

'''simple docstring'''

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_xlnet import XLNetTokenizer
else:
							SCREAMING_SNAKE_CASE							:				List[str]     	=			None


SCREAMING_SNAKE_CASE							:				str     	=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:				Any     	=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE							:				str     	=			{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE							:				List[Any]     	=			{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

SCREAMING_SNAKE_CASE							:				Tuple     	=			"▁"

# Segments (not really needed)
SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			0
SCREAMING_SNAKE_CASE							:				Optional[int]     	=			1
SCREAMING_SNAKE_CASE							:				Dict     	=			2
SCREAMING_SNAKE_CASE							:				Dict     	=			3
SCREAMING_SNAKE_CASE							:				List[Any]     	=			4






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     VOCAB_FILES_NAMES
				_a    	=     PRETRAINED_VOCAB_FILES_MAP
				_a    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_a    	=     """left"""
				_a    	=     XLNetTokenizer



				def __init__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=False,     _lowercase=True,     _lowercase=False,     _lowercase="<s>",     _lowercase="</s>",     _lowercase="<unk>",     _lowercase="<sep>",     _lowercase="<pad>",     _lowercase="<cls>",     _lowercase="<mask>",     _lowercase=["<eop>", "<eod>"],     **_lowercase,     )     ->							Dict:
											# Mask token behave like a normal word, i.e. include the space before it
											SCREAMING_SNAKE_CASE_				=				AddedToken(_lowercase,     lstrip=_lowercase,     rstrip=_lowercase       ) if isinstance(_lowercase,     _lowercase       ) else mask_token

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

											SCREAMING_SNAKE_CASE_				=				3
											SCREAMING_SNAKE_CASE_				=				do_lower_case
											SCREAMING_SNAKE_CASE_				=				remove_space
											SCREAMING_SNAKE_CASE_				=				keep_accents
											SCREAMING_SNAKE_CASE_				=				vocab_file
											SCREAMING_SNAKE_CASE_				=				False if not self.vocab_file else True



				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							List[int]:
											SCREAMING_SNAKE_CASE_				=				[self.sep_token_id]
											SCREAMING_SNAKE_CASE_				=				[self.cls_token_id]
											if token_ids_a is None:
																		return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls



				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							List[int]:
											SCREAMING_SNAKE_CASE_				=				[self.sep_token_id]
											SCREAMING_SNAKE_CASE_				=				[2]

											if token_ids_a is None:
																		return len(token_ids_a + sep       ) * [0] + cls_segment_id
											return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1] + cls_segment_id





				def   a__					(    self,     _lowercase,     _lowercase = 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(_lowercase       ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
																		return
											SCREAMING_SNAKE_CASE_				=				os.path.join(
											    _lowercase,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

											return (out_vocab_file,)








'''simple docstring'''

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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class    snake_case     :






				"""simple docstring"""
				# setable values
				_a    	=     None
				_a    	=     None
				_a    	=     None  # sigma(t_i)



				@classmethod
				def   a__					(    cls       )     ->							Dict:
											return cls()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     42
				_a    	=     42
				_a    	=     42






class    snake_case     (      lowercase_,						lowercase_		):






				"""simple docstring"""



				@property
				def   a__					(    self       )     ->							int:
											return True



				@register_to_config
				def __init__(    self,     _lowercase = 0.02,     _lowercase = 100,     _lowercase = 1.007,     _lowercase = 80,     _lowercase = 0.05,     _lowercase = 50,     )     ->							List[Any]:
											pass



				def   a__					(    self       )     ->							List[Any]:
											return KarrasVeSchedulerState.create()



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase = ()       )     ->							KarrasVeSchedulerState:
											SCREAMING_SNAKE_CASE_				=				jnp.arange(0,     _lowercase       )[::-1].copy()
											SCREAMING_SNAKE_CASE_				=				[
											    (
											        self.config.sigma_max**2
											        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
											    )
											    for i in timesteps
											]

											return state.replace(
											    num_inference_steps=_lowercase,     schedule=jnp.array(_lowercase,     dtype=jnp.floataa       ),     timesteps=_lowercase,     )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     )     ->							Tuple[jnp.ndarray, float]:
											if self.config.s_min <= sigma <= self.config.s_max:
																		SCREAMING_SNAKE_CASE_				=				min(self.config.s_churn / state.num_inference_steps,     2**0.5 - 1       )
											else:
																		SCREAMING_SNAKE_CASE_				=				0

											# sample eps ~ N(0, S_noise^2 * I)
											SCREAMING_SNAKE_CASE_				=				random.split(_lowercase,     num=1       )
											SCREAMING_SNAKE_CASE_				=				self.config.s_noise * random.normal(key=_lowercase,     shape=sample.shape       )
											SCREAMING_SNAKE_CASE_				=				sigma + gamma * sigma
											SCREAMING_SNAKE_CASE_				=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

											return sample_hat, sigma_hat



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_hat + sigma_hat * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_hat - pred_original_sample) / sigma_hat
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * derivative

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase = True,     )     ->							Union[FlaxKarrasVeOutput, Tuple]:
											SCREAMING_SNAKE_CASE_				=				sample_prev + sigma_prev * model_output
											SCREAMING_SNAKE_CASE_				=				(sample_prev - pred_original_sample) / sigma_prev
											SCREAMING_SNAKE_CASE_				=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

											if not return_dict:
																		return (sample_prev, derivative, state)

											return FlaxKarrasVeOutput(prev_sample=_lowercase,     derivative=_lowercase,     state=_lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											raise NotImplementedError()





