


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

SCREAMING_SNAKE_CASE							    =							'\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  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__=8   ):
   __a					=  height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   __a					=  width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       ):
        super().__init__()

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


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        if latents is None:
           __a					=  randn_tensor(__A  ,       generator=__A  ,       device=__A  ,       dtype=__A    )
        else:
           if latents.shape != shape:
              raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
           __a					=  latents.to(__A    )

        __a					=  latents * scheduler.init_noise_sigma
        return latents


     def 	snake_case_  (	self  ,       __A=0    ):
        if is_accelerate_available():
           from accelerate import cpu_offload
        else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

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

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


     def 	snake_case_  (	self  ,       __A=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."""    )

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

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

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

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


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 	snake_case_  (	self    ):
        if not hasattr(self.unet  ,       """_hf_hook"""    ):
           return self.device
        for module in self.unet.modules():
           if (
               hasattr(__A  ,       """_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(__A    )
     def __call__(	self  ,       __A  ,       __A  ,       __A = 512  ,       __A = 512  ,       __A = 100  ,       __A = 4.0  ,       __A = 1  ,       __A = None  ,       __A = None  ,       __A = "pil"  ,       __A = True  ,       ):
        __a					=  self._execution_device

        __a					=  guidance_scale > 1.0

        if isinstance(__A  ,       __A    ):
           __a					=  torch.cat(__A  ,       dim=0    )
        __a					=  image_embeds.shape[0] * num_images_per_prompt
        if isinstance(__A  ,       __A    ):
           __a					=  torch.cat(__A  ,       dim=0    )

        if do_classifier_free_guidance:
           __a					=  image_embeds.repeat_interleave(__A  ,       dim=0    )
           __a					=  negative_image_embeds.repeat_interleave(__A  ,       dim=0    )

        __a					=  torch.cat([negative_image_embeds, image_embeds]  ,       dim=0    ).to(dtype=self.unet.dtype  ,       device=__A    )

        self.scheduler.set_timesteps(__A  ,       device=__A    )
        __a					=  self.scheduler.timesteps

        __a					=  self.unet.config.in_channels

        __a	,    __a					=  downscale_height_and_width(__A  ,       __A  ,       self.movq_scale_factor    )

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

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

           __a					=  {"""image_embeds""": image_embeds}
           __a					=  self.unet(
               sample=__A  ,       timestep=__A  ,       encoder_hidden_states=__A  ,       added_cond_kwargs=__A  ,       return_dict=__A  ,       )[0]

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

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

        if output_type == "pil":
           __a					=  self.numpy_to_pil(__A    )

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=__A    )





'''simple docstring'''

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


snake_case									=    logging.get_logger(__name__)
snake_case									=    {"""vocab_file""": """spiece.model"""}

snake_case									=    {
    """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""",
    }
}

snake_case									=    {
    """AI-Sweden/gpt-sw3-126m""": 20_48,
    """AI-Sweden/gpt-sw3-350m""": 20_48,
    """AI-Sweden/gpt-sw3-1.6b""": 20_48,
    """AI-Sweden/gpt-sw3-6.7b""": 20_48,
    """AI-Sweden/gpt-sw3-20b""": 20_48,
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Dict	=			VOCAB_FILES_NAMES
 A_				:				Optional[int]	=			PRETRAINED_VOCAB_FILES_MAP
 A_				:				Optional[Any]	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A_				:				Union[str, Any]	=			["""input_ids""", """attention_mask"""]


 def __init__(    self  : List[str]			,							a__  : Any			,							a__  : Dict=False			,							a__  : List[Any]=False			,							a__  : Dict=False			,							a__  : Any=None			,							a__  : Tuple=None			,							a__  : str=None			,							a__  : Optional[int]=None			,							a__  : Optional[Dict[str, Any]] = None			,							**a__  : Union[str, Any]			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	{} if sp_model_kwargs is None else sp_model_kwargs

  lowerCAmelCase__     :   List[str]     		=	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"    )
   lowerCAmelCase__     :   Dict     		=	"None"

  # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
  lowerCAmelCase__     :   Tuple     		=	"<|endoftext|>" if eos_token is None else eos_token
  lowerCAmelCase__     :   List[str]     		=	"<unk>" if unk_token is None else unk_token
  if "gpt-sw3-7b" in name_or_path:
   lowerCAmelCase__     :   List[str]     		=	unk_token if pad_token is None else pad_token
   lowerCAmelCase__     :   Any     		=	eos_token if bos_token is None else bos_token
  else:
   lowerCAmelCase__     :   str     		=	"<pad>" if pad_token is None else pad_token
   lowerCAmelCase__     :   Union[str, Any]     		=	"<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__			,							)

  lowerCAmelCase__     :   Dict     		=	do_lower_case
  lowerCAmelCase__     :   Union[str, Any]     		=	remove_space
  lowerCAmelCase__     :   Any     		=	keep_accents
  lowerCAmelCase__     :   List[str]     		=	vocab_file

  lowerCAmelCase__     :   Dict     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(a__    )

  # Used for whitespace normalization in input texts
  # fmt : off
  lowerCAmelCase__     :   List[str]     		=	{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
  # fmt : on

  # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
  lowerCAmelCase__     :   Dict     		=	re.compile(
      F'''[{''.join(map(a__			,							list(range(0			,							9    )    ) + list(range(11			,							32    )    ) + list(range(127			,							160    )    ) + [160, 173, 8203]    )    )}]'''    )


 def __getstate__(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.__dict__.copy()
  lowerCAmelCase__     :   List[str]     		=	None
  return state


 def __setstate__(    self  : List[Any]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	d

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

  lowerCAmelCase__     :   str     		=	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    _A	(    self  : str    ):
  '''simple docstring'''

  return len(self.sp_model    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.non_printing_characters_re.sub(""			,							a__    )

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

  # NFC Unicode normalization
  lowerCAmelCase__     :   Tuple     		=	unicodedata.normalize("NFC"			,							a__    )
  return text


 def    _A	(    self  : str			,							a__  : str			,							**a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.preprocess_text(a__    )
  return self.sp_model.encode(a__			,							out_type=a__    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  return self.sp_model.PieceToId(a__    )


 def    _A	(    self  : Optional[int]			,							a__  : int    ):
  '''simple docstring'''

  return self.sp_model.IdToPiece(a__    )


 @staticmethod
 def    _A	(    a__  : str    ):
  '''simple docstring'''

  return out_string


 def    _A	(    self  : int			,							a__  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	[]
  lowerCAmelCase__     :   Tuple     		=	""
  lowerCAmelCase__     :   str     		=	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
    lowerCAmelCase__     :   Union[str, Any]     		=	True
    lowerCAmelCase__     :   Dict     		=	[]
   else:
    current_sub_tokens.append(a__    )
    lowerCAmelCase__     :   Dict     		=	False
  out_string += self.sp_model.decode(a__    )

  return out_string


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	{self.convert_ids_to_tokens(a__    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def    _A	(    self  : str			,							a__  : str			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

  if not os.path.isdir(a__    ):
   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
   return
  lowerCAmelCase__     :   List[str]     		=	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:
    lowerCAmelCase__     :   int     		=	self.sp_model.serialized_model_proto()
    fi.write(a__    )

  return (out_vocab_file,)


 def    _A	(    self  : List[Any]			,							a__  : Union[str, List[str]]			,							a__  : Union[str, bool] = False    ):
  '''simple docstring'''

  if isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   int     		=	self.preprocess_text(a__    )
   lowerCAmelCase__     :   Dict     		=	self.sp_model.encode(a__    )
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	[self.preprocess_text(a__    ) for t in text]
   lowerCAmelCase__     :   Optional[int]     		=	self.sp_model.encode(a__    )

  if return_tensors is True or return_tensors == "pt":
   lowerCAmelCase__     :   Union[str, Any]     		=	torch.tensor(a__    )

  return token_ids


 def    _A	(    self  : Dict			,							a__  : Union[int, List[int]]    ):
  '''simple docstring'''

  return self.sp_model.decode(a__    )


 def    _A	(    self  : int			,							a__  : "Conversation"    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
  lowerCAmelCase__     :   List[str]     		=	(
      F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(a__    ) + F'''{self.bos_token}Bot:'''
  )
  return self.encode(text=a__    )





