



import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


UpperCAmelCase__			:   str      			=    logging.get_logger(__name__)





@dataclass
class 							lowerCAmelCase_		:
		SCREAMING_SNAKE_CASE_       :			str
		SCREAMING_SNAKE_CASE_       :			str
		SCREAMING_SNAKE_CASE_       :			Optional[str]					       =	None
		SCREAMING_SNAKE_CASE_       :			Optional[str]					       =	None







		def 					a_  (    self  :   List[str]					)       ->		Union[str, Any]:


						'''simple docstring'''







						return json.dumps(dataclasses.asdict(self					)       ,		indent=2					) + "\n"





@dataclass(frozen=lowercase_     )
class 							lowerCAmelCase_		:
		SCREAMING_SNAKE_CASE_       :			List[int]
		SCREAMING_SNAKE_CASE_       :			Optional[List[int]]					       =	None
		SCREAMING_SNAKE_CASE_       :			Optional[List[int]]					       =	None
		SCREAMING_SNAKE_CASE_       :			Optional[Union[int, float]]					       =	None







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


						'''simple docstring'''







						return json.dumps(dataclasses.asdict(self					)					) + "\n"





class 							lowerCAmelCase_		:







		def 					a_  (    self  :   str       ,		UpperCAmelCase_  :   int					)       ->		List[Any]:


						'''simple docstring'''







						raise NotImplementedError()







		def 					a_  (    self  :   Dict       ,		UpperCAmelCase_  :   List[str]					)       ->		Union[str, Any]:


						'''simple docstring'''







						raise NotImplementedError()







		def 					a_  (    self  :   List[str]       ,		UpperCAmelCase_  :   Any					)       ->		Any:


						'''simple docstring'''







						raise NotImplementedError()







		def 					a_  (    self  :   List[str]       ,		UpperCAmelCase_  :   str					)       ->		Tuple:


						'''simple docstring'''







						raise NotImplementedError()







		def 					a_  (    self  :   Optional[int]					)       ->		int:


						'''simple docstring'''







						raise NotImplementedError()







		def 					a_  (    self  :   Union[str, Any]       ,		UpperCAmelCase_  :   List[str]					)       ->		Any:


						'''simple docstring'''







						if len(self.get_labels()					) > 1:
										_UpperCAmelCase						:  Optional[int]     					=	self.get_labels()[int(example.label					)]
						return example







		@classmethod
		def 					a_  (    cls  :   List[str]       ,		UpperCAmelCase_  :   Optional[int]       ,		UpperCAmelCase_  :   Union[str, Any]=None					)       ->		str:


						'''simple docstring'''







						with open(UpperCAmelCase_       ,		'''r'''       ,		encoding='''utf-8-sig'''					) as f:
										return list(csv.reader(UpperCAmelCase_       ,		delimiter='''\t'''       ,		quotechar=UpperCAmelCase_					)					)





class 							lowerCAmelCase_		(		lowercase_     ):







		def __init__(    self  :   Dict       ,		UpperCAmelCase_  :   Optional[Any]=None       ,		UpperCAmelCase_  :   Dict=None       ,		UpperCAmelCase_  :   int="classification"       ,		UpperCAmelCase_  :   Tuple=False					)       ->		str:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	[] if labels is None else labels
						_UpperCAmelCase						:  int     					=	[] if examples is None else examples
						_UpperCAmelCase						:  Union[str, Any]     					=	mode
						_UpperCAmelCase						:  Optional[Any]     					=	verbose







		def __len__(    self  :   Tuple					)       ->		Optional[Any]:


						'''simple docstring'''







						return len(self.examples					)







		def __getitem__(    self  :   List[str]       ,		UpperCAmelCase_  :   Any					)       ->		Optional[int]:


						'''simple docstring'''







						if isinstance(UpperCAmelCase_       ,		UpperCAmelCase_					):
										return SingleSentenceClassificationProcessor(labels=self.labels       ,		examples=self.examples[idx]					)
						return self.examples[idx]







		@classmethod
		def 					a_  (    cls  :   Tuple       ,		UpperCAmelCase_  :   Any       ,		UpperCAmelCase_  :   Optional[Any]=""       ,		UpperCAmelCase_  :   Optional[int]=0       ,		UpperCAmelCase_  :   int=1       ,		UpperCAmelCase_  :   List[str]=None       ,		UpperCAmelCase_  :   List[str]=False       ,		**UpperCAmelCase_  :   List[str]					)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	cls(**UpperCAmelCase_					)
						processor.add_examples_from_csv(
						    UpperCAmelCase_       ,		split_name=UpperCAmelCase_       ,		column_label=UpperCAmelCase_       ,		column_text=UpperCAmelCase_       ,		column_id=UpperCAmelCase_       ,		skip_first_row=UpperCAmelCase_       ,		overwrite_labels=UpperCAmelCase_       ,		overwrite_examples=UpperCAmelCase_       ,		)
						return processor







