import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_UpperCAmelCase						=		logging.getLogger(__name__)





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    =					4_2
				lowerCamelCase_    =					4_2
				lowerCamelCase_    =					4_2





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    =					4_2
				lowerCamelCase_    =					4_2
				lowerCamelCase_    =					None
				lowerCamelCase_    =					None





class UpperCAmelCase						(					__SCREAMING_SNAKE_CASE  ):


				'''simple docstring'''






				lowerCamelCase_    =					'''train'''
				lowerCamelCase_    =					'''dev'''
				lowerCamelCase_    =					'''test'''





class UpperCAmelCase						:


				'''simple docstring'''






				@staticmethod
				def   lowerCAmelCase_   (						lowercase		,	lowercase					):



						"""simple docstring"""





						raise NotImplementedError
				@staticmethod
				def   lowerCAmelCase_   (						lowercase					):



						"""simple docstring"""





						raise NotImplementedError







				@staticmethod
				def   lowerCAmelCase_   (						lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase=False		,	lowercase="[CLS]"		,	lowercase=1		,	lowercase="[SEP]"		,	lowercase=False		,	lowercase=False		,	lowercase=0		,	lowercase=0		,	lowercase=-1_0_0		,	lowercase=0		,	lowercase=True		,	):



						"""simple docstring"""





						A_		:				Any     =      {label: i for i, label in enumerate(lowercase					)}

						A_		:				str     =      []
						for ex_index, example in enumerate(lowercase					):
								if ex_index % 1_0_0_0_0 == 0:
										logger.info('Writing example %d of %d'		,	lowercase		,	len(lowercase					)					)

								A_		:				Dict     =      []
								A_		:				List[Any]     =      []
								for word, label in zip(example.words		,	example.labels					):
										A_		:				Any     =      tokenizer.tokenize(lowercase					)

										# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
										if len(lowercase					) > 0:
												tokens.extend(lowercase					)
												# Use the real label id for the first token of the word, and padding ids for the remaining tokens
												label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase					) - 1)					)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
								A_		:				str     =      tokenizer.num_special_tokens_to_add()
								if len(lowercase					) > max_seq_length - special_tokens_count:
										A_		:				Any     =      tokens[: (max_seq_length - special_tokens_count)]
										A_		:				List[Any]     =      label_ids[: (max_seq_length - special_tokens_count)]

								# The convention in BERT is:
								# (a) For sequence pairs:
								#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
								#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
								# (b) For single sequences:
								#  tokens:   [CLS] the dog is hairy . [SEP]
								#  type_ids:   0   0   0   0  0     0   0
								#
								# Where "type_ids" are used to indicate whether this is the first
								# sequence or the second sequence. The embedding vectors for `type=0` and
								# `type=1` were learned during pre-training and are added to the wordpiece
								# embedding vector (and position vector). This is not *strictly* necessary
								# since the [SEP] token unambiguously separates the sequences, but it makes
								# it easier for the model to learn the concept of sequences.
								#
								# For classification tasks, the first vector (corresponding to [CLS]) is
								# used as the "sentence vector". Note that this only makes sense because
								# the entire model is fine-tuned.
								tokens += [sep_token]
								label_ids += [pad_token_label_id]
								if sep_token_extra:
										# roberta uses an extra separator b/w pairs of sentences
										tokens += [sep_token]
										label_ids += [pad_token_label_id]
								A_		:				Union[str, Any]     =      [sequence_a_segment_id] * len(lowercase					)

								if cls_token_at_end:
										tokens += [cls_token]
										label_ids += [pad_token_label_id]
										segment_ids += [cls_token_segment_id]
								else:
										A_		:				Dict     =      [cls_token] + tokens
										A_		:				Union[str, Any]     =      [pad_token_label_id] + label_ids
										A_		:				Union[str, Any]     =      [cls_token_segment_id] + segment_ids

								A_		:				Union[str, Any]     =      tokenizer.convert_tokens_to_ids(lowercase					)

								# The mask has 1 for real tokens and 0 for padding tokens. Only real
								# tokens are attended to.
								A_		:				Tuple     =      [1 if mask_padding_with_zero else 0] * len(lowercase					)

