




import torch

from transformers import AutoTokenizer






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     def __init__(     self	: Union[str, Any]						,	A__	: int      )					->							str:
           lowerCamelCase__   	=					AutoTokenizer.from_pretrained(A__      )




     def 			__snake_case (     self	: Optional[int]						,	A__	: List[Any]      )					->							Dict:

           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_8_4						,	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_8_4						,	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 			__snake_case (     self	: List[str]						,	A__	: Optional[int]						,	A__	: Tuple						,	A__	: Tuple						,	A__	: int						,	A__	: Union[str, Any]=0.70      )					->							Any:
           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





from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     : Tuple      ={
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Dict      =["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : int      =["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          """OpenLlamaForCausalLM""",
          """OpenLlamaModel""",
          """OpenLlamaPreTrainedModel""",
          """OpenLlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)