






import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

lowerCamelCase__       		=  {
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}






def 	UpperCamelCase	(							snake_case__  : Optional[Any]	):




     '''simple docstring'''






     __snake_case      :Tuple 							=    set()
     __snake_case      :Tuple 							=    word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)	)
          __snake_case      :Optional[int] 							=    char

     __snake_case      :Optional[int] 							=    set(snake_case__	)
     return pairs




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Optional[int]								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					int								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[Any]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__      ,    a__="<s>"      ,    a__="</s>"      ,    a__="</s>"      ,    a__="<s>"      ,    a__="<unk>"      ,    a__="<pad>"      ,    a__="<mask>"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            super().__init__(
                bos_token=a__      ,    eos_token=a__      ,    unk_token=a__      ,    sep_token=a__      ,    cls_token=a__      ,    pad_token=a__      ,    mask_token=a__      ,    **a__      ,    )

            __snake_case      :str 							=    vocab_file
            __snake_case      :Optional[Any] 							=    merges_file

            __snake_case      :int 							=    {}
            __snake_case      :Any 							=    0
            __snake_case      :Tuple 							=    1
            __snake_case      :Optional[int] 							=    2
            __snake_case      :Any 							=    3

            self.add_from_file(a__     )

            __snake_case      :str 							=    {v: k for k, v in self.encoder.items()}

            with open(a__      ,    encoding="""utf-8"""     ) as merges_handle:
                 __snake_case      :Tuple 							=    merges_handle.read().split("""\n"""     )[:-1]
            __snake_case      :List[Any] 							=    [tuple(merge.split()[:-1]     ) for merge in merges]
            __snake_case      :List[str] 							=    dict(zip(a__      ,    range(len(a__     )     )     )     )
            __snake_case      :Any 							=    {}



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            __snake_case      :List[str] 							=    [self.cls_token_id]
            __snake_case      :List[str] 							=    [self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''simple docstring'''





            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 [1] + ([0] * len(a__     )) + [1]
            return [1] + ([0] * len(a__     )) + [1, 1] + ([0] * len(a__     )) + [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    [self.sep_token_id]
            __snake_case      :str 							=    [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 + sep + token_ids_a + sep     ) * [0]



       @property
       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            return len(self.encoder     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            return dict(self.encoder      ,    **self.added_tokens_encoder     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            if token in self.cache:
                 return self.cache[token]
            __snake_case      :Tuple 							=    tuple(a__     )
            __snake_case      :Optional[int] 							=    tuple(list(word[:-1]     ) + [word[-1] + """</w>"""]     )
            __snake_case      :List[str] 							=    get_pairs(a__     )

            if not pairs:
                 return token

            while True:
                 __snake_case      :Optional[int] 							=    min(a__      ,    key=lambda a__     : self.bpe_ranks.get(a__      ,    float("""inf"""     )     )     )
                 if bigram not in self.bpe_ranks:
                      break
                 __snake_case							,			__snake_case      :Any 							=    bigram
                 __snake_case      :Tuple 							=    []
                 __snake_case      :Optional[Any] 							=    0
                 while i < len(a__     ):
                      try:
                           __snake_case      :Tuple 							=    word.index(a__      ,    a__     )
                      except ValueError:
                           new_word.extend(word[i:]     )
                           break
                      else:
                           new_word.extend(word[i:j]     )
                           __snake_case      :List[Any] 							=    j

                      if word[i] == first and i < len(a__     ) - 1 and word[i + 1] == second:
                           new_word.append(first + second     )
                           i += 2
                      else:
                           new_word.append(word[i]     )
                           i += 1
                 __snake_case      :int 							=    tuple(a__     )
                 __snake_case      :List[str] 							=    new_word
                 if len(a__     ) == 1:
                      break
                 else:
                      __snake_case      :Union[str, Any] 							=    get_pairs(a__     )
            __snake_case      :str 							=    """@@ """.join(a__     )
            __snake_case      :Optional[int] 							=    word[:-4]
            __snake_case      :Dict 							=    word
            return word



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :int 							=    []

            __snake_case      :List[str] 							=    re.findall(R"""\S+\n?"""      ,    a__     )

            for token in words:
                 split_tokens.extend(list(self.bpe(a__     ).split(""" """     )     )     )
            return split_tokens



       def        __lowercase    (				self      ,    a__     )			->   Optional[Any]:






            '''simple docstring'''





            return self.encoder.get(a__      ,    self.encoder.get(self.unk_token     )     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            return self.decoder.get(a__      ,    self.unk_token     )



       def        __lowercase    (				self      ,    a__     )			->   int:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    """ """.join(a__     ).replace("""@@ """      ,    """"""     ).strip()
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





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

            if os.path.abspath(self.vocab_file     ) != os.path.abspath(a__     ):
                 copyfile(self.vocab_file      ,    a__     )

            if os.path.abspath(self.merges_file     ) != os.path.abspath(a__     ):
                 copyfile(self.merges_file      ,    a__     )

            return out_vocab_file, out_merge_file



       def        __lowercase    (				self      ,    a__     )			->   List[Any]:






            '''simple docstring'''





            if isinstance(a__      ,    a__     ):
                 try:
                      with open(a__      ,    """r"""      ,    encoding="""utf-8"""     ) as fd:
                           self.add_from_file(a__     )
                 except FileNotFoundError as fnfe:
                      raise fnfe
                 except UnicodeError:
                      raise Exception(F'''Incorrect encoding detected in {f}, please rebuild the dataset'''     )
                 return

