






"""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,
)


_SCREAMING_SNAKE_CASE					:				Tuple					    =						logging.get_logger(__name__)  # pylint: disable=invalid-name

_SCREAMING_SNAKE_CASE					:				Dict					    =						'\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      lowerCamelCase__       (							_lowerCamelCase       :		int		,				_lowerCamelCase       :		Union[str, Any]		,				_lowerCamelCase       :		Optional[Any]=8 )      ->				Union[str, Any]:
  lowerCamelCase_         =						height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  lowerCamelCase_         =						width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor






class      a						(					__SCREAMING_SNAKE_CASE		):






    def __init__(       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							UNetaDConditionModel       ,						__SCREAMING_SNAKE_CASE     :							DDPMScheduler       ,						__SCREAMING_SNAKE_CASE     :							VQModel       ,						)     ->		Any:
      super().__init__()

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






    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]					)     ->		int:
      if latents is None:
        lowerCamelCase_         =						randn_tensor(__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						device=__SCREAMING_SNAKE_CASE       ,						dtype=__SCREAMING_SNAKE_CASE					)
      else:
        if latents.shape != shape:
          raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
        lowerCamelCase_         =						latents.to(__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						latents * scheduler.init_noise_sigma
      return latents






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=0					)     ->		List[str]:
      if is_accelerate_available():
        from accelerate import cpu_offload
      else:
        raise ImportError('Please install accelerate via `pip install accelerate`'					)

      lowerCamelCase_         =						torch.device(F'''cuda:{gpu_id}'''					)

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






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=0					)     ->		int:
      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.'					)

      lowerCamelCase_         =						torch.device(F'''cuda:{gpu_id}'''					)

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

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

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






    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		str:
      if not hasattr(self.unet       ,						'_hf_hook'					):
        return self.device
      for module in self.unet.modules():
        if (
            hasattr(__SCREAMING_SNAKE_CASE       ,						'_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(__SCREAMING_SNAKE_CASE					)
    def __call__(       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Union[torch.FloatTensor, List[torch.FloatTensor]]       ,						__SCREAMING_SNAKE_CASE     :							Union[torch.FloatTensor, List[torch.FloatTensor]]       ,						__SCREAMING_SNAKE_CASE     :							torch.FloatTensor       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 512       ,						__SCREAMING_SNAKE_CASE     :							int = 100       ,						__SCREAMING_SNAKE_CASE     :							float = 4.0       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						__SCREAMING_SNAKE_CASE     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[torch.FloatTensor] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = "pil"       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						)     ->		Dict:
      lowerCamelCase_         =						self._execution_device

      lowerCamelCase_         =						guidance_scale > 1.0

      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						torch.cat(__SCREAMING_SNAKE_CASE       ,						dim=0					)
      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						torch.cat(__SCREAMING_SNAKE_CASE       ,						dim=0					)
      if isinstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						torch.cat(__SCREAMING_SNAKE_CASE       ,						dim=0					)

      lowerCamelCase_         =						image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
        lowerCamelCase_         =						image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE       ,						dim=0					)
        lowerCamelCase_         =						negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE       ,						dim=0					)
        lowerCamelCase_         =						hint.repeat_interleave(__SCREAMING_SNAKE_CASE       ,						dim=0					)

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

      lowerCamelCase_         =						self.movq.config.latent_channels

      lowerCamelCase_     ,	lowerCamelCase_         =						downscale_height_and_width(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						self.movq_scale_factor					)

      # create initial latent
      lowerCamelCase_         =						self.prepare_latents(
          (batch_size, num_channels_latents, height, width)       ,						image_embeds.dtype       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						self.scheduler       ,						)

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

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

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

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

      if output_type == "pil":
        lowerCamelCase_         =						self.numpy_to_pil(__SCREAMING_SNAKE_CASE					)

      if not return_dict:
        return (image,)

      return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE					)






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
					import torch


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.get_logger(__name__)
SCREAMING_SNAKE_CASE							:List[str]							 =				{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE							:Dict							 =				{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				{
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}





class 							UpperCAmelCase      (  __SCREAMING_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					: Optional[int]							,A					: Optional[Any]							,A					: Optional[int]=False							,A					: int=False							,A					: Union[str, Any]=False							,A					: int=None							,A					: Optional[Any]=None							,A					: Union[str, Any]=None							,A					: Optional[Any]=None							,A					: Optional[Dict[str, Any]] = None							,**A					: Tuple							,):
					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					__A   			= kwargs.get("name_or_path" )
					if name_or_path is None:
									logger.warning(
									    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
									    " you are testing the model, this can safely be ignored" )
									__A   			= "None"

					# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
					__A   			= "<|endoftext|>" if eos_token is None else eos_token
					__A   			= "<unk>" if unk_token is None else unk_token
					if "gpt-sw3-7b" in name_or_path:
									__A   			= unk_token if pad_token is None else pad_token
									__A   			= eos_token if bos_token is None else bos_token
					else:
									__A   			= "<pad>" if pad_token is None else pad_token
									__A   			= "<s>" if bos_token is None else bos_token

					super().__init__(
					    do_lower_case=A							,remove_space=A							,keep_accents=A							,bos_token=A							,eos_token=A							,unk_token=A							,pad_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A   			= do_lower_case
					__A   			= remove_space
					__A   			= keep_accents
					__A   			= vocab_file

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(A )

					# Used for whitespace normalization in input texts
					# fmt : off
					__A   			= {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
					# fmt : on

					# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
					__A   			= re.compile(
					    f'''[{''.join(map(A							,list(range(0							,9 ) ) + list(range(11							,32 ) ) + list(range(1_27							,1_60 ) ) + [1_60, 1_73, 82_03] ) )}]''' )






	def __getstate__(       self					: Optional[int] ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Optional[Any]							,A					: Union[str, Any] ):
					__A   			= d

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

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )






	@property
	# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
	def 				UpperCamelCase_  (       self					: List[str] ):
					return len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int							,A					: str ):
					__A   			= self.non_printing_characters_re.sub(""							,A )

					# Normalize whitespaces
					__A   			= "".join([char if char not in self.whitespaces else " " for char in text] )

					# NFC Unicode normalization
					__A   			= unicodedata.normalize("NFC"							,A )
					return text






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: str							,**A					: Optional[int] ):
					__A   			= self.preprocess_text(A )
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: int ):
					return self.sp_model.IdToPiece(A )






	@staticmethod
	def 				UpperCamelCase_  (       A					: str ):
					return out_string






	def 				UpperCamelCase_  (       self					: str							,A					: List[str] ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
													if not prev_is_special:
																	out_string += " "

													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )

					return out_string






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: List[str]							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

					return (out_vocab_file,)






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Union[str, List[str]]							,A					: Union[str, bool] = False ):

					if isinstance(A							,A ):
									__A   			= self.preprocess_text(A )
									__A   			= self.sp_model.encode(A )
					else:
									__A   			= [self.preprocess_text(A ) for t in text]
									__A   			= self.sp_model.encode(A )

					if return_tensors is True or return_tensors == "pt":
									__A   			= torch.tensor(A )

					return token_ids






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Union[int, List[int]] ):
					return self.sp_model.decode(A )







	def 				UpperCamelCase_  (       self					: List[str]							,A					: "Conversation" ):
					__A   			= [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
					__A   			= (
					    f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(A ) + f'''{self.bos_token}Bot:'''
					)
					return self.encode(text=A )
