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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_      =      logging.get_logger(__name__)

A_      =      {"vocab_file": "spiece.model"}

A_      =      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

A_      =      {"bert_for_seq_generation": 512}






class      __lowercase					(    _A    ):
						lowercase					 =     VOCAB_FILES_NAMES
						lowercase					 =     PRETRAINED_VOCAB_FILES_MAP
						lowercase					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase					 =     []
						lowercase					 =     ['input_ids', 'attention_mask']

						def __init__(	self				:      str							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      str="<s>"							,							__lowerCamelCase				:      Tuple="</s>"							,							__lowerCamelCase				:      int="<unk>"							,							__lowerCamelCase				:      List[Any]="<pad>"							,							__lowerCamelCase				:      Optional[int]="<::::>"							,							__lowerCamelCase				:      Optional[Dict[str, Any]] = None							,							**__lowerCamelCase				:      List[Any]							,							)							->		None:


									'''simple docstring'''

									lowercase							=		{} if sp_model_kwargs is None else sp_model_kwargs

									# Add extra_ids to the special token list
									super().__init__(
									    bos_token=__lowerCamelCase							,							eos_token=__lowerCamelCase							,							unk_token=__lowerCamelCase							,							pad_token=__lowerCamelCase							,							sep_token=__lowerCamelCase							,							sp_model_kwargs=self.sp_model_kwargs							,							**__lowerCamelCase							,							)

									lowercase							=		vocab_file

									lowercase							=		spm.SentencePieceProcessor(**self.sp_model_kwargs   )
									self.sp_model.Load(__lowerCamelCase   )

						@property
						def        __a		(	self				:      List[Any]   )							->		int:


									'''simple docstring'''

									return self.sp_model.get_piece_size()

						def        __a		(	self				:      Optional[int]   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		{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				:      Tuple   )							->		Optional[int]:


									'''simple docstring'''

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

						def __setstate__(	self				:      Any							,							__lowerCamelCase				:      Optional[Any]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		d

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

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

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		List[str]:


									'''simple docstring'''

									return self.sp_model.encode(__lowerCamelCase							,							out_type=__lowerCamelCase   )

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Dict   )							->		Any:


									'''simple docstring'''

									return self.sp_model.piece_to_id(__lowerCamelCase   )

						def        __a		(	self				:      int							,							__lowerCamelCase				:      int   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.sp_model.IdToPiece(__lowerCamelCase   )
									return token

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									lowercase							=		''''''
									for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
															out_string += self.sp_model.decode(__lowerCamelCase   ) + token
															lowercase							=		[]
												else:
															current_sub_tokens.append(__lowerCamelCase   )
									out_string += self.sp_model.decode(__lowerCamelCase   )
									return out_string.strip()



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[str] = None   )							->		Tuple[str]:


									'''simple docstring'''

									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   ) 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:
															lowercase							=		self.sp_model.serialized_model_proto()
															fi.write(__lowerCamelCase   )

									return (out_vocab_file,)






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      __lowercase					:
						# setable values
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None  # sigma(t_i)

						@classmethod
						def        __a		(	cls				:      List[str]   )							->		Dict:


									'''simple docstring'''

									return cls()






@dataclass
class      __lowercase					(    _A    ):
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return True

						@register_to_config
						def __init__(	self				:      int							,							__lowerCamelCase				:      float = 0.02							,							__lowerCamelCase				:      float = 1_00							,							__lowerCamelCase				:      float = 1.007							,							__lowerCamelCase				:      float = 80							,							__lowerCamelCase				:      float = 0.05							,							__lowerCamelCase				:      float = 50							,							)							->		List[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									return KarrasVeSchedulerState.create()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple = ()   )							->		KarrasVeSchedulerState:


									'''simple docstring'''

									lowercase							=		jnp.arange(0							,							__lowerCamelCase   )[::-1].copy()
									lowercase							=		[
									    (
									        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=__lowerCamelCase							,							schedule=jnp.array(__lowerCamelCase							,							dtype=jnp.floataa   )							,							timesteps=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      random.KeyArray							,							)							->		Tuple[jnp.ndarray, float]:


									'''simple docstring'''

									if self.config.s_min <= sigma <= self.config.s_max:
												lowercase							=		min(self.config.s_churn / state.num_inference_steps							,							2**0.5 - 1   )
									else:
												lowercase							=		0

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

									return sample_hat, sigma_hat

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_hat + sigma_hat * model_output
									lowercase							=		(sample_hat - pred_original_sample) / sigma_hat
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * derivative

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

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

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_prev + sigma_prev * model_output
									lowercase							=		(sample_prev - pred_original_sample) / sigma_prev
									lowercase							=		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=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )



						def        __a		(	self				:      int							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									raise NotImplementedError()





