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""": 2_0_4_8,
}
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							VOCAB_FILES_NAMES
   __lowerCAmelCase											=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase											=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase											=							["""input_ids""", """attention_mask"""]
   __lowerCAmelCase											=							XGLMTokenizer







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





    """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__(
        lowerCamelCase_      ,      tokenizer_file=lowerCamelCase_      ,      bos_token=lowerCamelCase_      ,      eos_token=lowerCamelCase_      ,      sep_token=lowerCamelCase_      ,      cls_token=lowerCamelCase_      ,      unk_token=lowerCamelCase_      ,      pad_token=lowerCamelCase_      ,      **lowerCamelCase_      ,      )

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







   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	List[int]      ,      lowerCamelCase_	:	Optional[List[int]] = 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 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	List[int]      ,      lowerCamelCase_	:	Optional[List[int]] = 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 	lowerCamelCase_  (    self	:	Optional[Any]      ,      lowerCamelCase_	:	str      ,      lowerCamelCase_	:	Optional[str] = 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(lowerCamelCase_							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""							)
     return
    UpperCamelCase      =			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,)

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   		=			{
    """configuration_align""": [
        """ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AlignConfig""",
        """AlignTextConfig""",
        """AlignVisionConfig""",
    ],
    """processing_align""": ["""AlignProcessor"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   		=			[
         """ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AlignModel""",
         """AlignPreTrainedModel""",
         """AlignTextModel""",
         """AlignVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

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