								# Zero-pad up to the sequence length.
								A_		:				int     =      max_seq_length - len(lowercase					)
								if pad_on_left:
										A_		:				Union[str, Any]     =      ([pad_token] * padding_length) + input_ids
										A_		:				Optional[Any]     =      ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
										A_		:				int     =      ([pad_token_segment_id] * padding_length) + segment_ids
										A_		:				Tuple     =      ([pad_token_label_id] * padding_length) + label_ids
								else:
										input_ids += [pad_token] * padding_length
										input_mask += [0 if mask_padding_with_zero else 1] * padding_length
										segment_ids += [pad_token_segment_id] * padding_length
										label_ids += [pad_token_label_id] * padding_length

								assert len(lowercase					) == max_seq_length
								assert len(lowercase					) == max_seq_length
								assert len(lowercase					) == max_seq_length
								assert len(lowercase					) == max_seq_length

								if ex_index < 5:
										logger.info('*** Example ***'					)
										logger.info('guid: %s'		,	example.guid					)
										logger.info('tokens: %s'		,	' '.join([str(lowercase					) for x in tokens]					)					)
										logger.info('input_ids: %s'		,	' '.join([str(lowercase					) for x in input_ids]					)					)
										logger.info('input_mask: %s'		,	' '.join([str(lowercase					) for x in input_mask]					)					)
										logger.info('segment_ids: %s'		,	' '.join([str(lowercase					) for x in segment_ids]					)					)
										logger.info('label_ids: %s'		,	' '.join([str(lowercase					) for x in label_ids]					)					)

								if "token_type_ids" not in tokenizer.model_input_names:
										A_		:				str     =      None

								features.append(
								    InputFeatures(
								        input_ids=lowercase		,	attention_mask=lowercase		,	token_type_ids=lowercase		,	label_ids=lowercase					)					)
						return features


if is_torch_available():
	import torch
	from torch import nn
	from torch.utils.data import Dataset





	class UpperCAmelCase						(					__SCREAMING_SNAKE_CASE  ):


					'''simple docstring'''






					lowerCamelCase_    =					4_2
					lowerCamelCase_    =					nn.CrossEntropyLoss().ignore_index
					def __init__(						self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase = None		,	lowercase=False		,	lowercase = Split.train		,	):



							"""simple docstring"""





							A_		:				Tuple     =      os.path.join(
							    lowercase		,	'cached_{}_{}_{}'.format(mode.value		,	tokenizer.__class__.__name__		,	str(lowercase					)					)		,	)

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							A_		:				Tuple     =      cached_features_file + '.lock'
							with FileLock(lowercase					):
									if os.path.exists(lowercase					) and not overwrite_cache:
											logger.info(F'''Loading features from cached file {cached_features_file}'''					)
											A_		:				Dict     =      torch.load(lowercase					)
									else:
											logger.info(F'''Creating features from dataset file at {data_dir}'''					)
											A_		:				str     =      token_classification_task.read_examples_from_file(lowercase		,	lowercase					)
											# TODO clean up all this to leverage built-in features of tokenizers
											A_		:				Optional[Any]     =      token_classification_task.convert_examples_to_features(
											    lowercase		,	lowercase		,	lowercase		,	lowercase		,	cls_token_at_end=bool(model_type in ['xlnet']					)		,	cls_token=tokenizer.cls_token		,	cls_token_segment_id=2 if model_type in ['xlnet'] else 0		,	sep_token=tokenizer.sep_token		,	sep_token_extra=lowercase		,	pad_on_left=bool(tokenizer.padding_side == 'left'					)		,	pad_token=tokenizer.pad_token_id		,	pad_token_segment_id=tokenizer.pad_token_type_id		,	pad_token_label_id=self.pad_token_label_id		,	)
											logger.info(F'''Saving features into cached file {cached_features_file}'''					)
											torch.save(self.features		,	lowercase					)
					def __len__(						self					):



							"""simple docstring"""





							return len(self.features					)







					def __getitem__(						self		,	lowercase					):



							"""simple docstring"""





							return self.features[i]


if is_tf_available():
	import tensorflow as tf





	class UpperCAmelCase						:


					'''simple docstring'''






					lowerCamelCase_    =					4_2
					lowerCamelCase_    =					-1_0_0
					def __init__(						self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase = None		,	lowercase=False		,	lowercase = Split.train		,	):



							"""simple docstring"""





