
'''simple docstring'''


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,
)


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

snake_case_ :  int     =					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       Dict=8):
   UpperCamelCase       =  height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   UpperCamelCase       =  width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







      super().__init__()

      self.register_modules(
          unet=lowerCamelCase__ ,   scheduler=lowerCamelCase__ ,   movq=lowerCamelCase__ ,   )
      UpperCamelCase       =  2 ** (len(self.movq.config.block_out_channels   ) - 1)







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      if latents is None:
         UpperCamelCase       =  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}'   )
         UpperCamelCase       =  latents.to(lowerCamelCase__   )

      UpperCamelCase       =  latents * scheduler.init_noise_sigma
      return latents







   def       UpperCAmelCase				(			self ,   lowerCamelCase__=0   ):

      '''simple docstring'''







      if is_accelerate_available():
         from accelerate import cpu_offload
      else:
         raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

      UpperCamelCase       =  torch.device(f'cuda:{gpu_id}'   )

      UpperCamelCase       =  [
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
            cpu_offload(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__=0   ):

      '''simple docstring'''







      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.'''   )

      UpperCamelCase       =  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)

      UpperCamelCase       =  None
      for cpu_offloaded_model in [self.unet, self.movq]:
         UpperCamelCase     , UpperCamelCase       =  cpu_offload_with_hook(lowerCamelCase__ ,   lowerCamelCase__ ,   prev_module_hook=lowerCamelCase__   )

      # We'll offload the last model manually.
      UpperCamelCase       =  hook







   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      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 ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = 5_1_2 ,   lowerCamelCase__ = 5_1_2 ,   lowerCamelCase__ = 1_0_0 ,   lowerCamelCase__ = 4.0 ,   lowerCamelCase__ = 1 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "pil" ,   lowerCamelCase__ = True ,   ):

      '''simple docstring'''







      UpperCamelCase       =  self._execution_device

      UpperCamelCase       =  guidance_scale > 1.0

      if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  torch.cat(lowerCamelCase__ ,   dim=0   )
      if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  torch.cat(lowerCamelCase__ ,   dim=0   )
      if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  torch.cat(lowerCamelCase__ ,   dim=0   )

      UpperCamelCase       =  image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
         UpperCamelCase       =  image_embeds.repeat_interleave(lowerCamelCase__ ,   dim=0   )
         UpperCamelCase       =  negative_image_embeds.repeat_interleave(lowerCamelCase__ ,   dim=0   )
         UpperCamelCase       =  hint.repeat_interleave(lowerCamelCase__ ,   dim=0   )

      UpperCamelCase       =  torch.cat([negative_image_embeds, image_embeds] ,   dim=0   ).to(dtype=self.unet.dtype ,   device=lowerCamelCase__   )
      UpperCamelCase       =  torch.cat([hint, hint] ,   dim=0   ).to(dtype=self.unet.dtype ,   device=lowerCamelCase__   )
      self.scheduler.set_timesteps(lowerCamelCase__ ,   device=lowerCamelCase__   )
      UpperCamelCase       =  self.scheduler.timesteps

      UpperCamelCase       =  self.movq.config.latent_channels

      UpperCamelCase     , UpperCamelCase       =  downscale_height_and_width(lowerCamelCase__ ,   lowerCamelCase__ ,   self.movq_scale_factor   )

      # create initial latent
      UpperCamelCase       =  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
         UpperCamelCase       =  torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

         UpperCamelCase       =  {'''image_embeds''': image_embeds, '''hint''': hint}
         UpperCamelCase       =  self.unet(
             sample=lowerCamelCase__ ,   timestep=lowerCamelCase__ ,   encoder_hidden_states=lowerCamelCase__ ,   added_cond_kwargs=lowerCamelCase__ ,   return_dict=lowerCamelCase__ ,   )[0]

         if do_classifier_free_guidance:
            UpperCamelCase     , UpperCamelCase       =  noise_pred.split(latents.shape[1] ,   dim=1   )
            UpperCamelCase     , UpperCamelCase       =  noise_pred.chunk(2   )
            UpperCamelCase     , UpperCamelCase       =  variance_pred.chunk(2   )
            UpperCamelCase       =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            UpperCamelCase       =  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"]
         ):
            UpperCamelCase     , UpperCamelCase       =  noise_pred.split(latents.shape[1] ,   dim=1   )

         # compute the previous noisy sample x_t -> x_t-1
         UpperCamelCase       =  self.scheduler.step(
             lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   generator=lowerCamelCase__ ,   )[0]
      # post-processing
      UpperCamelCase       =  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"]:
         UpperCamelCase       =  image * 0.5 + 0.5
         UpperCamelCase       =  image.clamp(0 ,   1   )
         UpperCamelCase       =  image.cpu().permute(0 ,   2 ,   3 ,   1   ).float().numpy()

      if output_type == "pil":
         UpperCamelCase       =  self.numpy_to_pil(lowerCamelCase__   )

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=lowerCamelCase__   )




'''simple docstring'''


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


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

snake_case_ :  int     =					'▁'

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

snake_case_ :  Optional[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',
    },
}

snake_case_ :  Dict     =					{'vinai/bartpho-syllable': 1_024}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							['''input_ids''', '''attention_mask''']







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="</s>" ,   lowerCamelCase__="<s>" ,   lowerCamelCase__="<unk>" ,   lowerCamelCase__="<pad>" ,   lowerCamelCase__="<mask>" ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  AddedToken(lowerCamelCase__ ,   lstrip=lowerCamelCase__ ,   rstrip=lowerCamelCase__   ) if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ) else mask_token

      UpperCamelCase       =  {} 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__ ,   sp_model_kwargs=self.sp_model_kwargs ,   **lowerCamelCase__ ,   )

      UpperCamelCase       =  vocab_file
      UpperCamelCase       =  monolingual_vocab_file
      UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(lowerCamelCase__   )   )

      # 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(lowerCamelCase__   ) not in self.fairseq_tokens_to_ids:
            UpperCamelCase       =  cnt
            cnt += 1
      with open(lowerCamelCase__ ,   '''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(lowerCamelCase__   ) 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   ):

      '''simple docstring'''







      UpperCamelCase       =  self.__dict__.copy()
      UpperCamelCase       =  None
      UpperCamelCase       =  self.sp_model.serialized_model_proto()
      return state







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return len(self.fairseq_ids_to_tokens   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {self.convert_ids_to_tokens(lowerCamelCase__   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.encode(lowerCamelCase__ ,   out_type=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.fairseq_ids_to_tokens[index]







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  ''''''.join(lowerCamelCase__   ).replace(lowerCamelCase__ ,   ''' '''   ).strip()
      return out_string







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







      if not os.path.isdir(lowerCamelCase__   ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
         return
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
      UpperCamelCase       =  os.path.join(
          lowerCamelCase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_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:
            UpperCamelCase       =  self.sp_model.serialized_model_proto()
            fi.write(lowerCamelCase__   )

      if os.path.abspath(self.monolingual_vocab_file   ) != os.path.abspath(
          lowerCamelCase__   ) and os.path.isfile(self.monolingual_vocab_file   ):
         copyfile(self.monolingual_vocab_file ,   lowerCamelCase__   )
      elif not os.path.isfile(self.monolingual_vocab_file   ):
         with open(lowerCamelCase__ ,   '''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(lowerCamelCase__   )} \n'   )

      return out_vocab_file, out_monolingual_vocab_file





