code: 
  '''simple docstring'''
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  snake_case						=			{
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  						if not is_sentencepiece_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						snake_case						=			["CamembertTokenizer"]
  
  try:
  						if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						snake_case						=			["CamembertTokenizerFast"]
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						snake_case						=			[
  						    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "CamembertForCausalLM",
  						    "CamembertForMaskedLM",
  						    "CamembertForMultipleChoice",
  						    "CamembertForQuestionAnswering",
  						    "CamembertForSequenceClassification",
  						    "CamembertForTokenClassification",
  						    "CamembertModel",
  						    "CamembertPreTrainedModel",
  						]
  
  try:
  						if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						snake_case						=			[
  						    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "TFCamembertForCausalLM",
  						    "TFCamembertForMaskedLM",
  						    "TFCamembertForMultipleChoice",
  						    "TFCamembertForQuestionAnswering",
  						    "TFCamembertForSequenceClassification",
  						    "TFCamembertForTokenClassification",
  						    "TFCamembertModel",
  						    "TFCamembertPreTrainedModel",
  						]
  
  
  if TYPE_CHECKING:
  						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  						try:
  												if not is_sentencepiece_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .tokenization_camembert import CamembertTokenizer
  
  						try:
  												if not is_tokenizers_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .tokenization_camembert_fast import CamembertTokenizerFast
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_camembert import (
  												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    CamembertForCausalLM,
  												    CamembertForMaskedLM,
  												    CamembertForMultipleChoice,
  												    CamembertForQuestionAnswering,
  												    CamembertForSequenceClassification,
  												    CamembertForTokenClassification,
  												    CamembertModel,
  												    CamembertPreTrainedModel,
  												)
  
  						try:
  												if not is_tf_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_tf_camembert import (
  												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    TFCamembertForCausalLM,
  												    TFCamembertForMaskedLM,
  												    TFCamembertForMultipleChoice,
  												    TFCamembertForQuestionAnswering,
  												    TFCamembertForSequenceClassification,
  												    TFCamembertForTokenClassification,
  												    TFCamembertModel,
  												    TFCamembertPreTrainedModel,
  												)
  
  else:
  						import sys
  
  						snake_case						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  '''simple docstring'''
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: List[str]			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	AutoTokenizer.from_pretrained(UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: str			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Any     =	None
  						for l in x:
  								snake_case     :					str     =	self.tokenizer(
  								    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  								snake_case     :					Tuple     =	torch.tensor([len(UpperCamelCase__			)]			)
  								if d is not None:
  										for k in d.keys():
  												snake_case     :					int     =	torch.cat((d[k], t[k])     ,  0			)
  								else:
  										snake_case     :					List[str]     =	t
  
  						snake_case     :					Union[str, Any]     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''			)			)
  						snake_case     :					int     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''			)			)
  
  				elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Optional[int]     =	self.tokenizer(
  						    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  
  				else:
  						raise Exception(
  						    '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
  						    ''' strings` for supports are supported.'''			)
  
  				return d
  
  
  
  		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Any=0.70			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	[]
  				for idx in range(len(W_query['''input_ids''']			)			):
  						snake_case     :					Optional[int]     =	range(p_start.shape[1]			)
  
  						snake_case     :					str     =	[]
  						for start_id in start_indexes:
  								for end_id in end_indexes:
  										if start_id < end_id:
  												output.append(
  												    (
  												        start_id,
  												        end_id,
  												        p_start[idx][start_id].item(),
  												        p_end[idx][end_id].item(),
  												    )			)
  
  						output.sort(key=lambda UpperCamelCase__			: (tup[2] * tup[3])     ,  reverse=UpperCamelCase__			)
  						snake_case     :					int     =	[]
  						for k in range(len(UpperCamelCase__			)			):
  								if output[k][2] * output[k][3] >= thresh:
  										snake_case ,snake_case     :					Tuple     =	output[k][0], output[k][1]
  										snake_case     :					str     =	self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]			)
  										temp.append((decoded, output[k][2] * output[k][3])			)
  
  						final_outputs.append(UpperCamelCase__			)
  
  				return final_outputs
  
  
  