


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


_A			=						logging.get_logger(__name__)

_A			=						'▁'

_A			=						{'vocab_file': 'prophetnet.tokenizer'}

_A			=						{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

_A			=						{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

_A			=						{
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str    ):
     __UpperCamelCase   							=collections.OrderedDict()
     with open(SCREAMING_SNAKE_CASE__      ,					'r'      ,					encoding='utf-8'    ) as reader:
          __UpperCamelCase   							=reader.readlines()
     for index, token in enumerate(SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=token.rstrip('\n'    )
          __UpperCamelCase   							=index
     return vocab






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[Any]              =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		int              =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Dict              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		List[str]              =  ["input_ids", "attention_mask"]






    def __init__(      self ,       A_ ,       A_="[SEP]" ,       A_="[SEP]" ,       A_="[SEP]" ,       A_="[UNK]" ,       A_="[PAD]" ,       A_="[CLS]" ,       A_="[MASK]" ,       A_ = None ,       **A_ ,       )  ->	None:
         __UpperCamelCase   							={} if sp_model_kwargs is None else sp_model_kwargs

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

         try:
              import sentencepiece as spm
         except ImportError:
              logger.warning(
                  'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                  ' pip install sentencepiece'   )
              raise

         __UpperCamelCase   							=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(A_   )   )
         __UpperCamelCase   							=vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # put special tokens and [unused] tokens into the vocab
         __UpperCamelCase   							={'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

         for i in range(10   ):
              __UpperCamelCase   							=f'[unused{i}]'
              __UpperCamelCase   							=5 + i

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         __UpperCamelCase   							=12
         __UpperCamelCase   							={v: k for k, v in self.fairseq_tokens_to_ids.items()}
         for k in self.fairseq_tokens_to_ids.keys():
              self.unique_no_split_tokens.append(A_   )






    def __getstate__(      self   )  ->	Tuple:
         __UpperCamelCase   							=self.__dict__.copy()
         __UpperCamelCase   							=None
         return state






    def __setstate__(      self ,       A_   )  ->	Dict:
         __UpperCamelCase   							=d
         try:
              import sentencepiece as spm
         except ImportError:
              logger.warning(
                  'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                  ' pip install sentencepiece'   )
              raise

         # 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 _a (      self ,       A_ ,       A_ = None ,       A_ = False   )  ->	List[int]:

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

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






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:
         __UpperCamelCase   							=[self.sep_token_id]

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






    @property
    def _a (      self   )  ->	Optional[int]:
         return len(self.sp_model   ) + self.fairseq_offset






    def _a (      self   )  ->	Optional[Any]:
         __UpperCamelCase   							={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 ,       A_   )  ->	str:
         return self.sp_model.encode(A_ ,       out_type=A_   )






    def _a (      self ,       A_   )  ->	Dict:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         __UpperCamelCase   							=self.sp_model.PieceToId(A_   )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






    def _a (      self ,       A_   )  ->	List[Any]:
         if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset   )






    def _a (      self ,       A_   )  ->	str:
         __UpperCamelCase   							=''.join(A_   ).replace(A_ ,       ' '   ).strip()
         return out_string






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple[str]:
         if not os.path.isdir(A_   ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
              return
         __UpperCamelCase   							=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:
                   __UpperCamelCase   							=self.sp_model.serialized_model_proto()
                   fi.write(A_   )

         return (out_vocab_file,)






    def _a (      self ,       A_ ,       A_ = None   )  ->	List[int]:

         if token_ids_a is None:
              return token_ids_a + [self.sep_token_id]
         __UpperCamelCase   							=[self.sep_token_id]
         return token_ids_a + sep + token_ids_a + sep









import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		torch.FloatTensor
    UpperCAmelCase__						:		Optional[torch.FloatTensor]              =  None

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : int=0.999      ,					SCREAMING_SNAKE_CASE__      : str="cosine"      ,					):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Any    ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
               return math.exp(t * -12.0    )

     else:
          raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

     __UpperCamelCase   							=[]
     for i in range(SCREAMING_SNAKE_CASE__    ):
          __UpperCamelCase   							=i / num_diffusion_timesteps
          __UpperCamelCase   							=(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__    ) / alpha_bar_fn(SCREAMING_SNAKE_CASE__    )      ,					SCREAMING_SNAKE_CASE__    )    )
     return torch.tensor(SCREAMING_SNAKE_CASE__      ,					dtype=torch.floataa    )






class    UpperCAmelCase__	( A_       ,  A_		):



    """simple docstring"""






    @register_to_config
    def __init__(      self ,       A_ = 1000 ,       A_ = "fixed_small_log" ,       A_ = True ,       A_ = 1.0 ,       A_ = "epsilon" ,       A_ = "squaredcos_cap_v2" ,       )  ->	Tuple:
         if beta_schedule != "squaredcos_cap_v2":
              raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''   )

