"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A   					=    logging.get_logger(__name__)


_A   					=    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
_A   					=    {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

_A   					=    {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}




@lru_cache()
def 						SCREAMING_SNAKE_CASE					(  )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				(
				    list(range(ord("!"       )  ,					ord("~"       ) + 1       )       ) + list(range(ord("¡"       )  ,					ord("¬"       ) + 1       )       ) + list(range(ord("®"       )  ,					ord("ÿ"       ) + 1       )       )
				)
				SCREAMING_SNAKE_CASE__		  =				bs[:]
				SCREAMING_SNAKE_CASE__		  =				0
				for b in range(2**8       ):
								if b not in bs:
												bs.append(__UpperCAmelCase       )
												cs.append(2**8 + n       )
												n += 1
				SCREAMING_SNAKE_CASE__		  =				[chr(__UpperCAmelCase       ) for n in cs]
				return dict(zip(__UpperCAmelCase  ,					__UpperCAmelCase       )       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Union[str, Any]:
				SCREAMING_SNAKE_CASE__		  =				set()
				SCREAMING_SNAKE_CASE__		  =				word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)       )
								SCREAMING_SNAKE_CASE__		  =				char
				return pairs

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		PRETRAINED_VOCAB_FILES_MAP
					a   	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		["input_ids", "attention_mask"]



					def __init__(						self			:				Any   , _snake_case			:				List[str]   , _snake_case			:				str   , _snake_case			:				str="replace"   , _snake_case			:				List[Any]="<s>"   , _snake_case			:				Any="</s>"   , _snake_case			:				Tuple="</s>"   , _snake_case			:				Dict="<s>"   , _snake_case			:				List[str]="<unk>"   , _snake_case			:				Union[str, Any]="<pad>"   , _snake_case			:				Any="<mask>"   , _snake_case			:				Dict=False   , **_snake_case			:				Tuple   , )							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_snake_case   , lstrip=_snake_case   , rstrip=_snake_case		) if isinstance(_snake_case   , _snake_case		) else bos_token
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_snake_case   , lstrip=_snake_case   , rstrip=_snake_case		) if isinstance(_snake_case   , _snake_case		) else eos_token
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_snake_case   , lstrip=_snake_case   , rstrip=_snake_case		) if isinstance(_snake_case   , _snake_case		) else sep_token
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_snake_case   , lstrip=_snake_case   , rstrip=_snake_case		) if isinstance(_snake_case   , _snake_case		) else cls_token
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_snake_case   , lstrip=_snake_case   , rstrip=_snake_case		) if isinstance(_snake_case   , _snake_case		) else unk_token
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_snake_case   , lstrip=_snake_case   , rstrip=_snake_case		) if isinstance(_snake_case   , _snake_case		) else pad_token

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

									super().__init__(
									    errors=_snake_case   , bos_token=_snake_case   , eos_token=_snake_case   , unk_token=_snake_case   , sep_token=_snake_case   , cls_token=_snake_case   , pad_token=_snake_case   , mask_token=_snake_case   , add_prefix_space=_snake_case   , **_snake_case   , )

									with open(_snake_case   , encoding="utf-8"		) as vocab_handle:
													SCREAMING_SNAKE_CASE__		  =				json.load(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				{v: k for k, v in self.encoder.items()}
									SCREAMING_SNAKE_CASE__		  =				errors  # how to handle errors in decoding
									SCREAMING_SNAKE_CASE__		  =				bytes_to_unicode()
									SCREAMING_SNAKE_CASE__		  =				{v: k for k, v in self.byte_encoder.items()}
									with open(_snake_case   , encoding="utf-8"		) as merges_handle:
													SCREAMING_SNAKE_CASE__		  =				merges_handle.read().split("\n"		)[1:-1]
									SCREAMING_SNAKE_CASE__		  =				[tuple(merge.split()		) for merge in bpe_merges]
									SCREAMING_SNAKE_CASE__		  =				dict(zip(_snake_case   , range(len(_snake_case		)		)		)		)
									SCREAMING_SNAKE_CASE__		  =				{}
									SCREAMING_SNAKE_CASE__		  =				add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									SCREAMING_SNAKE_CASE__		  =				re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)



					@property
					def 			lowerCAmelCase_					(						self			:				int		)							->       Union[str, Any]:
									return len(self.encoder		)



					def 			lowerCAmelCase_					(						self			:				int		)							->       Dict:
									return dict(self.encoder   , **self.added_tokens_encoder		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				List[Any]		)							->       str:
									if token in self.cache:
													return self.cache[token]
									SCREAMING_SNAKE_CASE__		  =				tuple(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				get_pairs(_snake_case		)

									if not pairs:
													return token

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

																	if word[i] == first and i < len(_snake_case		) - 1 and word[i + 1] == second:
																					new_word.append(first + second		)
																					i += 2
																	else:
																					new_word.append(word[i]		)
																					i += 1
													SCREAMING_SNAKE_CASE__		  =				tuple(_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				new_word
													if len(_snake_case		) == 1:
																	break
													else:
																	SCREAMING_SNAKE_CASE__		  =				get_pairs(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				" ".join(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				word
									return word



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[Any]		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				[]
									for token in re.findall(self.pat   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				"".join(
													    self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_snake_case		).split(" "		)		)
									return bpe_tokens