            __snake_case      :Dict 							=    f.readlines()
            for lineTmp in lines:
                 __snake_case      :str 							=    lineTmp.strip()
                 __snake_case      :int 							=    line.rfind(""" """     )
                 if idx == -1:
                      raise ValueError("""Incorrect dictionary format, expected '<token> <cnt>'"""     )
                 __snake_case      :str 							=    line[:idx]
                 __snake_case      :Tuple 							=    len(self.encoder     )










# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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




@dataclass
class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					torch.FloatTensor
       lowerCamelCase				:					torch.FloatTensor




class       snake_case__ (				lowercase_						,     lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					int								=			1



       @register_to_config
       def __init__(				self      ,    a__ = 20_00      ,    a__ = 0.15      ,    a__ = 0.01      ,    a__ = 13_48.0      ,    a__ = 1e-5      ,    a__ = 1      ,    )			->   Any:






            '''simple docstring'''





            __snake_case      :int 							=    sigma_max

            # setable values
            __snake_case      :Any 							=    None

            self.set_sigmas(a__      ,    a__      ,    a__      ,    a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   torch.FloatTensor:






            '''simple docstring'''





            return sample



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

            __snake_case      :int 							=    torch.linspace(1      ,    a__      ,    a__      ,    device=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = None      ,    a__ = None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    sigma_min if sigma_min is not None else self.config.sigma_min
            __snake_case      :Any 							=    sigma_max if sigma_max is not None else self.config.sigma_max
            __snake_case      :Any 							=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
            if self.timesteps is None:
                 self.set_timesteps(a__      ,    a__     )

            __snake_case      :int 							=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
            __snake_case      :List[str] 							=    torch.exp(torch.linspace(math.log(a__     )      ,    math.log(a__     )      ,    a__     )     )
            __snake_case      :Any 							=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   Tuple:






            '''simple docstring'''





            return torch.where(
                timesteps == 0      ,    torch.zeros_like(t.to(timesteps.device     )     )      ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device     )      ,    )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SdeVeOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            __snake_case      :Optional[int] 							=    timestep * torch.ones(
                sample.shape[0]      ,    device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
            __snake_case      :Tuple 							=    (timestep * (len(self.timesteps     ) - 1)).long()

            # mps requires indices to be in the same device, so we use cpu as is the default with cuda
            __snake_case      :str 							=    timesteps.to(self.discrete_sigmas.device     )

            __snake_case      :str 							=    self.discrete_sigmas[timesteps].to(sample.device     )
            __snake_case      :Union[str, Any] 							=    self.get_adjacent_sigma(a__      ,    a__     ).to(sample.device     )
            __snake_case      :Dict 							=    torch.zeros_like(a__     )
            __snake_case      :str 							=    (sigma**2 - adjacent_sigma**2) ** 0.5

            # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
            # also equation 47 shows the analog from SDE models to ancestral sampling methods
            __snake_case      :Optional[Any] 							=    diffusion.flatten()
            while len(diffusion.shape     ) < len(sample.shape     ):
                 __snake_case      :int 							=    diffusion.unsqueeze(-1     )
            __snake_case      :Optional[Any] 							=    drift - diffusion**2 * model_output

            #  equation 6: sample noise for the diffusion term of
            __snake_case      :str 							=    randn_tensor(
                sample.shape      ,    layout=sample.layout      ,    generator=a__      ,    device=sample.device      ,    dtype=sample.dtype     )
            __snake_case      :str 							=    sample - drift  # subtract because `dt` is a small negative timestep
            # TODO is the variable diffusion the correct scaling term for the noise?
            __snake_case      :str 							=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

            if not return_dict:
                 return (prev_sample, prev_sample_mean)

            return SdeVeOutput(prev_sample=a__      ,    prev_sample_mean=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__ = None      ,    a__ = True      ,    )			->   Union[SchedulerOutput, Tuple]:






            '''simple docstring'''





            if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

            # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
            # sample noise for correction
            __snake_case      :Any 							=    randn_tensor(sample.shape      ,    layout=sample.layout      ,    generator=a__     ).to(sample.device     )

            # compute step size from the model_output, the noise, and the snr
            __snake_case      :List[Any] 							=    torch.norm(model_output.reshape(model_output.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Tuple 							=    torch.norm(noise.reshape(noise.shape[0]      ,    -1     )      ,    dim=-1     ).mean()
            __snake_case      :Dict 							=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
            __snake_case      :int 							=    step_size * torch.ones(sample.shape[0]     ).to(sample.device     )
            # self.repeat_scalar(step_size, sample.shape[0])

            # compute corrected sample: model_output term and noise term
            __snake_case      :Tuple 							=    step_size.flatten()
            while len(step_size.shape     ) < len(sample.shape     ):
                 __snake_case      :Optional[int] 							=    step_size.unsqueeze(-1     )
            __snake_case      :List[str] 							=    sample + step_size * model_output
            __snake_case      :Tuple 							=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

            if not return_dict:
                 return (prev_sample,)

            return SchedulerOutput(prev_sample=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__      ,    )			->   torch.FloatTensor:






            '''simple docstring'''





            __snake_case      :Tuple 							=    timesteps.to(original_samples.device     )
            __snake_case      :Optional[Any] 							=    self.discrete_sigmas.to(original_samples.device     )[timesteps]
            __snake_case      :int 							=    (
                noise * sigmas[:, None, None, None]
                if noise is not None
                else torch.randn_like(a__     ) * sigmas[:, None, None, None]
            )
            __snake_case      :str 							=    noise + original_samples
            return noisy_samples



       def __len__(				self     )			->   Optional[int]:






            '''simple docstring'''





            return self.config.num_train_timesteps


