






"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

A__					: List[str]=	"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=8					)					->	Optional[int]:
    """simple docstring"""

    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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=5_12 ,	SCREAMING_SNAKE_CASE=5_12					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    pil_image.resize((w, h) ,	resample=Image.BICUBIC ,	reducing_gap=1					)
    UpperCamelCase__         =    np.array(pil_image.convert('RGB'					)					)
    UpperCamelCase__         =    arr.astype(np.floataa					) / 127.5 - 1
    UpperCamelCase__         =    np.transpose(SCREAMING_SNAKE_CASE ,	[2, 0, 1]					)
    UpperCamelCase__         =    torch.from_numpy(SCREAMING_SNAKE_CASE					).unsqueeze(0					)
    return image





class    __lowerCamelCase    (  _a  ):

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				)		->       Union[str, Any]:
       super().__init__()

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       Union[str, Any]:
       # get the original timestep using init_timestep
       UpperCamelCase__         =    min(int(num_inference_steps * strength      ) ,				snake_case_      )

       UpperCamelCase__         =    max(num_inference_steps - init_timestep ,				0      )
       UpperCamelCase__         =    self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_=None      )		->       str:
       if not isinstance(snake_case_ ,				(torch.Tensor, PIL.Image.Image, list)      ):
           raise ValueError(
               F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case_      )}'      )

       UpperCamelCase__         =    image.to(device=snake_case_ ,				dtype=snake_case_      )

       UpperCamelCase__         =    batch_size * num_images_per_prompt

       if image.shape[1] == 4:
           UpperCamelCase__         =    image

       else:
           if isinstance(snake_case_ ,				snake_case_      ) and len(snake_case_      ) != batch_size:
               raise ValueError(
                   F'You have passed a list of generators of length {len(snake_case_      )}, but requested an effective batch'
                   F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

           elif isinstance(snake_case_ ,				snake_case_      ):
               UpperCamelCase__         =    [
                   self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(snake_case_      )
               ]
               UpperCamelCase__         =    torch.cat(snake_case_ ,				dim=0      )
           else:
               UpperCamelCase__         =    self.movq.encode(snake_case_      ).latent_dist.sample(snake_case_      )

           UpperCamelCase__         =    self.movq.config.scaling_factor * init_latents

       UpperCamelCase__         =    torch.cat([init_latents] ,				dim=0      )

       UpperCamelCase__         =    init_latents.shape
       UpperCamelCase__         =    randn_tensor(snake_case_ ,				generator=snake_case_ ,				device=snake_case_ ,				dtype=snake_case_      )

       # get latents
       UpperCamelCase__         =    self.scheduler.add_noise(snake_case_ ,				snake_case_ ,				snake_case_      )

       UpperCamelCase__         =    init_latents

       return latents

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=0      )		->       Tuple:
       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(snake_case_ ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=0      )		->       List[str]:
       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=snake_case_      )
           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(snake_case_ ,				snake_case_ ,				prev_module_hook=snake_case_      )

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

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       if not hasattr(self.unet ,				'_hf_hook'      ):
           return self.device
       for module in self.unet.modules():
           if (
               hasattr(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(snake_case_      )
   def __call__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ = 512 ,				snake_case_ = 512 ,				snake_case_ = 100 ,				snake_case_ = 4.0 ,				snake_case_ = 0.3 ,				snake_case_ = 1 ,				snake_case_ = None ,				snake_case_ = "pil" ,				snake_case_ = True ,				)		->       List[str]:
       UpperCamelCase__         =    self._execution_device

       UpperCamelCase__         =    guidance_scale > 1.0

       if isinstance(snake_case_ ,				snake_case_      ):
           UpperCamelCase__         =    torch.cat(snake_case_ ,				dim=0      )
       UpperCamelCase__         =    image_embeds.shape[0]
       if isinstance(snake_case_ ,				snake_case_      ):
           UpperCamelCase__         =    torch.cat(snake_case_ ,				dim=0      )

       if do_classifier_free_guidance:
           UpperCamelCase__         =    image_embeds.repeat_interleave(snake_case_ ,				dim=0      )
           UpperCamelCase__         =    negative_image_embeds.repeat_interleave(snake_case_ ,				dim=0      )

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

       if not isinstance(snake_case_ ,				snake_case_      ):
           UpperCamelCase__         =    [image]
       if not all(isinstance(snake_case_ ,				(PIL.Image.Image, torch.Tensor)      ) for i in image      ):
           raise ValueError(
               F'Input is in incorrect format: {[type(snake_case_      ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'      )

       UpperCamelCase__         =    torch.cat([prepare_image(snake_case_ ,				snake_case_ ,				snake_case_      ) for i in image] ,				dim=0      )
       UpperCamelCase__         =    image.to(dtype=image_embeds.dtype ,				device=snake_case_      )

       UpperCamelCase__         =    self.movq.encode(snake_case_      )['latents']
       UpperCamelCase__         =    latents.repeat_interleave(snake_case_ ,				dim=0      )
       self.scheduler.set_timesteps(snake_case_ ,				device=snake_case_      )
       UpperCamelCase__       ,						UpperCamelCase__         =    self.get_timesteps(snake_case_ ,				snake_case_ ,				snake_case_      )
       UpperCamelCase__         =    timesteps[:1].repeat(batch_size * num_images_per_prompt      )
       UpperCamelCase__       ,						UpperCamelCase__         =    downscale_height_and_width(snake_case_ ,				snake_case_ ,				self.movq_scale_factor      )
       UpperCamelCase__         =    self.prepare_latents(
           snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				image_embeds.dtype ,				snake_case_ ,				snake_case_      )
       for i, t in enumerate(self.progress_bar(snake_case_      )      ):
           # 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}
           UpperCamelCase__         =    self.unet(
               sample=snake_case_ ,				timestep=snake_case_ ,				encoder_hidden_states=snake_case_ ,				added_cond_kwargs=snake_case_ ,				return_dict=snake_case_ ,				)[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(
               snake_case_ ,				snake_case_ ,				snake_case_ ,				generator=snake_case_ ,				)[0]

