




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_albert import AlbertTokenizer
else:
  _lowerCamelCase      :						List[str]							=  None

_lowerCamelCase      :						str							=  logging.get_logger(__name__)
_lowerCamelCase      :						Tuple							=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase      :						Any							=  {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

_lowerCamelCase      :						Any							=  {
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

_lowerCamelCase      :						Any							=  """▁"""


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = VOCAB_FILES_NAMES
    UpperCAmelCase__				    = PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__				    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__				    = AlbertTokenizer


    def __init__(     self :      int   ,					UpperCAmelCase__ :      int=None   ,					UpperCAmelCase__ :      Dict=None   ,					UpperCAmelCase__ :      Optional[Any]=True   ,					UpperCAmelCase__ :      Optional[int]=True   ,					UpperCAmelCase__ :      List[Any]=False   ,					UpperCAmelCase__ :      Optional[Any]="[CLS]"   ,					UpperCAmelCase__ :      str="[SEP]"   ,					UpperCAmelCase__ :      int="<unk>"   ,					UpperCAmelCase__ :      Union[str, Any]="[SEP]"   ,					UpperCAmelCase__ :      Dict="<pad>"   ,					UpperCAmelCase__ :      List[Any]="[CLS]"   ,					UpperCAmelCase__ :      str="[MASK]"   ,					**UpperCAmelCase__ :      int   ,					)		->str:





           '''simple docstring'''

           A__ 					=       (
               AddedToken(UpperCAmelCase__   ,					lstrip=UpperCAmelCase__   ,					rstrip=UpperCAmelCase__   ,					normalized=UpperCAmelCase__)
               if isinstance(UpperCAmelCase__   ,					UpperCAmelCase__)
               else mask_token
           )

           super().__init__(
               UpperCAmelCase__   ,					tokenizer_file=UpperCAmelCase__   ,					do_lower_case=UpperCAmelCase__   ,					remove_space=UpperCAmelCase__   ,					keep_accents=UpperCAmelCase__   ,					bos_token=UpperCAmelCase__   ,					eos_token=UpperCAmelCase__   ,					unk_token=UpperCAmelCase__   ,					sep_token=UpperCAmelCase__   ,					pad_token=UpperCAmelCase__   ,					cls_token=UpperCAmelCase__   ,					mask_token=UpperCAmelCase__   ,					**UpperCAmelCase__   ,					)

           A__ 					=       do_lower_case
           A__ 					=       remove_space
           A__ 					=       keep_accents
           A__ 					=       vocab_file
           A__ 					=       False if not self.vocab_file else True


    def 			SCREAMING_SNAKE_CASE				(     self :      Any   ,					UpperCAmelCase__ :      List[int]   ,					UpperCAmelCase__ :      Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      List[int]   ,					UpperCAmelCase__ :      Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           A__ 					=       [self.sep_token_id]
           A__ 					=       [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]





    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      Optional[str] = None)		->Tuple[str]:





           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                      '''tokenizer.''')

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__ 					=       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__):
                  copyfile(self.vocab_file   ,					UpperCAmelCase__)

           return (out_vocab_file,)



'''simple docstring'''


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class     A ( UpperCAmelCase							,						UpperCAmelCase ):
							a_										=   1





							@register_to_config
							def __init__(   self					:				Tuple  ,		__a					:				int = 1_0_0_0  ,		__a					:				Optional[Union[np.ndarray, List[float]]] = None						)				->						int:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(__a						)

								# standard deviation of the initial noise distribution
								__UpperCAmelCase      					=      1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								__UpperCAmelCase      					=      4

								# running values
								__UpperCAmelCase      					=      []





							def    snake_case__    (   self					:				str  ,		__a					:				int  ,		__a					:				Union[str, torch.device] = None						)				->						str:
								__UpperCAmelCase      					=      num_inference_steps
								__UpperCAmelCase      					=      torch.linspace(1  ,		0  ,		num_inference_steps + 1						)[:-1]
								__UpperCAmelCase      					=      torch.cat([steps, torch.tensor([0.0]						)]						)

								if self.config.trained_betas is not None:
									__UpperCAmelCase      					=      torch.tensor(self.config.trained_betas  ,		dtype=torch.floataa						)
								else:
									__UpperCAmelCase      					=      torch.sin(steps * math.pi / 2						) ** 2

								__UpperCAmelCase      					=      (1.0 - self.betas**2) ** 0.5

								__UpperCAmelCase      					=      (torch.atana(self.betas  ,		self.alphas						) / math.pi * 2)[:-1]
								__UpperCAmelCase      					=      timesteps.to(__a						)

								__UpperCAmelCase      					=      []





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				torch.FloatTensor  ,		__a					:				int  ,		__a					:				torch.FloatTensor  ,		__a					:				bool = True  ,		)				->						Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
									raise ValueError(
									    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''						)

								__UpperCAmelCase      					=      (self.timesteps == timestep).nonzero().item()
								__UpperCAmelCase      					=      timestep_index + 1

								__UpperCAmelCase      					=      sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(__a						)

								if len(self.ets						) == 1:
									__UpperCAmelCase      					=      self.ets[-1]
								elif len(self.ets						) == 2:
									__UpperCAmelCase      					=      (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets						) == 3:
									__UpperCAmelCase      					=      (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
								else:
									__UpperCAmelCase      					=      (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

								__UpperCAmelCase      					=      self._get_prev_sample(__a  ,		__a  ,		__a  ,		__a						)

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=__a						)





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				torch.FloatTensor  ,		*__a					:				Any  ,		**__a					:				Optional[int]						)				->						torch.FloatTensor:
								return sample





							def    snake_case__    (   self					:				int  ,		__a					:				List[Any]  ,		__a					:				Any  ,		__a					:				int  ,		__a					:				Union[str, Any]						)				->						Optional[Any]:
								__UpperCAmelCase      					=      self.alphas[timestep_index]
								__UpperCAmelCase      					=      self.betas[timestep_index]

								__UpperCAmelCase      					=      self.alphas[prev_timestep_index]
								__UpperCAmelCase      					=      self.betas[prev_timestep_index]

								__UpperCAmelCase      					=      (sample - sigma * ets) / max(__a  ,		1e-8						)
								__UpperCAmelCase      					=      next_alpha * pred + ets * next_sigma

								return prev_sample






							def __len__(   self					:				Dict						)				->						str:
								return self.config.num_train_timesteps
