         return out_string.strip()






       def __getstate__(      self:    Dict ):
         lowercase			:int							=  self.__dict__.copy()
         lowercase			:List[str]							=  None
         return state






       def __setstate__(      self:    Optional[Any]				,    _lowerCAmelCase:    Dict ):
         lowercase			:int							=  d

         # for backward compatibility
         if not hasattr(self				,    "sp_model_kwargs" ):
           lowercase			:Optional[int]							=  {}

         lowercase			:List[str]							=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Optional[str] = None ):
         if not os.path.isdir(_lowerCAmelCase ):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
           return
         lowercase			:Any							=  os.path.join(
             _lowerCAmelCase				,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

         return (out_vocab_file,)