							A_		:				Dict     =      token_classification_task.read_examples_from_file(lowercase		,	lowercase					)
							# TODO clean up all this to leverage built-in features of tokenizers
							A_		:				Union[str, Any]     =      token_classification_task.convert_examples_to_features(
							    lowercase		,	lowercase		,	lowercase		,	lowercase		,	cls_token_at_end=bool(model_type in ['xlnet']					)		,	cls_token=tokenizer.cls_token		,	cls_token_segment_id=2 if model_type in ['xlnet'] else 0		,	sep_token=tokenizer.sep_token		,	sep_token_extra=lowercase		,	pad_on_left=bool(tokenizer.padding_side == 'left'					)		,	pad_token=tokenizer.pad_token_id		,	pad_token_segment_id=tokenizer.pad_token_type_id		,	pad_token_label_id=self.pad_token_label_id		,	)

							def gen():
									for ex in self.features:
											if ex.token_type_ids is None:
													yield (
													    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
													    ex.label_ids,
													)
											else:
													yield (
													    {
													        "input_ids": ex.input_ids,
													        "attention_mask": ex.attention_mask,
													        "token_type_ids": ex.token_type_ids,
													    },
													    ex.label_ids,
													)

							if "token_type_ids" not in tokenizer.model_input_names:
									A_		:				Dict     =      tf.data.Dataset.from_generator(
									    lowercase		,	({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)		,	(
									        {'input_ids': tf.TensorShape([None]					), 'attention_mask': tf.TensorShape([None]					)},
									        tf.TensorShape([None]					),
									    )		,	)
							else:
									A_		:				List[Any]     =      tf.data.Dataset.from_generator(
									    lowercase		,	({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)		,	(
									        {
									            'input_ids': tf.TensorShape([None]					),
									            'attention_mask': tf.TensorShape([None]					),
									            'token_type_ids': tf.TensorShape([None]					),
									        },
									        tf.TensorShape([None]					),
									    )		,	)
					def   lowerCAmelCase_   (						self					):



							"""simple docstring"""





							A_		:				Tuple     =      self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

							return self.dataset
					def __len__(						self					):



							"""simple docstring"""





							return len(self.features					)







					def __getitem__(						self		,	lowercase					):



							"""simple docstring"""





							return self.features[i]








import os
import re
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


SCREAMING_SNAKE_CASE							:List[Any]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:int							 =				{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

SCREAMING_SNAKE_CASE							:int							 =				{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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






	def __init__(       self					: Any							,A					: List[str]							,A					: str="<unk>"							,A					: int="<s>"							,A					: Union[str, Any]="</s>"							,A					: List[str]="<pad>"							,A					: int="[SEP]"							,A					: Optional[Any]="[MASK]"							,A					: Tuple="[CLS]"							,A					: Optional[Dict[str, Any]] = None							,**A					: Any							,):
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else bos_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else eos_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else unk_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else pad_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else cls_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else sep_token

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

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,pad_token=A							,sep_token=A							,mask_token=A							,cls_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A   			= vocab_file

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(A )






	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					return self.sp_model.get_piece_size()






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def __getstate__(       self					: Optional[int] ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: str							,A					: Optional[Any] ):
					__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					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Tuple ):
					return self.sp_model.piece_to_id(A )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Tuple ):
					__A   			= self.sp_model.IdToPiece(A )
					return token






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: int ):
					__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(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def 				UpperCamelCase_  (       self					: Tuple							,A					: List[int]							,A					: bool = False							,A					: bool = None							,A					: bool = True							,**A					: Union[str, Any]							,):
					__A   			= kwargs.pop("use_source_tokenizer"							,A )

					__A   			= self.convert_ids_to_tokens(A							,skip_special_tokens=A )

					# 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(A ) )
																	__A   			= []
													sub_texts.append(A )
									else:
													current_sub_text.append(A )
					if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A ) )

					# Mimic the behavior of the Rust tokenizer:
					# No space before [MASK] and [SEP]
					if spaces_between_special_tokens:
									__A   			= re.sub(R" (\[(MASK|SEP)\])"							,R"\1"							," ".join(A ) )
					else:
									__A   			= "".join(A )

					__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(A )
									return clean_text
					else:
									return text






	def 				UpperCamelCase_  (       self					: str							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file							,A )
					elif not os.path.isfile(self.vocab_file ):
									with open(A							,"wb" ) as fi:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None ):
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A   			= [self.cls_token_id]
					__A   			= [self.sep_token_id]
					return cls + token_ids_a + sep + token_ids_a + sep






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A )

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







	def 				UpperCamelCase_  (       self					: Any							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__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]