       # post-processing
       UpperCamelCase__         =    self.movq.decode(snake_case_ ,				force_not_quantize=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"]:
           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(snake_case_      )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=snake_case_      )












"""simple docstring"""





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
  from ...tokenization_utils_base import TextInput
from ...utils import logging


A__					: Dict=	logging.get_logger(__name__)

A__					: str=	{"""vocab_file""": """spiece.model"""}

A__					: Union[str, Any]=	{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A__					: Union[str, Any]=	{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

A__					: Optional[Any]=	"""▁"""
class    __lowerCamelCase    (  _a  ):
   a      :				Dict 						=VOCAB_FILES_NAMES
   a      :				str 						=PRETRAINED_VOCAB_FILES_MAP
   a      :				Union[str, Any] 						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a      :				List[str] 						=["""input_ids""", """attention_mask"""]

   def __init__(					self ,				snake_case_ ,				snake_case_="</s>" ,				snake_case_="<unk>" ,				snake_case_="<pad>" ,				snake_case_=100 ,				snake_case_=None ,				snake_case_ = None ,				snake_case_=True ,				**snake_case_ ,				)		->       None:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase__         =    [F'<extra_id_{i}>' for i in range(snake_case_      )]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           UpperCamelCase__         =    len(set(filter(lambda snake_case_      : bool('extra_id' in str(snake_case_      )      ) ,				snake_case_      )      )      )
           if extra_tokens != extra_ids:
               raise ValueError(
                   F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                   ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                   ' tokens'      )
       if legacy:
           logger.warning_once(
               F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'      )

       UpperCamelCase__         =    legacy
       UpperCamelCase__         =    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=snake_case_ ,				unk_token=snake_case_ ,				pad_token=snake_case_ ,				extra_ids=snake_case_ ,				additional_special_tokens=snake_case_ ,				sp_model_kwargs=self.sp_model_kwargs ,				legacy=snake_case_ ,				**snake_case_ ,				)

       UpperCamelCase__         =    vocab_file
       UpperCamelCase__         =    extra_ids

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(snake_case_      )

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           UpperCamelCase__         =    TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
               return init_max_model_length
           elif init_max_model_length is None:
               warnings.warn(
                   'This tokenizer was incorrectly instantiated with a model max length of'
                   F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                   ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                   ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                   F' {pretrained_model_name_or_path} automatically truncating your input to'
                   F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                   F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                   ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                   ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,				snake_case_ ,				)

       return max_model_length

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       return self.sp_model.get_piece_size() + self._extra_ids

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    {self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = False      )		->       List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case_ ,				token_ids_a=snake_case_ ,				already_has_special_tokens=snake_case_      )

       # normal case: some special tokens
       if token_ids_a is None:
           return ([0] * len(snake_case_      )) + [1]
       return ([0] * len(snake_case_      )) + [1] + ([0] * len(snake_case_      )) + [1]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return list(
           set(filter(lambda snake_case_      : bool(re.search(r'<extra_id_\d+>' ,				snake_case_      )      ) is not None ,				self.additional_special_tokens      )      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return [self._convert_token_to_id(snake_case_      ) for token in self.get_sentinel_tokens()]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[int]:
       if len(snake_case_      ) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
               ' eos tokens being added.'      )
           return token_ids
       else:
           return token_ids + [self.eos_token_id]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    [self.eos_token_id]

       if token_ids_a is None:
           return len(token_ids_a + eos      ) * [0]
       return len(token_ids_a + eos + token_ids_a + eos      ) * [0]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
       if token_ids_a is None:
           return token_ids_a
       else:
           UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
           return token_ids_a + token_ids_a

   def __getstate__(					self      )		->       str:
       UpperCamelCase__         =    self.__dict__.copy()
       UpperCamelCase__         =    None
       return state

   def __setstate__(					self ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    d

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

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[str]:
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
           UpperCamelCase__         =    SPIECE_UNDERLINE + text.replace(snake_case_ ,				' '      )
       return super().tokenize(snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[Any]:
       if not self.legacy:
           UpperCamelCase__         =    text.startswith(snake_case_      )
           if is_first:
               UpperCamelCase__         =    text[1:]

       UpperCamelCase__         =    self.sp_model.encode(snake_case_ ,				out_type=snake_case_      )

       if not self.legacy and not is_first and not text.startswith(' '      ) and tokens[0].startswith(snake_case_      ):
           UpperCamelCase__         =    ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
       return tokens

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       if token.startswith('<extra_id_'      ):
           UpperCamelCase__         =    re.match(r'<extra_id_(\d+)>' ,				snake_case_      )
           UpperCamelCase__         =    int(match.group(1      )      )
           return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       if index < self.sp_model.get_piece_size():
           UpperCamelCase__         =    self.sp_model.IdToPiece(snake_case_      )
       else:
           UpperCamelCase__         =    F'<extra_id_{self.vocab_size - 1 - index}>'
       return token

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    ''
       UpperCamelCase__         =    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(snake_case_      ) + token
               UpperCamelCase__         =    True
               UpperCamelCase__         =    []
           else:
               current_sub_tokens.append(snake_case_      )
               UpperCamelCase__         =    False
       out_string += self.sp_model.decode(snake_case_      )
       return out_string.strip()







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       Tuple[str]:
       if not os.path.isdir(snake_case_      ):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
           return
       UpperCamelCase__         =    os.path.join(
           snake_case_ ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

       return (out_vocab_file,)