		@classmethod
		def 					a_  (    cls  :   int       ,		UpperCAmelCase_  :   Any       ,		UpperCAmelCase_  :   List[Any]=None       ,		**UpperCAmelCase_  :   str					)       ->		str:


						'''simple docstring'''







						_UpperCAmelCase						:  int     					=	cls(**UpperCAmelCase_					)
						processor.add_examples(UpperCAmelCase_       ,		labels=UpperCAmelCase_					)
						return processor







		def 					a_  (    self  :   Tuple       ,		UpperCAmelCase_  :   Any       ,		UpperCAmelCase_  :   str=""       ,		UpperCAmelCase_  :   Dict=0       ,		UpperCAmelCase_  :   Optional[int]=1       ,		UpperCAmelCase_  :   int=None       ,		UpperCAmelCase_  :   str=False       ,		UpperCAmelCase_  :   Union[str, Any]=False       ,		UpperCAmelCase_  :   Dict=False       ,		)       ->		Optional[Any]:


						'''simple docstring'''







						_UpperCAmelCase						:  Dict     					=	self._read_tsv(UpperCAmelCase_					)
						if skip_first_row:
										_UpperCAmelCase						:  Union[str, Any]     					=	lines[1:]
						_UpperCAmelCase						:  int     					=	[]
						_UpperCAmelCase						:  Optional[int]     					=	[]
						_UpperCAmelCase						:  int     					=	[]
						for i, line in enumerate(UpperCAmelCase_					):
										texts.append(line[column_text]					)
										labels.append(line[column_label]					)
										if column_id is not None:
														ids.append(line[column_id]					)
										else:
														_UpperCAmelCase						:  Tuple     					=	F'''{split_name}-{i}''' if split_name else str(UpperCAmelCase_					)
														ids.append(UpperCAmelCase_					)

						return self.add_examples(
						    UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_       ,		overwrite_labels=UpperCAmelCase_       ,		overwrite_examples=UpperCAmelCase_					)







		def 					a_  (    self  :   List[Any]       ,		UpperCAmelCase_  :   Tuple       ,		UpperCAmelCase_  :   int=None       ,		UpperCAmelCase_  :   Optional[int]=None       ,		UpperCAmelCase_  :   List[str]=False       ,		UpperCAmelCase_  :   str=False					)       ->		Optional[Any]:


						'''simple docstring'''







						if labels is not None and len(UpperCAmelCase_					) != len(UpperCAmelCase_					):
										raise ValueError(
										    F'''Text and labels have mismatched lengths {len(UpperCAmelCase_					)} and {len(UpperCAmelCase_					)}'''					)
						if ids is not None and len(UpperCAmelCase_					) != len(UpperCAmelCase_					):
										raise ValueError(F'''Text and ids have mismatched lengths {len(UpperCAmelCase_					)} and {len(UpperCAmelCase_					)}'''					)
						if ids is None:
										_UpperCAmelCase						:  Any     					=	[None] * len(UpperCAmelCase_					)
						if labels is None:
										_UpperCAmelCase						:  List[str]     					=	[None] * len(UpperCAmelCase_					)
						_UpperCAmelCase						:  Union[str, Any]     					=	[]
						_UpperCAmelCase						:  int     					=	set()
						for text_or_text_and_label, label, guid in zip(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					):
										if isinstance(UpperCAmelCase_       ,		(tuple, list)					) and label is None:
														_UpperCAmelCase						:  Dict     					=	text_or_text_and_label
										else:
														_UpperCAmelCase						:  Optional[int]     					=	text_or_text_and_label
										added_labels.add(UpperCAmelCase_					)
										examples.append(InputExample(guid=UpperCAmelCase_       ,		text_a=UpperCAmelCase_       ,		text_b=UpperCAmelCase_       ,		label=UpperCAmelCase_					)					)

						# Update examples
						if overwrite_examples:
										_UpperCAmelCase						:  Any     					=	examples
						else:
										self.examples.extend(UpperCAmelCase_					)

