



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

import sentencepiece as spm

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


_lowercase:				Optional[Any]       =	logging.get_logger(__name__)
_lowercase:				List[str]       =	{"vocab_file": "spiece.model"}

_lowercase:				Any       =	{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

_lowercase:				Optional[int]       =	{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_lowercase:				Optional[int]       =	"▁"



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							VOCAB_FILES_NAMES
	__A					=							PRETRAINED_VOCAB_FILES_MAP
	__A					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A					=							["input_ids", "token_type_ids"]




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=False	, lowerCamelCase_=True	, lowerCamelCase_=True	, lowerCamelCase_="<unk>"	, lowerCamelCase_="[SEP]"	, lowerCamelCase_="<pad>"	, lowerCamelCase_="[CLS]"	, lowerCamelCase_="[MASK]"	, lowerCamelCase_ = None	, **lowerCamelCase_	, ):
						"""simple docstring"""

						a        =     (
						    AddedToken(lowerCamelCase_	, lstrip=lowerCamelCase_	, rstrip=lowerCamelCase_	, normalized=lowerCamelCase_	)
						    if isinstance(lowerCamelCase_	, lowerCamelCase_	)
						    else mask_token
						)

						a        =     {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    do_lower_case=lowerCamelCase_	, remove_space=lowerCamelCase_	, keep_accents=lowerCamelCase_	, unk_token=lowerCamelCase_	, sep_token=lowerCamelCase_	, pad_token=lowerCamelCase_	, cls_token=lowerCamelCase_	, mask_token=lowerCamelCase_	, sp_model_kwargs=self.sp_model_kwargs	, **lowerCamelCase_	, )

						a        =     do_lower_case
						a        =     remove_space
						a        =     keep_accents
						a        =     vocab_file

						a        =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
						self.sp_model.Load(lowerCamelCase_	)




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return len(self.sp_model	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     {self.convert_ids_to_tokens(lowerCamelCase_	): i for i in range(self.vocab_size	)}
						vocab.update(self.added_tokens_encoder	)
						return vocab




	def __getstate__(self	):
						"""simple docstring"""

						a        =     self.__dict__.copy()
						a        =     None
						return state




	def __setstate__(self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     d

						# for backward compatibility
						if not hasattr(self	, "sp_model_kwargs"	):
											a        =     {}

						a        =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
						self.sp_model.Load(self.vocab_file	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						if self.remove_space:
											a        =     " ".join(inputs.strip().split()	)
						else:
											a        =     inputs
						a        =     outputs.replace("``"	, "\""	).replace("''"	, "\""	)

						if not self.keep_accents:
											a        =     unicodedata.normalize("NFKD"	, lowerCamelCase_	)
											a        =     "".join([c for c in outputs if not unicodedata.combining(lowerCamelCase_	)]	)
						if self.do_lower_case:
											a        =     outputs.lower()

						return outputs




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     self.preprocess_text(lowerCamelCase_	)
						a        =     self.sp_model.encode(lowerCamelCase_	, out_type=lowerCamelCase_	)
						a        =     []
						for piece in pieces:
											if len(lowerCamelCase_	) > 1 and piece[-1] == str(","	) and piece[-2].isdigit():
																a        =     self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase_	, ""	)	)
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																					if len(cur_pieces[0]	) == 1:
																										a        =     cur_pieces[1:]
																					else:
																										a        =     cur_pieces[0][1:]
																cur_pieces.append(piece[-1]	)
																new_pieces.extend(lowerCamelCase_	)
											else:
																new_pieces.append(lowerCamelCase_	)

						return new_pieces




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						return self.sp_model.PieceToId(lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						return self.sp_model.IdToPiece(lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     []
						a        =     ""
						a        =     False
						for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
																if not prev_is_special:
																					out_string += " "
																out_string += self.sp_model.decode(lowerCamelCase_	) + token
																a        =     True
																a        =     []
											else:
																current_sub_tokens.append(lowerCamelCase_	)
																a        =     False
						out_string += self.sp_model.decode(lowerCamelCase_	)
						return out_string.strip()




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = False	, lowerCamelCase_ = None	, lowerCamelCase_ = True	, **lowerCamelCase_	, ):
						"""simple docstring"""

						a        =     kwargs.pop("use_source_tokenizer"	, lowerCamelCase_	)

						a        =     self.convert_ids_to_tokens(lowerCamelCase_	, skip_special_tokens=lowerCamelCase_	)

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						a        =     []
						a        =     []
						for token in filtered_tokens:
											if skip_special_tokens and token in self.all_special_ids:
																continue
											if token in self.added_tokens_encoder:
																if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_	)	)
																					a        =     []
																sub_texts.append(lowerCamelCase_	)
											else:
																current_sub_text.append(lowerCamelCase_	)
						if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(lowerCamelCase_	)	)

