

from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy



class     lowercase						(       _UpperCamelCase					):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE												=				"""MarkupLMFeatureExtractor"""
 __SCREAMING_SNAKE_CASE												=				("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
 __SCREAMING_SNAKE_CASE												=				True




 def __call__(self       ,       __a=None       ,       __a=None       ,       __a=None       ,       __a=None       ,       __a=None       ,       __a = True       ,       __a = False       ,       __a = None       ,       __a = None       ,       __a = 0       ,       __a = None       ,       __a = None       ,       __a = None       ,       __a = False       ,       __a = False       ,       __a = False       ,       __a = False       ,       __a = True       ,       __a = None       ,       **__a       ,       ) ->					BatchEncoding:






  """simple docstring"""





  if self.parse_html:
   if html_strings is None:
    raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`' )

   if nodes is not None or xpaths is not None or node_labels is not None:
    raise ValueError(
        'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`' )

   UpperCAmelCase__							=				self.feature_extractor(__a )
   UpperCAmelCase__							=				features['nodes']
   UpperCAmelCase__							=				features['xpaths']
  else:
   if html_strings is not None:
    raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.' )
   if nodes is None or xpaths is None:
    raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`' )

        # # second, apply the tokenizer
  if questions is not None and self.parse_html:
   if isinstance(__a       ,       __a ):
    UpperCAmelCase__							=				[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

  UpperCAmelCase__							=				self.tokenizer(
      text=questions if questions is not None else nodes       ,       text_pair=nodes if questions is not None else None       ,       xpaths=__a       ,       node_labels=__a       ,       add_special_tokens=__a       ,       padding=__a       ,       truncation=__a       ,       max_length=__a       ,       stride=__a       ,       pad_to_multiple_of=__a       ,       return_token_type_ids=__a       ,       return_attention_mask=__a       ,       return_overflowing_tokens=__a       ,       return_special_tokens_mask=__a       ,       return_offsets_mapping=__a       ,       return_length=__a       ,       verbose=__a       ,       return_tensors=__a       ,       **__a       ,       )

  return encoded_inputs




 def   UpperCamelCase__							(self       ,       *__a       ,       **__a ) ->					List[str]:






  """simple docstring"""





  return self.tokenizer.batch_decode(*__a       ,       **__a )




 def   UpperCamelCase__							(self       ,       *__a       ,       **__a ) ->					Optional[Any]:






  """simple docstring"""





  return self.tokenizer.decode(*__a       ,       **__a )




 @property
 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tokenizer.model_input_names
  return tokenizer_input_names







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        __lowerCAmelCase    (SCREAMING_SNAKE_CASE					)->  Tuple:




      """simple docstring"""

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