



import os
import re
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


_lowerCamelCase						    =  logging.get_logger(__name__)

_lowerCamelCase						    =  {"""vocab_file""": """spiece.model"""}

_lowerCamelCase						    =  {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

_lowerCamelCase						    =  {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			VOCAB_FILES_NAMES
     lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase					=			["""input_ids""", """attention_mask"""]
     lowerCAmelCase					=			[]





     def __init__(       self :							str     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Tuple="<unk>"     ,  UpperCamelCase :							Tuple="<s>"     ,  UpperCamelCase :							List[str]="</s>"     ,  UpperCamelCase :							List[str]="<pad>"     ,  UpperCamelCase :							Dict="[SEP]"     ,  UpperCamelCase :							List[str]="[MASK]"     ,  UpperCamelCase :							Dict="[CLS]"     ,  UpperCamelCase :							Optional[Dict[str, Any]] = None     ,  **UpperCamelCase :							str     ,  )->None:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	AddedToken(UpperCamelCase     ,  lstrip=UpperCamelCase     ,  rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,  UpperCamelCase				) else bos_token
      __SCREAMING_SNAKE_CASE       :    List[str]       =	AddedToken(UpperCamelCase     ,  lstrip=UpperCamelCase     ,  rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,  UpperCamelCase				) else eos_token
      __SCREAMING_SNAKE_CASE       :    Tuple       =	AddedToken(UpperCamelCase     ,  lstrip=UpperCamelCase     ,  rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,  UpperCamelCase				) else unk_token
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	AddedToken(UpperCamelCase     ,  lstrip=UpperCamelCase     ,  rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,  UpperCamelCase				) else pad_token
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	AddedToken(UpperCamelCase     ,  lstrip=UpperCamelCase     ,  rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,  UpperCamelCase				) else cls_token
      __SCREAMING_SNAKE_CASE       :    List[str]       =	AddedToken(UpperCamelCase     ,  lstrip=UpperCamelCase     ,  rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,  UpperCamelCase				) else sep_token

      # Mask token behave like a normal word, i.e. include the space before it
      __SCREAMING_SNAKE_CASE       :    Dict       =	AddedToken(UpperCamelCase     ,  lstrip=UpperCamelCase     ,  rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,  UpperCamelCase				) else mask_token

      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=UpperCamelCase     ,  eos_token=UpperCamelCase     ,  unk_token=UpperCamelCase     ,  pad_token=UpperCamelCase     ,  sep_token=UpperCamelCase     ,  mask_token=UpperCamelCase     ,  cls_token=UpperCamelCase     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **UpperCamelCase     ,  )

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	vocab_file

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.Load(UpperCamelCase				)





     @property
     def 	__snake_case    (       self :							Optional[Any]				)->Optional[int]:
      return self.sp_model.get_piece_size()





     def 	__snake_case    (       self :							Optional[int]				)->str:
      __SCREAMING_SNAKE_CASE       :    Any       =	{self.convert_ids_to_tokens(UpperCamelCase				): i for i in range(self.vocab_size				)}
      vocab.update(self.added_tokens_encoder				)
      return vocab





     def __getstate__(       self :							List[Any]				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	self.__dict__.copy()
      __SCREAMING_SNAKE_CASE       :    int       =	None
      return state





     def __setstate__(       self :							Optional[Any]     ,  UpperCamelCase :							Optional[int]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	d

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

      __SCREAMING_SNAKE_CASE       :    str       =	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.Load(self.vocab_file				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							str				)->List[str]:
      return self.sp_model.encode(UpperCamelCase     ,  out_type=UpperCamelCase				)





     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Any				)->str:
      return self.sp_model.piece_to_id(UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Tuple				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.sp_model.IdToPiece(UpperCamelCase				)
      return token





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							List[str]				)->Any:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[]
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	""
      __SCREAMING_SNAKE_CASE       :    int       =	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(UpperCamelCase				) + token
        __SCREAMING_SNAKE_CASE       :    str       =	True
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	[]
       else:
        current_sub_tokens.append(UpperCamelCase				)
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	False
      out_string += self.sp_model.decode(UpperCamelCase				)
      return out_string.strip()





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[int]     ,  UpperCamelCase :							bool = False     ,  UpperCamelCase :							bool = None     ,  UpperCamelCase :							bool = True     ,  **UpperCamelCase :							int     ,  )->str:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	kwargs.pop("use_source_tokenizer"     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	self.convert_ids_to_tokens(UpperCamelCase     ,  skip_special_tokens=UpperCamelCase				)

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	[]
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[]
      for token in filtered_tokens:
       if skip_special_tokens and token in self.all_special_ids:
        continue
       if token in self.added_tokens_encoder:
        if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(UpperCamelCase				)				)
         __SCREAMING_SNAKE_CASE       :    Dict       =	[]
        sub_texts.append(UpperCamelCase				)
       else:
        current_sub_text.append(UpperCamelCase				)
      if current_sub_text:
       sub_texts.append(self.convert_tokens_to_string(UpperCamelCase				)				)