						# Update labels
						if overwrite_labels:
										_UpperCAmelCase						:  List[str]     					=	list(UpperCAmelCase_					)
						else:
										_UpperCAmelCase						:  Optional[int]     					=	list(set(self.labels					).union(UpperCAmelCase_					)					)

						return self.examples







		def 					a_  (    self  :   List[str]       ,		UpperCAmelCase_  :   str       ,		UpperCAmelCase_  :   Union[str, Any]=None       ,		UpperCAmelCase_  :   List[Any]=False       ,		UpperCAmelCase_  :   List[str]=0       ,		UpperCAmelCase_  :   List[Any]=True       ,		UpperCAmelCase_  :   str=None       ,		)       ->		Union[str, Any]:


						'''simple docstring'''







						if max_length is None:
										_UpperCAmelCase						:  Any     					=	tokenizer.max_len

						_UpperCAmelCase						:  List[Any]     					=	{label: i for i, label in enumerate(self.labels					)}

						_UpperCAmelCase						:  Union[str, Any]     					=	[]
						for ex_index, example in enumerate(self.examples					):
										if ex_index % 10000 == 0:
														logger.info(F'''Tokenizing example {ex_index}'''					)

										_UpperCAmelCase						:  Optional[int]     					=	tokenizer.encode(
										    example.text_a       ,		add_special_tokens=UpperCAmelCase_       ,		max_length=min(UpperCAmelCase_       ,		tokenizer.max_len					)       ,		)
										all_input_ids.append(UpperCAmelCase_					)

						_UpperCAmelCase						:  str     					=	max(len(UpperCAmelCase_					) for input_ids in all_input_ids					)

						_UpperCAmelCase						:  Optional[Any]     					=	[]
						for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase_       ,		self.examples					)					):
										if ex_index % 10000 == 0:
														logger.info(F'''Writing example {ex_index}/{len(self.examples					)}'''					)
										# The mask has 1 for real tokens and 0 for padding tokens. Only real
										# tokens are attended to.
										_UpperCAmelCase						:  Any     					=	[1 if mask_padding_with_zero else 0] * len(UpperCAmelCase_					)

										# Zero-pad up to the sequence length.
										_UpperCAmelCase						:  Dict     					=	batch_length - len(UpperCAmelCase_					)
										if pad_on_left:
														_UpperCAmelCase						:  Dict     					=	([pad_token] * padding_length) + input_ids
														_UpperCAmelCase						:  Any     					=	([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
										else:
														_UpperCAmelCase						:  Optional[Any]     					=	input_ids + ([pad_token] * padding_length)
														_UpperCAmelCase						:  int     					=	attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

										if len(UpperCAmelCase_					) != batch_length:
														raise ValueError(F'''Error with input length {len(UpperCAmelCase_					)} vs {batch_length}'''					)
										if len(UpperCAmelCase_					) != batch_length:
														raise ValueError(F'''Error with input length {len(UpperCAmelCase_					)} vs {batch_length}'''					)

										if self.mode == "classification":
														_UpperCAmelCase						:  Dict     					=	label_map[example.label]
										elif self.mode == "regression":
														_UpperCAmelCase						:  Optional[int]     					=	float(example.label					)
										else:
														raise ValueError(self.mode					)

										if ex_index < 5 and self.verbose:
														logger.info('''*** Example ***'''					)
														logger.info(F'''guid: {example.guid}'''					)
														logger.info(F'''input_ids: {' '.join([str(UpperCAmelCase_					) for x in input_ids]					)}'''					)
														logger.info(F'''attention_mask: {' '.join([str(UpperCAmelCase_					) for x in attention_mask]					)}'''					)
														logger.info(F'''label: {example.label} (id = {label})'''					)

										features.append(InputFeatures(input_ids=UpperCAmelCase_       ,		attention_mask=UpperCAmelCase_       ,		label=UpperCAmelCase_					)					)

						if return_tensors is None:
										return features
						elif return_tensors == "tf":
										if not is_tf_available():
														raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''					)
										import tensorflow as tf

										def gen():
														for ex in features:
																		yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

										_UpperCAmelCase						:  Tuple     					=	tf.data.Dataset.from_generator(
										    UpperCAmelCase_       ,		({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)       ,		({'''input_ids''': tf.TensorShape([None]					), '''attention_mask''': tf.TensorShape([None]					)}, tf.TensorShape([]					))       ,		)
										return dataset
						elif return_tensors == "pt":
										if not is_torch_available():
														raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''					)
										import torch
										from torch.utils.data import TensorDataset

