
"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_=1_0      ):
					for _ in tqdm(range(0,					args.n_examples,					SCREAMING_SNAKE_CASE_      )      ):
										yield [next(SCREAMING_SNAKE_CASE_      )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE_      )]


# Configuration
snake_case			       =		HfArgumentParser(TokenizerTrainingArguments)
snake_case			       =		parser.parse_args()

# Base tokenizer
snake_case			       =		AutoTokenizer.from_pretrained(args.base_tokenizer)
snake_case			       =		list(bytes_to_unicode().values())

# Load dataset
snake_case			       =		load_dataset(args.dataset_name, split='train', streaming=True)
snake_case			       =		iter(dataset)


# Training and saving
snake_case			       =		tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)



new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)


"""simple docstring"""




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


snake_case			       =		logging.get_logger(__name__)




@dataclass
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				Optional[str]						  =    None
				UpperCAmelCase_    :				Optional[str]						  =    None


				def 							A (    self      )   ->   int:
									"""simple docstring"""







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






@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				List[int]
				UpperCAmelCase_    :				Optional[List[int]]						  =    None
				UpperCAmelCase_    :				Optional[List[int]]						  =    None
				UpperCAmelCase_    :				Optional[Union[int, float]]						  =    None


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







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






class 					UpperCamelCase     :

				"""simple docstring"""



				def 							A (    self		,     lowercase__      )   ->   Union[str, Any]:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   int:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







									raise NotImplementedError()


				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







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




				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=None      )   ->   Dict:
									"""simple docstring"""







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






class 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				def __init__(    self		,     lowercase__=None		,     lowercase__=None		,     lowercase__="classification"		,     lowercase__=False      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     [] if labels is None else labels
									SCREAMING_SNAKE_CASE           =     [] if examples is None else examples
									SCREAMING_SNAKE_CASE           =     mode
									SCREAMING_SNAKE_CASE           =     verbose


				def __len__(    self      )   ->   Optional[int]:
									"""simple docstring"""







									return len(self.examples      )


				def __getitem__(    self		,     lowercase__      )   ->   List[Any]:
									"""simple docstring"""







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


				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=""		,     lowercase__=0		,     lowercase__=1		,     lowercase__=None		,     lowercase__=False		,     **lowercase__      )   ->   Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     cls(**lowercase__      )
									processor.add_examples_from_csv(
									    lowercase__		,     split_name=lowercase__		,     column_label=lowercase__		,     column_text=lowercase__		,     column_id=lowercase__		,     skip_first_row=lowercase__		,     overwrite_labels=lowercase__		,     overwrite_examples=lowercase__		,     )
									return processor


				@classmethod
				def 							A (    cls		,     lowercase__		,     lowercase__=None		,     **lowercase__      )   ->   int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     cls(**lowercase__      )
									processor.add_examples(lowercase__		,     labels=lowercase__      )
									return processor


				def 							A (    self		,     lowercase__		,     lowercase__=""		,     lowercase__=0		,     lowercase__=1		,     lowercase__=None		,     lowercase__=False		,     lowercase__=False		,     lowercase__=False		,     )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     self._read_tsv(lowercase__      )
									if skip_first_row:
														SCREAMING_SNAKE_CASE           =     lines[1:]
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									for i, line in enumerate(lowercase__      ):
														texts.append(line[column_text]      )
														labels.append(line[column_label]      )
														if column_id is not None:
																			ids.append(line[column_id]      )
														else:
																			SCREAMING_SNAKE_CASE           =     f'''{split_name}-{i}''' if split_name else str(lowercase__      )
																			ids.append(lowercase__      )

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


				def 							A (    self		,     lowercase__		,     lowercase__=None		,     lowercase__=None		,     lowercase__=False		,     lowercase__=False      )   ->   str:
									"""simple docstring"""







									if labels is not None and len(lowercase__      ) != len(lowercase__      ):
														raise ValueError(
														    f'''Text and labels have mismatched lengths {len(lowercase__      )} and {len(lowercase__      )}'''      )
									if ids is not None and len(lowercase__      ) != len(lowercase__      ):
														raise ValueError(f'''Text and ids have mismatched lengths {len(lowercase__      )} and {len(lowercase__      )}'''      )
									if ids is None:
														SCREAMING_SNAKE_CASE           =     [None] * len(lowercase__      )
									if labels is None:
														SCREAMING_SNAKE_CASE           =     [None] * len(lowercase__      )
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     set()
									for text_or_text_and_label, label, guid in zip(lowercase__		,     lowercase__		,     lowercase__      ):
														if isinstance(lowercase__		,     (tuple, list)      ) and label is None:
																			SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     text_or_text_and_label
														else:
																			SCREAMING_SNAKE_CASE           =     text_or_text_and_label
														added_labels.add(lowercase__      )
														examples.append(InputExample(guid=lowercase__		,     text_a=lowercase__		,     text_b=lowercase__		,     label=lowercase__      )      )

									# Update examples
									if overwrite_examples:
														SCREAMING_SNAKE_CASE           =     examples
									else:
														self.examples.extend(lowercase__      )

									# Update labels
									if overwrite_labels:
														SCREAMING_SNAKE_CASE           =     list(lowercase__      )
									else:
														SCREAMING_SNAKE_CASE           =     list(set(self.labels      ).union(lowercase__      )      )

									return self.examples




				def 							A (    self		,     lowercase__		,     lowercase__=None		,     lowercase__=False		,     lowercase__=0		,     lowercase__=True		,     lowercase__=None		,     )   ->   Optional[int]:
									"""simple docstring"""







									if max_length is None:
														SCREAMING_SNAKE_CASE           =     tokenizer.max_len

									SCREAMING_SNAKE_CASE           =     {label: i for i, label in enumerate(self.labels      )}

									SCREAMING_SNAKE_CASE           =     []
									for ex_index, example in enumerate(self.examples      ):
														if ex_index % 10000 == 0:
																			logger.info(f'''Tokenizing example {ex_index}'''      )

														SCREAMING_SNAKE_CASE           =     tokenizer.encode(
														    example.text_a		,     add_special_tokens=lowercase__		,     max_length=min(lowercase__		,     tokenizer.max_len      )		,     )
														all_input_ids.append(lowercase__      )

									SCREAMING_SNAKE_CASE           =     max(len(lowercase__      ) for input_ids in all_input_ids      )

									SCREAMING_SNAKE_CASE           =     []
									for ex_index, (input_ids, example) in enumerate(zip(lowercase__		,     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.
														SCREAMING_SNAKE_CASE           =     [1 if mask_padding_with_zero else 0] * len(lowercase__      )

														# Zero-pad up to the sequence length.
														SCREAMING_SNAKE_CASE           =     batch_length - len(lowercase__      )
														if pad_on_left:
																			SCREAMING_SNAKE_CASE           =     ([pad_token] * padding_length) + input_ids
																			SCREAMING_SNAKE_CASE           =     ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
														else:
																			SCREAMING_SNAKE_CASE           =     input_ids + ([pad_token] * padding_length)
																			SCREAMING_SNAKE_CASE           =     attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

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

														if self.mode == "classification":
																			SCREAMING_SNAKE_CASE           =     label_map[example.label]
														elif self.mode == "regression":
																			SCREAMING_SNAKE_CASE           =     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(lowercase__      ) for x in input_ids]      )}'''      )
																			logger.info(f'''attention_mask: {' '.join([str(lowercase__      ) for x in attention_mask]      )}'''      )
																			logger.info(f'''label: {example.label} (id = {label})'''      )

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

									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)

														SCREAMING_SNAKE_CASE           =     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([]      ))		,     )
														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

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

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