      # Mimic the behavior of the Rust tokenizer:
      # No space before [MASK] and [SEP]
      if spaces_between_special_tokens:
       __SCREAMING_SNAKE_CASE       :    Dict       =	re.sub(r" (\[(MASK|SEP)\])"     ,  r"\1"     ,  " ".join(UpperCamelCase				)				)
      else:
       __SCREAMING_SNAKE_CASE       :    Any       =	"".join(UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    Dict       =	(
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
       __SCREAMING_SNAKE_CASE       :    Any       =	self.clean_up_tokenization(UpperCamelCase				)
       return clean_text
      else:
       return text





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							str     ,  UpperCamelCase :							Optional[str] = None				)->Tuple[str]:
      if not os.path.isdir(UpperCamelCase				):
       logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
       return
      __SCREAMING_SNAKE_CASE       :    int       =	os.path.join(
          UpperCamelCase     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

      if os.path.abspath(self.vocab_file				) != os.path.abspath(UpperCamelCase				) and os.path.isfile(self.vocab_file				):
       copyfile(self.vocab_file     ,  UpperCamelCase				)
      elif not os.path.isfile(self.vocab_file				):
       with open(UpperCamelCase     ,  "wb"				) as fi:
        __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.sp_model.serialized_model_proto()
        fi.write(UpperCamelCase				)

      return (out_vocab_file,)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[int]     ,  UpperCamelCase :							Optional[List[int]] = None				)->List[int]:
      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      __SCREAMING_SNAKE_CASE       :    Tuple       =	[self.cls_token_id]
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep





     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							List[int]     ,  UpperCamelCase :							Optional[List[int]] = None     ,  UpperCamelCase :							bool = False				)->List[int]:
      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=UpperCamelCase     ,  token_ids_a=UpperCamelCase     ,  already_has_special_tokens=UpperCamelCase				)

      if token_ids_a is None:
       return [1] + ([0] * len(UpperCamelCase				)) + [1]
      return [1] + ([0] * len(UpperCamelCase				)) + [1] + ([0] * len(UpperCamelCase				)) + [1]






     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[int]     ,  UpperCamelCase :							Optional[List[int]] = None				)->List[int]:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	[self.sep_token_id]
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	[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				) * [0] + len(token_ids_a + sep				) * [1]











from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			["""vqvae"""]





     def __init__(       self :							Tuple     ,  UpperCamelCase :							AutoencoderKL     ,  UpperCamelCase :							UNetaDConditionModel     ,  UpperCamelCase :							Mel     ,  UpperCamelCase :							Union[DDIMScheduler, DDPMScheduler]     ,  )->Tuple:
      super().__init__()
      self.register_modules(unet=UpperCamelCase     ,  scheduler=UpperCamelCase     ,  mel=UpperCamelCase     ,  vqvae=UpperCamelCase				)





     def 	__snake_case    (       self :							List[Any]				)->int:
      return 5_0 if isinstance(self.scheduler     ,  UpperCamelCase				) else 1_0_0_0





     @torch.no_grad()
     def __call__(       self :							Dict     ,  UpperCamelCase :							int = 1     ,  UpperCamelCase :							str = None     ,  UpperCamelCase :							np.ndarray = None     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = 0     ,  UpperCamelCase :							int = None     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Generator = None     ,  UpperCamelCase :							float = 0     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							torch.Tensor = None     ,  UpperCamelCase :							Any=True     ,  )->Union[
         Union[AudioPipelineOutput, ImagePipelineOutput],
         Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	steps or self.get_default_steps()
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	step_generator or generator
      # For backwards compatibility
      if type(self.unet.config.sample_size				) == int:
       __SCREAMING_SNAKE_CASE       :    Dict       =	(self.unet.config.sample_size, self.unet.config.sample_size)
      if noise is None:
       __SCREAMING_SNAKE_CASE       :    Any       =	randn_tensor(
           (
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size[0],
               self.unet.config.sample_size[1],
           )     ,  generator=UpperCamelCase     ,  device=self.device     ,  )
      __SCREAMING_SNAKE_CASE       :    Any       =	noise
      __SCREAMING_SNAKE_CASE       :    Any       =	None

      if audio_file is not None or raw_audio is not None:
       self.mel.load_audio(UpperCamelCase     ,  UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    str       =	self.mel.audio_slice_to_image(UpperCamelCase				)
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	np.frombuffer(input_image.tobytes()     ,  dtype="uint8"				).reshape(
           (input_image.height, input_image.width)				)
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(input_image / 2_5_5) * 2 - 1
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.tensor(input_image[np.newaxis, :, :]     ,  dtype=torch.float				).to(self.device				)

       if self.vqvae is not None:
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.vqvae.encode(torch.unsqueeze(UpperCamelCase     ,  0				)				).latent_dist.sample(
            generator=UpperCamelCase				)[0]
        __SCREAMING_SNAKE_CASE       :    int       =	self.vqvae.config.scaling_factor * input_images

       if start_step > 0:
        __SCREAMING_SNAKE_CASE       :    List[str]       =	self.scheduler.add_noise(UpperCamelCase     ,  UpperCamelCase     ,  self.scheduler.timesteps[start_step - 1]				)

