



from typing import Optional, Union

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







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						"MarkupLMFeatureExtractor"
   A__      : Dict       =						("MarkupLMTokenizer", "MarkupLMTokenizerFast")
   A__      : int       =						True


   def __call__(    self:     str  ,lowerCamelCase_:     Tuple=None  ,lowerCamelCase_:     int=None  ,lowerCamelCase_:     List[Any]=None  ,lowerCamelCase_:     List[Any]=None  ,lowerCamelCase_:     Any=None  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Union[bool, str, PaddingStrategy] = False  ,lowerCamelCase_:     Union[bool, str, TruncationStrategy] = None  ,lowerCamelCase_:     Optional[int] = None  ,lowerCamelCase_:     int = 0  ,lowerCamelCase_:     Optional[int] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     bool = False  ,lowerCamelCase_:     bool = False  ,lowerCamelCase_:     bool = False  ,lowerCamelCase_:     bool = False  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Union[str, TensorType]] = None  ,**lowerCamelCase_:     Optional[int]  ,)	->      BatchEncoding:
       # first, create nodes and xpaths
       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_     :       Any  		=					self.feature_extractor(lowerCamelCase_							)
           UpperCAmelCase_     :       List[str]  		=					features["""nodes"""]
           UpperCAmelCase_     :       int  		=					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(lowerCamelCase_  ,lowerCamelCase_							):
               UpperCAmelCase_     :       Optional[Any]  		=					[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

       return encoded_inputs


   def       A__    (    self:     Any  ,*lowerCamelCase_:     Dict  ,**lowerCamelCase_:     Union[str, Any]							)	->      Any:
       return self.tokenizer.batch_decode(*lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     Tuple  ,*lowerCamelCase_:     Dict  ,**lowerCamelCase_:     str							)	->      Tuple:
       return self.tokenizer.decode(*lowerCamelCase_  ,**lowerCamelCase_							)






   @property
   def       A__    (    self:     List[Any]							)	->      List[Any]:
       UpperCAmelCase_     :       List[str]  		=					self.tokenizer.model_input_names
       return tokenizer_input_names


import torch

from accelerate import Accelerator, DistributedType







class 							lowercase						:




							def __enter__(							self):
												if self.enabled:
																	lowercase		      =		self.model.no_sync()
																	self.model_sync_obj.__enter__()

												return self




							def __exit__(							self  ,A__  ,A__  ,A__):
												if self.enabled:
																	# Average all models on exit
																	self._sync_and_avg_model_params()
																	self.model_sync_obj.__exit__(A__  ,A__  ,A__)




							def __init__(							self  ,A__  ,A__  ,A__  ,A__ = True):
												if accelerator.distributed_type not in [
												    DistributedType.NO,
												    DistributedType.MULTI_CPU,
												    DistributedType.MULTI_GPU,
												]:
																	raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''')
												lowercase		      =		enabled and accelerator.distributed_type != DistributedType.NO
												lowercase		      =		0
												if self.enabled:
																	lowercase		      =		accelerator
																	lowercase		      =		model
																	lowercase		      =		local_sgd_steps




							def 					A__ (							self):
												self.num_steps += 1
												if not self.enabled:
																	return

												if self.num_steps % self.local_sgd_steps == 0:
																	self._sync_and_avg_model_params()





							def 					A__ (							self):
												self.accelerator.wait_for_everyone()
												with self.accelerator.autocast():
																	for param in self.model.parameters():
																						lowercase		      =		self.accelerator.reduce(param.data  ,reduction='''mean''')




