
"""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': 2_0_4_8,
}






class        _lowercase   (				__UpperCAmelCase		):
	lowercase_        =	VOCAB_FILES_NAMES
	lowercase_        =	PRETRAINED_VOCAB_FILES_MAP
	lowercase_        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_        =	['input_ids', 'attention_mask']
	lowercase_        =	XGLMTokenizer




	def __init__( self		, UpperCAmelCase_=None		, UpperCAmelCase_=None		, UpperCAmelCase_="<s>"		, UpperCAmelCase_="</s>"		, UpperCAmelCase_="</s>"		, UpperCAmelCase_="<s>"		, UpperCAmelCase_="<unk>"		, UpperCAmelCase_="<pad>"		, **UpperCAmelCase_		, )		->							List[Any]:
			# Compatibility with the original tokenizer
			lowerCamelCase     :			List[str]		=    7
			lowerCamelCase     :			int		=    [F"""<madeupword{i}>""" for i in range(self.num_madeup_words					)]

			lowerCamelCase     :			Dict		=    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_		, )

			lowerCamelCase     :			Optional[int]		=    vocab_file
			lowerCamelCase     :			Optional[Any]		=    False if not self.vocab_file else True




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_ = None					)		->							List[int]:

			if token_ids_a is None:
					return [self.sep_token_id] + token_ids_a
			lowerCamelCase     :			Optional[int]		=    [self.sep_token_id]
			return sep + token_ids_a + sep + sep + token_ids_a




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_ = None					)		->							List[int]:
			lowerCamelCase     :			int		=    [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		, UpperCAmelCase_		, UpperCAmelCase_ = None					)		->							Tuple[str]:
			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
			lowerCamelCase     :			Dict		=    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,)







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_ )