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Any		)							->       Dict:
									return self.encoder.get(_snake_case   , self.encoder.get(self.unk_token		)		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Tuple		)							->       Optional[Any]:
									return self.decoder.get(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				"".join(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8"   , errors=self.errors		)
									return text



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

									with open(_snake_case   , "w"   , encoding="utf-8"		) as f:
													f.write(json.dumps(self.encoder   , indent=2   , sort_keys=_snake_case   , ensure_ascii=_snake_case		) + "\n"		)

									SCREAMING_SNAKE_CASE__		  =				0
									with open(_snake_case   , "w"   , encoding="utf-8"		) as writer:
													writer.write("#version: 0.2\n"		)
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   , key=lambda _snake_case		: kv[1]		):
																	if index != token_index:
																					logger.warning(
																					    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																					    " Please check that the tokenizer is not corrupted!"		)
																					SCREAMING_SNAKE_CASE__		  =				token_index
																	writer.write(" ".join(_snake_case		) + "\n"		)
																	index += 1

									return vocab_file, merge_file



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				List[int]   , _snake_case			:				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__		  =				[self.cls_token_id]
									SCREAMING_SNAKE_CASE__		  =				[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep



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

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



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				List[int]   , _snake_case			:				Optional[List[int]] = None		)							->       List[int]:
									SCREAMING_SNAKE_CASE__		  =				[self.sep_token_id]
									SCREAMING_SNAKE_CASE__		  =				[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 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				List[str]   , _snake_case			:				Optional[int]=False   , **_snake_case			:				str		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				kwargs.pop("add_prefix_space"   , self.add_prefix_space		)
									if (is_split_into_words or add_prefix_space) and (len(_snake_case		) > 0 and not text[0].isspace()):
													SCREAMING_SNAKE_CASE__		  =				" " + text
									return (text, kwargs)





"""simple docstring"""






# 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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42
					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		1



					@register_to_config
					def __init__(						self			:				Tuple   , _snake_case			:				int = 2000   , _snake_case			:				float = 0.15   , _snake_case			:				float = 0.01   , _snake_case			:				float = 1348.0   , _snake_case			:				float = 1e-5   , _snake_case			:				int = 1   , )							->       str:
									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE__		  =				sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__		  =				None

									self.set_sigmas(_snake_case   , _snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[int] = None		)							->       torch.FloatTensor:
									return sample



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				Union[str, torch.device] = None		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

									SCREAMING_SNAKE_CASE__		  =				torch.linspace(1   , _snake_case   , _snake_case   , device=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				float = None   , _snake_case			:				float = None		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				sigma_min if sigma_min is not None else self.config.sigma_min
									SCREAMING_SNAKE_CASE__		  =				sigma_max if sigma_max is not None else self.config.sigma_max
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if self.timesteps is None:
													self.set_timesteps(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
									SCREAMING_SNAKE_CASE__		  =				torch.exp(torch.linspace(math.log(_snake_case		)   , math.log(_snake_case		)   , _snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				List[str]		)							->       List[Any]:
									return torch.where(
									    timesteps == 0   , torch.zeros_like(t.to(timesteps.device		)		)   , self.discrete_sigmas[timesteps - 1].to(timesteps.device		)   , )



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				torch.FloatTensor   , _snake_case			:				int   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SdeVeOutput, Tuple]:
									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

									SCREAMING_SNAKE_CASE__		  =				timestep * torch.ones(
									    sample.shape[0]   , device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
									SCREAMING_SNAKE_CASE__		  =				(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
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(self.discrete_sigmas.device		)

									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas[timesteps].to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				self.get_adjacent_sigma(_snake_case   , _snake_case		).to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				torch.zeros_like(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				(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
									SCREAMING_SNAKE_CASE__		  =				diffusion.flatten()
									while len(diffusion.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				diffusion.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(
									    sample.shape   , layout=sample.layout   , generator=_snake_case   , device=sample.device   , dtype=sample.dtype		)
									SCREAMING_SNAKE_CASE__		  =				sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									SCREAMING_SNAKE_CASE__		  =				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=_snake_case   , prev_sample_mean=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SchedulerOutput, Tuple]:
									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
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(sample.shape   , layout=sample.layout   , generator=_snake_case		).to(sample.device		)

									# compute step size from the model_output, the noise, and the snr
									SCREAMING_SNAKE_CASE__		  =				torch.norm(model_output.reshape(model_output.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				torch.norm(noise.reshape(noise.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
									SCREAMING_SNAKE_CASE__		  =				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
									SCREAMING_SNAKE_CASE__		  =				step_size.flatten()
									while len(step_size.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				step_size.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				sample + step_size * model_output
									SCREAMING_SNAKE_CASE__		  =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , )							->       torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(original_samples.device		)
									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas.to(original_samples.device		)[timesteps]
									SCREAMING_SNAKE_CASE__		  =				(
									    noise * sigmas[:, None, None, None]
									    if noise is not None
									    else torch.randn_like(_snake_case		) * sigmas[:, None, None, None]
									)
									SCREAMING_SNAKE_CASE__		  =				noise + original_samples
									return noisy_samples



					def __len__(						self			:				List[Any]		)							->       int:
									return self.config.num_train_timesteps