										_UpperCAmelCase						:  int     					=	torch.tensor([f.input_ids for f in features]       ,		dtype=torch.long					)
										_UpperCAmelCase						:  str     					=	torch.tensor([f.attention_mask for f in features]       ,		dtype=torch.long					)
										if self.mode == "classification":
														_UpperCAmelCase						:  Tuple     					=	torch.tensor([f.label for f in features]       ,		dtype=torch.long					)
										elif self.mode == "regression":
														_UpperCAmelCase						:  Any     					=	torch.tensor([f.label for f in features]       ,		dtype=torch.float					)

										_UpperCAmelCase						:  Any     					=	TensorDataset(UpperCAmelCase_       ,		UpperCAmelCase_       ,		UpperCAmelCase_					)
										return dataset
						else:
										raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)





"""simple docstring"""







import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCAmelCase:  List[str]			     =				logging.getLogger(__name__)


def  __SCREAMING_SNAKE_CASE (  ):
		_lowercase				: List[Any]     =   argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""      )
		parser.add_argument(
		    """--dataset_name"""   ,				type=__UpperCAmelCase   ,				default="""wikitext"""   ,				help="""Name of the training. Explore datasets at: hf.co/datasets."""   ,				)
		parser.add_argument(
		    """--dataset_config"""   ,				type=__UpperCAmelCase   ,				default="""wikitext-103-raw-v1"""   ,				help="""Configuration name of the dataset."""      )
		parser.add_argument(
		    """--batch_size"""   ,				type=__UpperCAmelCase   ,				default=1000   ,				help="""Batch size during training."""   ,				)
		parser.add_argument(
		    """--vocab_size"""   ,				type=__UpperCAmelCase   ,				default=10048   ,				help="""Size of the desired vocabulary."""   ,				)
		parser.add_argument(
		    """--limit"""   ,				default=__UpperCAmelCase   ,				type=__UpperCAmelCase   ,				help="""Limit the number of shards (used for debugging)."""   ,				)
		parser.add_argument(
		    """--export_to_hub"""   ,				action="""store_true"""   ,				)

		_lowercase				: Any     =   parser.parse_args()
		return args


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase      ):
		_lowercase				: List[Any]     =   datasets.load_dataset(args.dataset_name   ,				args.dataset_config   ,				split="""train"""      )

		if args.limit is not None:
				_lowercase				: Tuple     =   min(len(__UpperCAmelCase      )   ,				args.limit      )
				_lowercase				: Optional[Any]     =   dataset.select(range(__UpperCAmelCase      )      )
				logger.info(F"""Limiting the dataset to {args.limit} entries."""      )

		def batch_iterator():
				for i in range(0   ,				len(__UpperCAmelCase      )   ,				args.batch_size      ):
						yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
		_lowercase				: Optional[int]     =   Tokenizer(Unigram()      )
		_lowercase				: List[str]     =   normalizers.Sequence([normalizers.Replace("""``"""   ,				"""\""""      ), normalizers.Replace("""''"""   ,				"""\""""      )]      )
		_lowercase				: str     =   pre_tokenizers.Metaspace()

		# Prepare the trainer.
		_lowercase				: int     =   UnigramTrainer(
		    unk_token="""<unk>"""   ,				special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""]   ,				vocab_size=args.vocab_size   ,				)

		logger.info("""Training the tokenizer."""      )
		tokenizer.train_from_iterator(batch_iterator()   ,				trainer=__UpperCAmelCase      )
		logger.info("""Tokenizer training complete!"""      )

		_lowercase				: int     =   tokenizer.token_to_id("""[CLS]"""      )
		_lowercase				: Optional[Any]     =   tokenizer.token_to_id("""[SEP]"""      )
		_lowercase				: Optional[int]     =   processors.TemplateProcessing(
		    single="""[CLS]:0 $A:0 [SEP]:0"""   ,				pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1"""   ,				special_tokens=[
		        ("""[CLS]""", cls_token_id),
		        ("""[SEP]""", sep_token_id),
		    ]   ,				)
		_lowercase				: Union[str, Any]     =   decoders.Metaspace()

		if args.export_to_hub:
				logger.info("""Exporting the trained tokenzier to Hub."""      )
				_lowercase				: List[Any]     =   AlbertTokenizerFast(tokenizer_object=__UpperCAmelCase      )
				new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""      )


if __name__ == "__main__":
		UpperCAmelCase:  List[Any]			     =				parse_args()
		main(args)



