





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:
				UpperCAmelCase						:   Any			  =     None


UpperCAmelCase						:   Union[str, Any]			  =     logging.get_logger(__name__)


UpperCAmelCase						:   Union[str, Any]			  =     "▁"

UpperCAmelCase						:   Union[str, Any]			  =     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase						:   int			  =     {
    "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"
    },
}

UpperCAmelCase						:   str			  =     {
    "google/pegasus-xsum": 5_12,
}





class 						_A(					_a   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[Any]					    =	VOCAB_FILES_NAMES
				UpperCamelCase    :  Any					    =	PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase    :  List[Any]					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase    :  List[Any]					    =	PegasusTokenizer
				UpperCamelCase    :  Optional[Any]					    =	["""input_ids""", """attention_mask"""]



				def __init__(			self     ,					_A=None     ,					_A=None     ,					_A="<pad>"     ,					_A="</s>"     ,					_A="<unk>"     ,					_A="<mask_2>"     ,					_A="<mask_1>"     ,					_A=None     ,					_A=103     ,					**_A     ,					):
									__A	:						str       =				offset

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

														__A	:						Any       =				(
														    ([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(_A     )     ,					self.offset - 1     )
														]

														if len(set(_A     )     ) != len(_A     ):
																			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}."""     )
														__A	:						Any       =				additional_special_tokens_extended
									else:
														__A	:						Optional[Any]       =				[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__(
									    _A     ,					tokenizer_file=_A     ,					pad_token=_A     ,					eos_token=_A     ,					unk_token=_A     ,					mask_token=_A     ,					mask_token_sent=_A     ,					offset=_A     ,					additional_special_tokens=_A     ,					**_A     ,					)
									__A	:						Optional[Any]       =				vocab_file
									__A	:						List[str]       =				False if not self.vocab_file else True



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						str       =				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     ,					_A     ,					_A = None     ,					_A = False     ):

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



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=None     ):

									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     ,					_A     ,					_A = None     ):

									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(_A     ):
														logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
														return
									__A	:						Any       =				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     ):
														copyfile(self.vocab_file     ,					_A     )

									return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Tuple												= ["LlamaTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= ["LlamaTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "LlamaForCausalLM",
     "LlamaModel",
     "LlamaPreTrainedModel",
     "LlamaForSequenceClassification",
 ]


if TYPE_CHECKING:
 from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama import LlamaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama_fast import LlamaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


