

"""simple docstring"""




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

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      _UpperCAmelCase   (							_lowerCAmelCase		,    _lowerCAmelCase       ):
	a__		: Tuple					=					1



	@register_to_config
	def __init__(  self     :       List[Any]			,    _lowercase     :       Any=20_00			,    _lowercase     :       Union[str, Any]=0.1			,    _lowercase     :       Union[str, Any]=20			,    _lowercase     :       Optional[int]=1E-3						):
		__UpperCAmelCase      		= None
		__UpperCAmelCase      		= None
		__UpperCAmelCase      		= None



	def        a  (  self     :       List[Any]			,    _lowercase     :       str			,    _lowercase     :       Union[str, torch.device] = None						):
		__UpperCAmelCase      		= torch.linspace(1			,    self.config.sampling_eps			,    _lowercase			,    device=_lowercase						)



	def        a  (  self     :       Optional[int]			,    _lowercase     :       Any			,    _lowercase     :       Tuple			,    _lowercase     :       Dict			,    _lowercase     :       Dict=None						):
		if self.timesteps is None:
			raise ValueError(
			    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

		# TODO(Patrick) better comments + non-PyTorch
		# postprocess model score
		__UpperCAmelCase      		= (
		    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
		)
		__UpperCAmelCase      		= torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
		__UpperCAmelCase      		= std.flatten()
		while len(std.shape						) < len(score.shape						):
			__UpperCAmelCase      		= std.unsqueeze(-1						)
		__UpperCAmelCase      		= -score / std

		# compute
		__UpperCAmelCase      		= -1.0 / len(self.timesteps						)

		__UpperCAmelCase      		= self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
		__UpperCAmelCase      		= beta_t.flatten()
		while len(beta_t.shape						) < len(x.shape						):
			__UpperCAmelCase      		= beta_t.unsqueeze(-1						)
		__UpperCAmelCase      		= -0.5 * beta_t * x

		__UpperCAmelCase      		= torch.sqrt(_lowercase						)
		__UpperCAmelCase      		= drift - diffusion**2 * score
		__UpperCAmelCase      		= x + drift * dt

		# add noise
		__UpperCAmelCase      		= randn_tensor(x.shape			,    layout=x.layout			,    generator=_lowercase			,    device=x.device			,    dtype=x.dtype						)
		__UpperCAmelCase      		= x_mean + diffusion * math.sqrt(-dt						) * noise

		return x, x_mean



	def __len__(  self     :       Optional[int]						):
		return self.config.num_train_timesteps


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_barthez import BarthezTokenizer
else:
						_snake_case      :							Any     	=    None

_snake_case      :							Union[str, Any]     	=    logging.get_logger(__name__)

_snake_case      :							Optional[Any]     	=    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_snake_case      :							int     	=    {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

_snake_case      :							str     	=    {
    "moussaKam/mbarthez": 10_24,
    "moussaKam/barthez": 10_24,
    "moussaKam/barthez-orangesum-title": 10_24,
}

_snake_case      :							Optional[int]     	=    "▁"





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Any				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  Union[str, Any]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  Dict				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Optional[Any]				   =					BarthezTokenizer





						def __init__(			self							:str		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:Union[str, Any]="<s>"		,    lowerCAmelCase__							:Optional[Any]="</s>"		,    lowerCAmelCase__							:str="</s>"		,    lowerCAmelCase__							:List[str]="<s>"		,    lowerCAmelCase__							:Dict="<unk>"		,    lowerCAmelCase__							:int="<pad>"		,    lowerCAmelCase__							:Optional[Any]="<mask>"		,    **lowerCAmelCase__							:Optional[int]		,    )  ->int:
										# Mask token behave like a normal word, i.e. include the space before it
										lowercase         =      AddedToken(lowerCAmelCase__		,    lstrip=lowerCAmelCase__		,    rstrip=lowerCAmelCase__     ) if isinstance(lowerCAmelCase__		,    lowerCAmelCase__     ) else mask_token

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

										lowercase         =      vocab_file
										lowercase         =      False if not self.vocab_file else True





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

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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      [self.sep_token_id]
										lowercase         =      [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]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = None     )  ->Tuple[str]:
										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(lowerCAmelCase__     ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
														return
										lowercase         =      os.path.join(
										    lowerCAmelCase__		,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

										return (out_vocab_file,)
