






"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase_								=	{
    'vocab_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'microsoft/layoutlm-base-uncased': (
            'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'
        ),
        'microsoft/layoutlm-large-uncased': (
            'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'
        ),
    },
}

lowercase_								=	{
    'microsoft/layoutlm-base-uncased': 512,
    'microsoft/layoutlm-large-uncased': 512,
}

lowercase_								=	{
    'microsoft/layoutlm-base-uncased': {'do_lower_case': True},
    'microsoft/layoutlm-large-uncased': {'do_lower_case': True},
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Any							=					VOCAB_FILES_NAMES
   A_		:      Tuple							=					PRETRAINED_VOCAB_FILES_MAP
   A_		:      Optional[int]							=					PRETRAINED_INIT_CONFIGURATION
   A_		:      List[Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_		:      Optional[int]							=					LayoutLMTokenizer


   def __init__(      self					:	List[Any],			_lowerCamelCase					:	Tuple=None,			_lowerCamelCase					:	Optional[Any]=None,			_lowerCamelCase					:	Optional[int]=True,			_lowerCamelCase					:	Optional[Any]="[UNK]",			_lowerCamelCase					:	Any="[SEP]",			_lowerCamelCase					:	Optional[int]="[PAD]",			_lowerCamelCase					:	Any="[CLS]",			_lowerCamelCase					:	Any="[MASK]",			_lowerCamelCase					:	int=True,			_lowerCamelCase					:	str=None,			**_lowerCamelCase					:	Tuple,			):

     '''simple docstring'''
     super().__init__(
         _lowerCamelCase,			tokenizer_file=_lowerCamelCase,			do_lower_case=_lowerCamelCase,			unk_token=_lowerCamelCase,			sep_token=_lowerCamelCase,			pad_token=_lowerCamelCase,			cls_token=_lowerCamelCase,			mask_token=_lowerCamelCase,			tokenize_chinese_chars=_lowerCamelCase,			strip_accents=_lowerCamelCase,			**_lowerCamelCase,			)

     __A		     =					json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
     if (
         normalizer_state.get('''lowercase''',			_lowerCamelCase    ) != do_lower_case
         or normalizer_state.get('''strip_accents''',			_lowerCamelCase    ) != strip_accents
         or normalizer_state.get('''handle_chinese_chars''',			_lowerCamelCase    ) != tokenize_chinese_chars
     ):
       __A		     =					getattr(_lowerCamelCase,			normalizer_state.pop('''type'''    )    )
       __A		     =					do_lower_case
       __A		     =					strip_accents
       __A		     =					tokenize_chinese_chars
       __A		     =					normalizer_class(**_lowerCamelCase    )

     __A		     =					do_lower_case


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Dict=None    ):

     '''simple docstring'''
     __A		     =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	List[int],			_lowerCamelCase					:	Optional[List[int]] = None    ):

     '''simple docstring'''
     __A		     =					[self.sep_token_id]
     __A		     =					[self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep    ) * [0]
     return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[str] = None    ):

     '''simple docstring'''
     __A		     =					self._tokenizer.model.save(_lowerCamelCase,			name=_lowerCamelCase    )
     return tuple(_lowerCamelCase    )









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


__magic_name__       =     get_logger(__name__)





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






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






