'''simple docstring'''




import torch

from transformers import AutoTokenizer




class 					a__   (     UpperCAmelCase__				):






     def __init__(				self	:	Dict						,       a	:	List[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						AutoTokenizer.from_pretrained(a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[int]						,       a	:	Dict					):





         """simple docstring"""



         if isinstance(a						,       a					) and all(isinstance(_x						,       a					) for _x in x					):
             __lowerCamelCase						=						None
             for l in x:
                 __lowerCamelCase						=						self.tokenizer(
                     a						,       padding='''max_length'''						,       max_length=3_84						,       truncation=a						,       return_tensors='''pt'''						,       )
                 __lowerCamelCase						=						torch.tensor([len(a					)]					)
                 if d is not None:
                     for k in d.keys():
                         __lowerCamelCase						=						torch.cat((d[k], t[k])						,       0					)
                 else:
                     __lowerCamelCase						=						t

             __lowerCamelCase						=						torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''					)					)
             __lowerCamelCase						=						torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''					)					)

         elif isinstance(a						,       a					) and all(isinstance(_x						,       a					) for _x in x					):
             __lowerCamelCase						=						self.tokenizer(
                 a						,       padding='''max_length'''						,       max_length=3_84						,       truncation=a						,       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      SCREAMING_SNAKE_CASE__     (				self	:	Union[str, Any]						,       a	:	Union[str, Any]						,       a	:	Optional[int]						,       a	:	Union[str, Any]						,       a	:	Optional[Any]						,       a	:	str=0.70					):





         """simple docstring"""



         __lowerCamelCase						=						[]
         for idx in range(len(W_query['''input_ids''']					)					):
             __lowerCamelCase						=						__lowerCamelCase						=						range(p_start.shape[1]					)

             __lowerCamelCase						=						[]
             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 a					: (tup[2] * tup[3])						,       reverse=a					)
             __lowerCamelCase						=						[]
             for k in range(len(a					)					):
                 if output[k][2] * output[k][3] >= thresh:
                     __lowerCamelCase			,						__lowerCamelCase						=						output[k][0], output[k][1]
                     __lowerCamelCase						=						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(a					)

         return final_outputs

'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCAmelCase  							={
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase  							=[
      "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RwkvForCausalLM",
      "RwkvModel",
      "RwkvPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rwkv import (
        RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
        RwkvForCausalLM,
        RwkvModel,
        RwkvPreTrainedModel,
    )
else:
  import sys

  __UpperCAmelCase  							=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