       __SCREAMING_SNAKE_CASE       :    int       =	(
           self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
       )
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(mask_start_secs * pixels_per_second				)
       __SCREAMING_SNAKE_CASE       :    int       =	int(mask_end_secs * pixels_per_second				)
       __SCREAMING_SNAKE_CASE       :    Any       =	self.scheduler.add_noise(UpperCamelCase     ,  UpperCamelCase     ,  torch.tensor(self.scheduler.timesteps[start_step:]				)				)

      for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]				)				):
       if isinstance(self.unet     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    str       =	self.unet(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)["sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    int       =	self.unet(UpperCamelCase     ,  UpperCamelCase				)["sample"]

       if isinstance(self.scheduler     ,  UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    int       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  eta=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]
       else:
        __SCREAMING_SNAKE_CASE       :    Tuple       =	self.scheduler.step(
            model_output=UpperCamelCase     ,  timestep=UpperCamelCase     ,  sample=UpperCamelCase     ,  generator=UpperCamelCase     ,  )["prev_sample"]

       if mask is not None:
        if mask_start > 0:
         __SCREAMING_SNAKE_CASE       :    int       =	mask[:, step, :, :mask_start]
        if mask_end > 0:
         __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	mask[:, step, :, -mask_end:]

      if self.vqvae is not None:
       # 0.18215 was scaling factor used in training to ensure unit variance
       __SCREAMING_SNAKE_CASE       :    Any       =	1 / self.vqvae.config.scaling_factor * images
       __SCREAMING_SNAKE_CASE       :    Any       =	self.vqvae.decode(UpperCamelCase				)["sample"]

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(images / 2 + 0.5).clamp(0     ,  1				)
      __SCREAMING_SNAKE_CASE       :    str       =	images.cpu().permute(0     ,  2     ,  3     ,  1				).numpy()
      __SCREAMING_SNAKE_CASE       :    Tuple       =	(images * 2_5_5).round().astype("uint8"				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	list(
          (Image.fromarray(_[:, :, 0]				) for _ in images)
          if images.shape[3] == 1
          else (Image.fromarray(UpperCamelCase     ,  mode="RGB"				).convert("L"				) for _ in images)				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	[self.mel.image_to_audio(UpperCamelCase				) for _ in images]
      if not return_dict:
       return images, (self.mel.get_sample_rate(), audios)

      return BaseOutput(**AudioPipelineOutput(np.array(UpperCamelCase				)[:, np.newaxis, :]				)     ,  **ImagePipelineOutput(UpperCamelCase				)				)





     @torch.no_grad()
     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Image.Image]     ,  UpperCamelCase :							int = 5_0				)->np.ndarray:
      assert isinstance(self.scheduler     ,  UpperCamelCase				)
      self.scheduler.set_timesteps(UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    int       =	np.array(
          [np.frombuffer(image.tobytes()     ,  dtype="uint8"				).reshape((1, image.height, image.width)				) for image in images]				)
      __SCREAMING_SNAKE_CASE       :    Dict       =	(sample / 2_5_5) * 2 - 1
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	torch.Tensor(UpperCamelCase				).to(self.device				)

      for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,  (0,)				)				):
       __SCREAMING_SNAKE_CASE       :    Optional[int]       =	t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.scheduler.alphas_cumprod[t]
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	(
           self.scheduler.alphas_cumprod[prev_timestep]
           if prev_timestep >= 0
           else self.scheduler.final_alpha_cumprod
       )
       __SCREAMING_SNAKE_CASE       :    Dict       =	1 - alpha_prod_t
       __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.unet(UpperCamelCase     ,  UpperCamelCase				)["sample"]
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(1 - alpha_prod_t_prev) ** 0.5 * model_output
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
       __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

      return sample






     @staticmethod
     def 	__snake_case    (       UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							torch.Tensor     ,  UpperCamelCase :							float				)->torch.Tensor:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	acos(torch.dot(torch.flatten(UpperCamelCase				)     ,  torch.flatten(UpperCamelCase				)				) / torch.norm(UpperCamelCase				) / torch.norm(UpperCamelCase				)				)
      return sin((1 - alpha) * theta				) * xa / sin(UpperCamelCase				) + sin(alpha * theta				) * xa / sin(UpperCamelCase				)






