


'''simple docstring'''
import torch

from transformers import AutoTokenizer







class 					_A						(						UpperCamelCase							):



   '''simple docstring'''






   def __init__(	self	:					Optional[Any]					, lowerCamelCase	:					str						)->	Optional[Any]:
          snake_case__					:	Union[str, Any]          =					AutoTokenizer.from_pretrained(lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					List[Any]					, lowerCamelCase	:					Any						)->	Tuple:







          if isinstance(lowerCamelCase					, lowerCamelCase						) and all(isinstance(_x					, lowerCamelCase						) for _x in x						):
                 snake_case__					:	Optional[Any]          =					None
                 for l in x:
                        snake_case__					:	Dict          =					self.tokenizer(
                            lowerCamelCase					, padding="""max_length"""					, max_length=384					, truncation=lowerCamelCase					, return_tensors="""pt"""					, )
                        snake_case__					:	int          =					torch.tensor([len(lowerCamelCase						)]						)
                        if d is not None:
                               for k in d.keys():
                                      snake_case__					:	Union[str, Any]          =					torch.cat((d[k], t[k])					, 0						)
                        else:
                               snake_case__					:	Any          =					t

                 snake_case__					:	int          =					torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""						)						)
                 snake_case__					:	Dict          =					torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""						)						)

          elif isinstance(lowerCamelCase					, lowerCamelCase						) and all(isinstance(_x					, lowerCamelCase						) for _x in x						):
                 snake_case__					:	Tuple          =					self.tokenizer(
                     lowerCamelCase					, padding="""max_length"""					, max_length=384					, truncation=lowerCamelCase					, 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	:					Dict					, lowerCamelCase	:					List[Any]					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					List[str]					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					Any=0.70						)->	List[Any]:
          snake_case__					:	Union[str, Any]          =					[]
          for idx in range(len(W_query["""input_ids"""]						)						):
                 snake_case__					:	str          =					range(p_start.shape[1]						)

                 snake_case__					:	Union[str, Any]          =					[]
                 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 lowerCamelCase						: (tup[2] * tup[3])					, reverse=lowerCamelCase						)
                 snake_case__					:	Tuple          =					[]
                 for k in range(len(lowerCamelCase						)						):
                        if output[k][2] * output[k][3] >= thresh:
                               snake_case__       ,   snake_case__					:	Optional[int]          =					output[k][0], output[k][1]
                               snake_case__					:	Optional[int]          =					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(lowerCamelCase						)

          return final_outputs












'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