         __UpperCamelCase   							=betas_for_alpha_bar(A_   )

         __UpperCamelCase   							=1.0 - self.betas
         __UpperCamelCase   							=torch.cumprod(self.alphas ,       dim=0   )
         __UpperCamelCase   							=torch.tensor(1.0   )

         # standard deviation of the initial noise distribution
         __UpperCamelCase   							=1.0

         # setable values
         __UpperCamelCase   							=None
         __UpperCamelCase   							=torch.from_numpy(np.arange(0 ,       A_   )[::-1].copy()   )

         __UpperCamelCase   							=variance_type






    def _a (      self ,       A_ ,       A_ = None   )  ->	torch.FloatTensor:
         return sample






    def _a (      self ,       A_ ,       A_ = None   )  ->	Tuple:
         __UpperCamelCase   							=num_inference_steps
         __UpperCamelCase   							=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         __UpperCamelCase   							=(np.arange(0 ,       A_   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
         __UpperCamelCase   							=torch.from_numpy(A_   ).to(A_   )






    def _a (      self ,       A_ ,       A_=None ,       A_=None ,       A_=None   )  ->	List[Any]:
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

         __UpperCamelCase   							=self.alphas_cumprod[t]
         __UpperCamelCase   							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         __UpperCamelCase   							=beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
              __UpperCamelCase   							=self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
              __UpperCamelCase   							=torch.log(torch.clamp(A_ ,       min=1E-20   )   )
              __UpperCamelCase   							=torch.exp(0.5 * variance   )
         elif variance_type == "learned_range":
              # NOTE difference with DDPM scheduler
              __UpperCamelCase   							=variance.log()
              __UpperCamelCase   							=beta.log()

              __UpperCamelCase   							=(predicted_variance + 1) / 2
              __UpperCamelCase   							=frac * max_log + (1 - frac) * min_log

         return variance






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       A_=None ,       A_ = True ,       )  ->	Union[UnCLIPSchedulerOutput, Tuple]:
         __UpperCamelCase   							=timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
              __UpperCamelCase							,				__UpperCamelCase   							=torch.split(A_ ,       sample.shape[1] ,       dim=1   )
         else:
              __UpperCamelCase   							=None

         # 1. compute alphas, betas
         if prev_timestep is None:
              __UpperCamelCase   							=t - 1

         __UpperCamelCase   							=self.alphas_cumprod[t]
         __UpperCamelCase   							=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __UpperCamelCase   							=1 - alpha_prod_t
         __UpperCamelCase   							=1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
              __UpperCamelCase   							=self.betas[t]
              __UpperCamelCase   							=self.alphas[t]
         else:
              __UpperCamelCase   							=1 - alpha_prod_t / alpha_prod_t_prev
              __UpperCamelCase   							=1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
              __UpperCamelCase   							=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
              __UpperCamelCase   							=model_output
         else:
              raise ValueError(
                  f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                  ' for the UnCLIPScheduler.'   )

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
              __UpperCamelCase   							=torch.clamp(
                  A_ ,       -self.config.clip_sample_range ,       self.config.clip_sample_range   )

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __UpperCamelCase   							=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         __UpperCamelCase   							=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         __UpperCamelCase   							=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         __UpperCamelCase   							=0
         if t > 0:
              __UpperCamelCase   							=randn_tensor(
                  model_output.shape ,       dtype=model_output.dtype ,       generator=A_ ,       device=model_output.device   )

              __UpperCamelCase   							=self._get_variance(
                  A_ ,       predicted_variance=A_ ,       prev_timestep=A_ ,       )

              if self.variance_type == "fixed_small_log":
                   __UpperCamelCase   							=variance
              elif self.variance_type == "learned_range":
                   __UpperCamelCase   							=(0.5 * variance).exp()
              else:
                   raise ValueError(
                       f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                       ' for the UnCLIPScheduler.'   )

              __UpperCamelCase   							=variance * variance_noise

         __UpperCamelCase   							=pred_prev_sample + variance

         if not return_dict:
              return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=A_ ,       pred_original_sample=A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       )  ->	torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
         __UpperCamelCase   							=self.alphas_cumprod.to(device=original_samples.device ,       dtype=original_samples.dtype   )
         __UpperCamelCase   							=timesteps.to(original_samples.device   )

         __UpperCamelCase   							=alphas_cumprod[timesteps] ** 0.5
         __UpperCamelCase   							=sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape   ) < len(original_samples.shape   ):
              __UpperCamelCase   							=sqrt_alpha_prod.unsqueeze(-1   )

         __UpperCamelCase   							=(1 - alphas_cumprod[timesteps]) ** 0.5
         __UpperCamelCase   							=sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape   ) < len(original_samples.shape   ):
              __UpperCamelCase   							=sqrt_one_minus_alpha_prod.unsqueeze(-1   )

         __UpperCamelCase   							=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples





