


'''simple docstring'''
import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)


def       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Any      ):
 if isinstance(UpperCamelCase__,					torch.Tensor      ):
  return image
 elif isinstance(UpperCamelCase__,					PIL.Image.Image      ):
  __A	:		List[Any]	  =      [image]

 if isinstance(image[0],					PIL.Image.Image      ):
  __A	:		Dict	  =      [np.array(i.resize((w, h),					resample=PIL_INTERPOLATION['lanczos']      )      )[None, :] for i in image]
  __A	:		List[Any]	  =      np.concatenate(UpperCamelCase__,					axis=0      )
  __A	:		Tuple	  =      np.array(UpperCamelCase__      ).astype(np.floataa      ) / 255.0
  __A	:		int	  =      image.transpose(0,					3,					1,					2      )
  __A	:		List[str]	  =      2.0 * image - 1.0
  __A	:		str	  =      torch.from_numpy(UpperCamelCase__      )
 elif isinstance(image[0],					torch.Tensor      ):
  __A	:		Union[str, Any]	  =      torch.cat(UpperCamelCase__,					dim=0      )
 return image


def       _lowercase							(					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       int,					UpperCamelCase__							:       Any,					UpperCamelCase__							:       List[Any]=0.9995      ):
 if not isinstance(UpperCamelCase__,					np.ndarray      ):
  __A	:		str	  =      True
  __A	:		str	  =      va.device
  __A	:		Tuple	  =      va.cpu().numpy()
  __A	:		Optional[Any]	  =      va.cpu().numpy()

 __A	:		Optional[int]	  =      np.sum(va * va / (np.linalg.norm(UpperCamelCase__      ) * np.linalg.norm(UpperCamelCase__      ))      )
 if np.abs(UpperCamelCase__      ) > DOT_THRESHOLD:
  __A	:		Union[str, Any]	  =      (1 - t) * va + t * va
 else:
  __A	:		Dict	  =      np.arccos(UpperCamelCase__      )
  __A	:		int	  =      np.sin(UpperCamelCase__      )
  __A	:		Dict	  =      theta_a * t
  __A	:		Dict	  =      np.sin(UpperCamelCase__      )
  __A	:		Optional[int]	  =      np.sin(theta_a - theta_t      ) / sin_theta_a
  __A	:		Any	  =      sin_theta_t / sin_theta_a
  __A	:		Union[str, Any]	  =      sa * va + sa * va

 if inputs_are_torch:
  __A	:		Optional[Any]	  =      torch.from_numpy(UpperCamelCase__      ).to(UpperCamelCase__      )

 return va


def       _lowercase							(					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       List[Any]      ):
 __A	:		Tuple	  =      F.normalize(UpperCamelCase__,					dim=-1      )
 __A	:		Dict	  =      F.normalize(UpperCamelCase__,					dim=-1      )
 return (x - y).norm(dim=-1      ).div(2      ).arcsin().pow(2      ).mul(2      )


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       Dict      ):
 for param in model.parameters():
  __A	:		List[Any]	  =      value


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   ):

  """simple docstring"""







  super().__init__()
  self.register_modules(
      vae=__lowercase   ,   text_encoder=__lowercase   ,   clip_model=__lowercase   ,   tokenizer=__lowercase   ,   unet=__lowercase   ,   scheduler=__lowercase   ,   feature_extractor=__lowercase   ,   coca_model=__lowercase   ,   coca_tokenizer=__lowercase   ,   coca_transform=__lowercase   ,   )
  __A	:		Dict	  =      (
      feature_extractor.size
      if isinstance(feature_extractor.size   ,   __lowercase    )
      else feature_extractor.size['shortest_edge']
  )
  __A	:		Optional[int]	  =      transforms.Normalize(mean=feature_extractor.image_mean   ,   std=feature_extractor.image_std    )
  set_requires_grad(self.text_encoder   ,   __lowercase    )
  set_requires_grad(self.clip_model   ,   __lowercase    )


 def 			snake_case__ (   self   ,   __lowercase = "auto"    ):

  """simple docstring"""







  if slice_size == "auto":
   # half the attention head size is usually a good trade-off between
   # speed and memory
   __A	:		Dict	  =      self.unet.config.attention_head_dim // 2
  self.unet.set_attention_slice(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self.enable_attention_slicing(__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  set_requires_grad(self.vae   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  set_requires_grad(self.vae   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  set_requires_grad(self.unet   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  set_requires_grad(self.unet   ,   __lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		str	  =      min(int(num_inference_steps * strength    )   ,   __lowercase    )

  __A	:		int	  =      max(num_inference_steps - init_timestep   ,   0    )
  __A	:		List[Any]	  =      self.scheduler.timesteps[t_start:]

  return timesteps, num_inference_steps - t_start


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  if not isinstance(__lowercase   ,   torch.Tensor    ):
   raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(__lowercase    )}"""    )

  __A	:		int	  =      image.to(device=__lowercase   ,   dtype=__lowercase    )

  if isinstance(__lowercase   ,   __lowercase    ):
   __A	:		List[str]	  =      [
       self.vae.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(__lowercase    )
   ]
   __A	:		Union[str, Any]	  =      torch.cat(__lowercase   ,   dim=0    )
  else:
   __A	:		str	  =      self.vae.encode(__lowercase    ).latent_dist.sample(__lowercase    )

  # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
  __A	:		List[str]	  =      0.1_8_2_1_5 * init_latents
  __A	:		Optional[int]	  =      init_latents.repeat_interleave(__lowercase   ,   dim=0    )

  __A	:		List[str]	  =      randn_tensor(init_latents.shape   ,   generator=__lowercase   ,   device=__lowercase   ,   dtype=__lowercase    )

  # get latents
  __A	:		Optional[int]	  =      self.scheduler.add_noise(__lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		Any	  =      init_latents

  return latents


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      self.coca_transform(__lowercase    ).unsqueeze(0    )
  with torch.no_grad(), torch.cuda.amp.autocast():
   __A	:		List[str]	  =      self.coca_model.generate(transformed_image.to(device=self.device   ,   dtype=self.coca_model.dtype    )    )
  __A	:		List[Any]	  =      self.coca_tokenizer.decode(generated[0].cpu().numpy()    )
  return generated.split('<end_of_text>'    )[0].replace('<start_of_text>'   ,   ''    ).rstrip(' .,'    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      self.feature_extractor.preprocess(__lowercase    )
  __A	:		Union[str, Any]	  =      torch.from_numpy(clip_image_input['pixel_values'][0]    ).unsqueeze(0    ).to(self.device    ).half()
  __A	:		Any	  =      self.clip_model.get_image_features(__lowercase    )
  __A	:		Any	  =      image_embeddings_clip / image_embeddings_clip.norm(p=2   ,   dim=-1   ,   keepdim=__lowercase    )
  __A	:		List[Any]	  =      image_embeddings_clip.repeat_interleave(__lowercase   ,   dim=0    )
  return image_embeddings_clip


 @torch.enable_grad()
 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      latents.detach().requires_grad_()

  __A	:		List[str]	  =      self.scheduler.scale_model_input(__lowercase   ,   __lowercase    )

  # predict the noise residual
  __A	:		int	  =      self.unet(__lowercase   ,   __lowercase   ,   encoder_hidden_states=__lowercase    ).sample

  if isinstance(self.scheduler   ,   (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)    ):
   __A	:		List[Any]	  =      self.scheduler.alphas_cumprod[timestep]
   __A	:		Optional[Any]	  =      1 - alpha_prod_t
   # compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   __A	:		Union[str, Any]	  =      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

   __A	:		Any	  =      torch.sqrt(__lowercase    )
   __A	:		Union[str, Any]	  =      pred_original_sample * (fac) + latents * (1 - fac)
  elif isinstance(self.scheduler   ,   __lowercase    ):
   __A	:		List[Any]	  =      self.scheduler.sigmas[index]
   __A	:		Optional[Any]	  =      latents - sigma * noise_pred
  else:
   raise ValueError(F"""scheduler type {type(self.scheduler    )} not supported"""    )

  # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
  __A	:		List[str]	  =      1 / 0.1_8_2_1_5 * sample
  __A	:		Dict	  =      self.vae.decode(__lowercase    ).sample
  __A	:		str	  =      (image / 2 + 0.5).clamp(0   ,   1    )

  __A	:		Optional[int]	  =      transforms.Resize(self.feature_extractor_size    )(__lowercase    )
  __A	:		Optional[Any]	  =      self.normalize(__lowercase    ).to(latents.dtype    )

  __A	:		Optional[Any]	  =      self.clip_model.get_image_features(__lowercase    )
  __A	:		List[str]	  =      image_embeddings_clip / image_embeddings_clip.norm(p=2   ,   dim=-1   ,   keepdim=__lowercase    )

  __A	:		Tuple	  =      spherical_dist_loss(__lowercase   ,   __lowercase    ).mean() * clip_guidance_scale

  __A	:		Optional[Any]	  =      -torch.autograd.grad(__lowercase   ,   __lowercase    )[0]

  if isinstance(self.scheduler   ,   __lowercase    ):
   __A	:		List[str]	  =      latents.detach() + grads * (sigma**2)
   __A	:		Optional[Any]	  =      noise_pred_original
  else:
   __A	:		str	  =      noise_pred_original - torch.sqrt(__lowercase    ) * grads
  return noise_pred, latents







 @torch.no_grad()
 def __call__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = 512   ,   __lowercase = 512   ,   __lowercase = 0.6   ,   __lowercase = 50   ,   __lowercase = 7.5   ,   __lowercase = 1   ,   __lowercase = 0.0   ,   __lowercase = 100   ,   __lowercase = None   ,   __lowercase = "pil"   ,   __lowercase = True   ,   __lowercase = 0.8   ,   __lowercase = 0.1   ,   __lowercase = 0.1   ,   ):

  """simple docstring"""







  if isinstance(__lowercase   ,   __lowercase    ) and len(__lowercase    ) != batch_size:
   raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(__lowercase    )} generators."""    )

  if height % 8 != 0 or width % 8 != 0:
   raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""    )

  if isinstance(__lowercase   ,   torch.Generator    ) and batch_size > 1:
   __A	:		Union[str, Any]	  =      [generator] + [None] * (batch_size - 1)

  __A	:		Dict	  =      [
      ('model', self.coca_model is None),
      ('tokenizer', self.coca_tokenizer is None),
      ('transform', self.coca_transform is None),
  ]
  __A	:		int	  =      [x[0] for x in coca_is_none if x[1]]
  __A	:		Tuple	  =      ', '.join(__lowercase    )
  # generate prompts with coca model if prompt is None
  if content_prompt is None:
   if len(__lowercase    ):
    raise ValueError(
        F"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
        F"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""    )
   __A	:		List[Any]	  =      self.get_image_description(__lowercase    )
  if style_prompt is None:
   if len(__lowercase    ):
    raise ValueError(
        F"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
        F""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""    )
   __A	:		int	  =      self.get_image_description(__lowercase    )

  # get prompt text embeddings for content and style
  __A	:		Optional[int]	  =      self.tokenizer(
      __lowercase   ,   padding='max_length'   ,   max_length=self.tokenizer.model_max_length   ,   truncation=__lowercase   ,   return_tensors='pt'   ,   )
  __A	:		List[Any]	  =      self.text_encoder(content_text_input.input_ids.to(self.device    )    )[0]

  __A	:		List[Any]	  =      self.tokenizer(
      __lowercase   ,   padding='max_length'   ,   max_length=self.tokenizer.model_max_length   ,   truncation=__lowercase   ,   return_tensors='pt'   ,   )
  __A	:		Tuple	  =      self.text_encoder(style_text_input.input_ids.to(self.device    )    )[0]

  __A	:		Dict	  =      slerp(__lowercase   ,   __lowercase   ,   __lowercase    )

  # duplicate text embeddings for each generation per prompt
  __A	:		Optional[Any]	  =      text_embeddings.repeat_interleave(__lowercase   ,   dim=0    )

