


"""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_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": 20_48,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	XGLMTokenizer







		def __init__(    self					:				Optional[int]			,      a__					:				str=None			,      a__					:				int=None			,      a__					:				Any="<s>"			,      a__					:				Optional[int]="</s>"			,      a__					:				Union[str, Any]="</s>"			,      a__					:				Optional[Any]="<s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Any="<pad>"			,      **a__					:				Any			,      )				->  Optional[int]:





						'''simple docstring'''







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

						_A         =	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__(
						    a__			,      tokenizer_file=a__			,      bos_token=a__			,      eos_token=a__			,      sep_token=a__			,      cls_token=a__			,      unk_token=a__			,      pad_token=a__			,      **a__			,      )

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







		def        a_       (    self					:				Optional[int]			,      a__					:				List[int]			,      a__					:				Optional[List[int]] = None						)				->  List[int]:





						'''simple docstring'''







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







		def        a_       (    self					:				Optional[Any]			,      a__					:				List[int]			,      a__					:				Optional[List[int]] = None						)				->  List[int]:





						'''simple docstring'''







						_A         =	[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        a_       (    self					:				Optional[Any]			,      a__					:				str			,      a__					:				Optional[str] = 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(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""						)
										return
						_A         =	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,)



"""simple docstring"""


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

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


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		a_											=			None


a_											=			logging.get_logger(__name__)


a_											=			"▁"

a_											=			{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=			{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

a_											=			{
    "google/reformer-crime-and-punishment": 52_42_88,
}
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	VOCAB_FILES_NAMES
		__UpperCamelCase							=	PRETRAINED_VOCAB_FILES_MAP
		__UpperCamelCase							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__UpperCamelCase							=	['input_ids', 'attention_mask']
		__UpperCamelCase							=	ReformerTokenizer







		def __init__(    self					:				Dict			,      a__					:				List[Any]=None			,      a__					:				int=None			,      a__					:				int="</s>"			,      a__					:				List[Any]="<unk>"			,      a__					:				Optional[Any]=[]			,      **a__					:				Tuple			,      )				->  Tuple:





						'''simple docstring'''







						super().__init__(
						    a__			,      tokenizer_file=a__			,      eos_token=a__			,      unk_token=a__			,      additional_special_tokens=a__			,      **a__			,      )

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







		def        a_       (    self					:				Any			,      a__					:				str			,      a__					:				Optional[str] = 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(a__						):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
										return
						_A         =	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,)