






"""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:
 lowerCAmelCase_		:			Any				   =					None


lowerCAmelCase_		:			Dict				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			List[str]				   =					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_		:			Dict				   =					{
    '''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''',
    },
}

lowerCAmelCase_		:			Optional[Any]				   =					{
    '''facebook/xglm-564M''': 2_0_4_8,
}





class      UpperCamelCase_       (				a_     ):
  _A				:    Optional[int] =		VOCAB_FILES_NAMES
  _A				:    Union[str, Any] =		PRETRAINED_VOCAB_FILES_MAP
  _A				:    Optional[int] =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A				:    Dict =		['input_ids', 'attention_mask']
  _A				:    Union[str, Any] =		XGLMTokenizer



  def __init__(					self       ,						snake_case__=None       ,						snake_case__=None       ,						snake_case__="<s>"       ,						snake_case__="</s>"       ,						snake_case__="</s>"       ,						snake_case__="<s>"       ,						snake_case__="<unk>"       ,						snake_case__="<pad>"       ,						**snake_case__       ,						)	->	Tuple:



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

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



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	List[int]:



         """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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	List[int]:



         """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 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = 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(snake_case__ ):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
                return
         UpperCAmelCase					=	os.path.join(
             snake_case__       ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

         if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
                copyfile(self.vocab_file       ,						snake_case__ )

         return (out_vocab_file,)

import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		Any ):
       if version.parse(lowercase_ ) < version.parse(lowercase_ ):
              if "dev" in min_version:
                     _UpperCamelCase   :				Tuple       =     (
                         'This example requires a source install from HuggingFace diffusers (see '
                         '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
                     )
              else:
                     _UpperCamelCase   :				str       =     F"This example requires a minimum version of {min_version},"
              error_message += F" but the version found is {__version__}.\n"
              raise ImportError(lowercase_ )