  # set timesteps
  __A	:		List[Any]	  =      'offset' in set(inspect.signature(self.scheduler.set_timesteps    ).parameters.keys()    )
  __A	:		Optional[int]	  =      {}
  if accepts_offset:
   __A	:		Union[str, Any]	  =      1

  self.scheduler.set_timesteps(__lowercase   ,   **__lowercase    )
  # Some schedulers like PNDM have timesteps as arrays
  # It's more optimized to move all timesteps to correct device beforehand
  self.scheduler.timesteps.to(self.device    )

  __A  ,__A	:		int	  =      self.get_timesteps(__lowercase   ,   __lowercase   ,   self.device    )
  __A	:		Tuple	  =      timesteps[:1].repeat(__lowercase    )

  # Preprocess image
  __A	:		Union[str, Any]	  =      preprocess(__lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		Optional[int]	  =      self.prepare_latents(
      __lowercase   ,   __lowercase   ,   __lowercase   ,   text_embeddings.dtype   ,   self.device   ,   __lowercase    )

  __A	:		str	  =      preprocess(__lowercase   ,   __lowercase   ,   __lowercase    )
  __A	:		Dict	  =      self.prepare_latents(
      __lowercase   ,   __lowercase   ,   __lowercase   ,   text_embeddings.dtype   ,   self.device   ,   __lowercase    )

  __A	:		Dict	  =      slerp(__lowercase   ,   __lowercase   ,   __lowercase    )

  if clip_guidance_scale > 0:
   __A	:		Optional[Any]	  =      self.get_clip_image_embeddings(__lowercase   ,   __lowercase    )
   __A	:		Optional[Any]	  =      self.get_clip_image_embeddings(__lowercase   ,   __lowercase    )
   __A	:		Union[str, Any]	  =      slerp(
       __lowercase   ,   __lowercase   ,   __lowercase    )

  # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
  # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
  # corresponds to doing no classifier free guidance.
  __A	:		Any	  =      guidance_scale > 1.0
  # get unconditional embeddings for classifier free guidance
  if do_classifier_free_guidance:
   __A	:		List[Any]	  =      content_text_input.input_ids.shape[-1]
   __A	:		int	  =      self.tokenizer(['']   ,   padding='max_length'   ,   max_length=__lowercase   ,   return_tensors='pt'    )
   __A	:		str	  =      self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]
   # duplicate unconditional embeddings for each generation per prompt
   __A	:		Any	  =      uncond_embeddings.repeat_interleave(__lowercase   ,   dim=0    )

   # For classifier free guidance, we need to do two forward passes.
   # Here we concatenate the unconditional and text embeddings into a single batch
   # to avoid doing two forward passes
   __A	:		List[Any]	  =      torch.cat([uncond_embeddings, text_embeddings]    )

  # get the initial random noise unless the user supplied it

  # Unlike in other pipelines, latents need to be generated in the target device
  # for 1-to-1 results reproducibility with the CompVis implementation.
  # However this currently doesn't work in `mps`.
  __A	:		List[Any]	  =      (batch_size, self.unet.config.in_channels, height // 8, width // 8)
  __A	:		str	  =      text_embeddings.dtype
  if latents is None:
   if self.device.type == "mps":
    # randn does not work reproducibly on mps
    __A	:		List[str]	  =      torch.randn(__lowercase   ,   generator=__lowercase   ,   device='cpu'   ,   dtype=__lowercase    ).to(
        self.device    )
   else:
    __A	:		str	  =      torch.randn(__lowercase   ,   generator=__lowercase   ,   device=self.device   ,   dtype=__lowercase    )
  else:
   if latents.shape != latents_shape:
    raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
   __A	:		str	  =      latents.to(self.device    )

  # scale the initial noise by the standard deviation required by the scheduler
  __A	:		Optional[Any]	  =      latents * self.scheduler.init_noise_sigma

  # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
  # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
  # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
  # and should be between [0, 1]
  __A	:		Tuple	  =      'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
  __A	:		Optional[Any]	  =      {}
  if accepts_eta:
   __A	:		Optional[Any]	  =      eta

