
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:
							SCREAMING_SNAKE_CASE__							=    None


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__							=    {
    """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""",
    },
}

SCREAMING_SNAKE_CASE__							=    {
    """facebook/xglm-564M""": 2048,
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Dict         =							VOCAB_FILES_NAMES
							lowerCAmelCase__		:						str         =							PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__		:						Dict         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__		:						Dict         =							["input_ids", "attention_mask"]
							lowerCAmelCase__		:						str         =							XGLMTokenizer





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



											"""simple docstring"""


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

											__lowercase								= 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					,			)

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





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



											"""simple docstring"""


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





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



											"""simple docstring"""


											__lowercase								= [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  :       Tuple					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       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(_UpperCAmelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""    )
															return
											__lowercase								= 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,)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__							=    {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """HubertForCTC""",
							    """HubertForSequenceClassification""",
							    """HubertModel""",
							    """HubertPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFHubertForCTC""",
							    """TFHubertModel""",
							    """TFHubertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_hubert import (
														    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    HubertForCTC,
														    HubertForSequenceClassification,
														    HubertModel,
														    HubertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_hubert import (
														    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFHubertForCTC,
														    TFHubertModel,
														    TFHubertPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





