





'''simple docstring'''



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_pegasus import PegasusTokenizer
else:
	_SCREAMING_SNAKE_CASE   =      None


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   =      '''▁'''

_SCREAMING_SNAKE_CASE   =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE   =      {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

_SCREAMING_SNAKE_CASE   =      {
    '''google/pegasus-xsum''': 5_1_2,
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Any     =    VOCAB_FILES_NAMES
	a      :			Union[str, Any]     =    PRETRAINED_VOCAB_FILES_MAP
	a      :			Optional[Any]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a      :			int     =    PegasusTokenizer
	a      :			Union[str, Any]     =    ["input_ids", "attention_mask"]


	def __init__(self       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase="<pad>"       ,_lowerCamelCase="</s>"       ,_lowerCamelCase="<unk>"       ,_lowerCamelCase="<mask_2>"       ,_lowerCamelCase="<mask_1>"       ,_lowerCamelCase=None       ,_lowerCamelCase=103       ,**_lowerCamelCase       ,)							->							Any:







				'''simple docstring'''





				__lowercase			  =							offset

				if additional_special_tokens is not None:
							if not isinstance(_lowerCamelCase       ,_lowerCamelCase	):
										raise TypeError(
										    f"additional_special_tokens should be of type {type(_lowerCamelCase	)}, but is"
										    f" {type(_lowerCamelCase	)}"	)

							__lowercase			  =							(
							    ([mask_token_sent] + additional_special_tokens)
							    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
							    else additional_special_tokens
							)
							# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
							additional_special_tokens_extended += [
							    f"<unk_{i}>" for i in range(len(_lowerCamelCase	)       ,self.offset - 1	)
							]

							if len(set(_lowerCamelCase	)	) != len(_lowerCamelCase	):
										raise ValueError(
										    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
										    f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."	)
							__lowercase			  =							additional_special_tokens_extended
				else:
							__lowercase			  =							[mask_token_sent] if mask_token_sent is not None else []
							additional_special_tokens += [f"<unk_{i}>" for i in range(2       ,self.offset	)]

				super().__init__(
				    _lowerCamelCase       ,tokenizer_file=_lowerCamelCase       ,pad_token=_lowerCamelCase       ,eos_token=_lowerCamelCase       ,unk_token=_lowerCamelCase       ,mask_token=_lowerCamelCase       ,mask_token_sent=_lowerCamelCase       ,offset=_lowerCamelCase       ,additional_special_tokens=_lowerCamelCase       ,**_lowerCamelCase       ,)
				__lowercase			  =							vocab_file
				__lowercase			  =							False if not self.vocab_file else True


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





				__lowercase			  =							set(self.all_special_ids	)  # call it once instead of inside list comp
				all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

				if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
							raise ValueError(
							    '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
							    f" {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}"	)

				return [1 if x in all_special_ids else 0 for x in seq]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None       ,_lowerCamelCase = False	)							->							List[int]:







				'''simple docstring'''





				if already_has_special_tokens:
							return self._special_token_mask(_lowerCamelCase	)
				elif token_ids_a is None:
							return self._special_token_mask(_lowerCamelCase	) + [1]
				else:
							return self._special_token_mask(token_ids_a + token_ids_a	) + [1]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							List[int]:







				'''simple docstring'''





				if token_ids_a is None:
							return token_ids_a + [self.eos_token_id]
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return token_ids_a + token_ids_a + [self.eos_token_id]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Tuple[str]:







				'''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(_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	):
							copyfile(self.vocab_file       ,_lowerCamelCase	)

				return (out_vocab_file,)












'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    "gpt_neox_japanese"


	def __init__(self       ,_lowerCamelCase=32000       ,_lowerCamelCase=2560       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase=4       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=1.0_0       ,_lowerCamelCase=10000       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-5       ,_lowerCamelCase=True       ,_lowerCamelCase=31996       ,_lowerCamelCase=31999       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.0       ,**_lowerCamelCase       ,)							->							Optional[int]:







				'''simple docstring'''





				super().__init__(bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,**_lowerCamelCase	)
				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_multiple_size
				__lowercase			  =							hidden_act
				__lowercase			  =							rotary_pct
				__lowercase			  =							rotary_emb_base
				__lowercase			  =							initializer_range
				__lowercase			  =							layer_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							attention_dropout
				__lowercase			  =							hidden_dropout