  # check if the scheduler accepts generator
  __A	:		Tuple	  =      'generator' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
  if accepts_generator:
   __A	:		str	  =      generator

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

    # predict the noise residual
    __A	:		Any	  =      self.unet(__lowercase   ,   __lowercase   ,   encoder_hidden_states=__lowercase    ).sample

    # perform classifier free guidance
    if do_classifier_free_guidance:
     __A  ,__A	:		Dict	  =      noise_pred.chunk(2    )
     __A	:		int	  =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

    # perform clip guidance
    if clip_guidance_scale > 0:
     __A	:		Optional[int]	  =      (
         text_embeddings.chunk(2    )[1] if do_classifier_free_guidance else text_embeddings
     )
     __A  ,__A	:		int	  =      self.cond_fn(
         __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   )

    # compute the previous noisy sample x_t -> x_t-1
    __A	:		Optional[int]	  =      self.scheduler.step(__lowercase   ,   __lowercase   ,   __lowercase   ,   **__lowercase    ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
  __A	:		List[Any]	  =      1 / 0.1_8_2_1_5 * latents
  __A	:		Optional[int]	  =      self.vae.decode(__lowercase    ).sample

  __A	:		List[str]	  =      (image / 2 + 0.5).clamp(0   ,   1    )
  __A	:		Optional[int]	  =      image.cpu().permute(0   ,   2   ,   3   ,   1    ).numpy()

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

  if not return_dict:
   return (image, None)

  return StableDiffusionPipelineOutput(images=__lowercase   ,   nsfw_content_detected=__lowercase    )










'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_			:  Any										=   logging.get_logger(__name__)

UpperCAmelCase_			:  Tuple										=   {'vocab_file': 'spm_char.model'}

UpperCAmelCase_			:  int										=   {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

UpperCAmelCase_			:  List[Any]										=   {
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    VOCAB_FILES_NAMES
 __lowercase					:		int     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		int     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		Optional[int]     				=    ['''input_ids''', '''attention_mask''']


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

  """simple docstring"""







  __A	:		Dict	  =      {} if sp_model_kwargs is None else sp_model_kwargs

  super().__init__(
      bos_token=__lowercase   ,   eos_token=__lowercase   ,   unk_token=__lowercase   ,   pad_token=__lowercase   ,   sp_model_kwargs=self.sp_model_kwargs   ,   **__lowercase   ,   )

  __A	:		Optional[Any]	  =      vocab_file

  __A	:		Any	  =      spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(__lowercase    )


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.sp_model.get_piece_size()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      {self.convert_ids_to_tokens(__lowercase    ): i for i in range(self.vocab_size    )}
  vocab.update(self.added_tokens_encoder    )
  return vocab


 def __getstate__(   self    ):

  """simple docstring"""







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


 def __setstate__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      d

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

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


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







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


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return self.sp_model.piece_to_id(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.sp_model.IdToPiece(__lowercase    )
  return token


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		str	  =      []
  __A	:		str	  =      ''
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    out_string += self.sp_model.decode(__lowercase    ) + token
    __A	:		Any	  =      []
   else:
    current_sub_tokens.append(__lowercase    )
  out_string += self.sp_model.decode(__lowercase    )
  return out_string.strip()


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  if token_ids_a is None:
   return token_ids_a + [self.eos_token_id]
  # We don't expect to process pairs, but leave the pair logic for API consistency
  return token_ids_a + token_ids_a + [self.eos_token_id]


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None   ,   __lowercase = False    ):

  """simple docstring"""







  if already_has_special_tokens:
   return super().get_special_tokens_mask(
       token_ids_a=__lowercase   ,   token_ids_a=__lowercase   ,   already_has_special_tokens=__lowercase    )

  __A	:		Dict	  =      [1]
  if token_ids_a is None:
   return ([0] * len(__lowercase    )) + suffix_ones
  return ([0] * len(__lowercase    )) + ([0] * len(__lowercase    )) + suffix_ones







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  if not os.path.isdir(__lowercase    ):
   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
   return
  __A	:		Optional[int]	  =      os.path.join(
      __lowercase   ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

  return (out_vocab_file,)






