






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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xglm import XGLMTokenizer
else:
		_a       =    None


_a       =    logging.get_logger(__name__)

_a       =    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

_a       =    {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
    },
}

_a       =    {
    '''facebook/xglm-564M''': 2048,
}





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	UpperCamelCase__								= VOCAB_FILES_NAMES
	UpperCamelCase__								= PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase__								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCamelCase__								= ["input_ids", "attention_mask"]
	UpperCamelCase__								= XGLMTokenizer

	def __init__(				self			,       UpperCAmelCase=None			,       UpperCAmelCase=None			,       UpperCAmelCase="<s>"			,       UpperCAmelCase="</s>"			,       UpperCAmelCase="</s>"			,       UpperCAmelCase="<s>"			,       UpperCAmelCase="<unk>"			,       UpperCAmelCase="<pad>"			,       **UpperCAmelCase			,       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     7
							_UpperCAmelCase							   =     [F"""<madeupword{i}>""" for i in range(self.num_madeup_words       )]

							_UpperCAmelCase							   =     kwargs.get('additional_special_tokens'			,       []       )
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    UpperCAmelCase			,       tokenizer_file=UpperCAmelCase			,       bos_token=UpperCAmelCase			,       eos_token=UpperCAmelCase			,       sep_token=UpperCAmelCase			,       cls_token=UpperCAmelCase			,       unk_token=UpperCAmelCase			,       pad_token=UpperCAmelCase			,       **UpperCAmelCase			,       )

							_UpperCAmelCase							   =     vocab_file
							_UpperCAmelCase							   =     False if not self.vocab_file else True

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase = None       ):
							"""simple docstring"""







							if token_ids_a is None:
													return [self.sep_token_id] + token_ids_a
							_UpperCAmelCase							   =     [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase = None       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     [self.sep_token_id]

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

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase = None       ):
							"""simple docstring"""







							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(UpperCAmelCase       ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""       )
													return
							_UpperCAmelCase							   =     os.path.join(
							    UpperCAmelCase			,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

							return (out_vocab_file,)








import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCAmelCase							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 2:
												raise ValueError('Minimum class count error'      )
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
						_UpperCAmelCase							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						mode='clip'      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