						# Mimic the behavior of the Rust tokenizer:
						# No space after <unk>
						if spaces_between_special_tokens:
											a        =     re.sub(R"(<unk>) "	, R"\1"	, " ".join(lowerCamelCase_	)	)
						else:
											a        =     "".join(lowerCamelCase_	)

						a        =     (
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
											a        =     self.clean_up_tokenization(lowerCamelCase_	)
											return clean_text
						else:
											return text




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	):
						"""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   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = False	):
						"""simple docstring"""

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

						if token_ids_a is not None:
											return [1] + ([0] * len(lowerCamelCase_	)) + [1] + ([0] * len(lowerCamelCase_	)) + [1]
						return [1] + ([0] * len(lowerCamelCase_	)) + [1]




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	):
						"""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   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_ = None	):
						"""simple docstring"""

						if not os.path.isdir(lowerCamelCase_	):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
											return
						a        =     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_	) and os.path.isfile(self.vocab_file	):
											copyfile(self.vocab_file	, lowerCamelCase_	)
						elif not os.path.isfile(self.vocab_file	):
											with open(lowerCamelCase_	, "wb"	) as fi:
																a        =     self.sp_model.serialized_model_proto()
																fi.write(lowerCamelCase_	)

						return (out_vocab_file,)





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

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left



@flax.struct.dataclass
class 			_lowercase							:





	"""simple docstring"""
	# setable values
	__A					=							None
	__A					=							None
	__A					=							None




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls()




@dataclass
class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							42
	__A					=							42
	__A					=							None




class 			_lowercase							(     lowerCAmelCase,   lowerCAmelCase			):





	"""simple docstring"""




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return True




	@register_to_config
	def __init__(self	, lowerCamelCase_ = 2000	, lowerCamelCase_ = 0.15	, lowerCamelCase_ = 0.01	, lowerCamelCase_ = 1348.0	, lowerCamelCase_ = 1E-5	, lowerCamelCase_ = 1	, ):
						"""simple docstring"""

						pass




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    lowerCamelCase_	, self.config.num_train_timesteps	, self.config.sigma_min	, self.config.sigma_max	, self.config.sampling_eps	, )




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = ()	, lowerCamelCase_ = None	):
						"""simple docstring"""

						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps

						a        =     jnp.linspace(1	, lowerCamelCase_	, lowerCamelCase_	)
						return state.replace(timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, lowerCamelCase_ = None	, ):
						"""simple docstring"""

						a        =     sigma_min if sigma_min is not None else self.config.sigma_min
						a        =     sigma_max if sigma_max is not None else self.config.sigma_max
						a        =     sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
											a        =     self.set_timesteps(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)

						a        =     jnp.exp(jnp.linspace(jnp.log(lowerCamelCase_	)	, jnp.log(lowerCamelCase_	)	, lowerCamelCase_	)	)
						a        =     jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]	)

						return state.replace(discrete_sigmas=lowerCamelCase_	, sigmas=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return jnp.where(timesteps == 0	, jnp.zeros_like(lowerCamelCase_	)	, state.discrete_sigmas[timesteps - 1]	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

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

						a        =     timestep * jnp.ones(
						    sample.shape[0]	, )
						a        =     (timestep * (len(state.timesteps	) - 1)).long()

						a        =     state.discrete_sigmas[timesteps]
						a        =     self.get_adjacent_sigma(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	)
						a        =     jnp.zeros_like(lowerCamelCase_	)
						a        =     (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
						a        =     diffusion.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)
						a        =     sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						a        =     prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
											return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, prev_sample_mean=lowerCamelCase_	, state=lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = True	, ):
						"""simple docstring"""

						if state.timesteps is None:
											raise ValueError(
											    "`state.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
						a        =     random.split(lowerCamelCase_	, num=1	)
						a        =     random.normal(key=lowerCamelCase_	, shape=sample.shape	)

						# compute step size from the model_output, the noise, and the snr
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     jnp.linalg.norm(lowerCamelCase_	)
						a        =     (self.config.snr * noise_norm / grad_norm) ** 2 * 2
						a        =     step_size * jnp.ones(sample.shape[0]	)

						# compute corrected sample: model_output term and noise term
						a        =     step_size.flatten()
						a        =     broadcast_to_shape_from_left(lowerCamelCase_	, sample.shape	)
						a        =     sample + step_size * model_output
						a        =     prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
											return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=lowerCamelCase_	, state=lowerCamelCase_	)




	def __len__(self	):
						"""simple docstring"""

						return self.config.num_train_timesteps
