


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A   :					List[Any]	=      logging.get_logger(__name__)

A   :					Any	=      {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "blenderbot-small"
       A__      		=    ["past_key_values"]
       A__      		=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    List[str]=50265      ,   __lowerCamelCase							:    str=512      ,   __lowerCamelCase							:    Tuple=8      ,   __lowerCamelCase							:    str=2048      ,   __lowerCamelCase							:    str=16      ,   __lowerCamelCase							:    List[Any]=8      ,   __lowerCamelCase							:    Any=2048      ,   __lowerCamelCase							:    List[str]=16      ,   __lowerCamelCase							:    Dict=0.0      ,   __lowerCamelCase							:    List[Any]=0.0      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Tuple="gelu"      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    int=0.0      ,   __lowerCamelCase							:    Union[str, Any]=0.0      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    str=1      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Optional[Any]=1      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    Any=2      ,   **__lowerCamelCase							:    int      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						vocab_size
             lowerCamelCase__	:       Union[str, Any]					=						max_position_embeddings
             lowerCamelCase__	:       Union[str, Any]					=						d_model
             lowerCamelCase__	:       Optional[int]					=						encoder_ffn_dim
             lowerCamelCase__	:       Dict					=						encoder_layers
             lowerCamelCase__	:       Any					=						encoder_attention_heads
             lowerCamelCase__	:       Union[str, Any]					=						decoder_ffn_dim
             lowerCamelCase__	:       str					=						decoder_layers
             lowerCamelCase__	:       Optional[Any]					=						decoder_attention_heads
             lowerCamelCase__	:       List[str]					=						dropout
             lowerCamelCase__	:       List[Any]					=						attention_dropout
             lowerCamelCase__	:       Dict					=						activation_dropout
             lowerCamelCase__	:       Optional[Any]					=						activation_function
             lowerCamelCase__	:       Dict					=						init_std
             lowerCamelCase__	:       List[str]					=						encoder_layerdrop
             lowerCamelCase__	:       Dict					=						decoder_layerdrop
             lowerCamelCase__	:       int					=						use_cache
             lowerCamelCase__	:       List[Any]					=						encoder_layers
             lowerCamelCase__	:       Tuple					=						scale_embedding  # scale factor will be sqrt(d_model) if True

             super().__init__(
                 pad_token_id=__lowerCamelCase      ,   bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   is_encoder_decoder=__lowerCamelCase      ,   decoder_start_token_id=__lowerCamelCase      ,   forced_eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase      ,   )







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       int					=						OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ] )

                   if self.use_past:
                         lowerCamelCase__	:       Union[str, Any]					=						{0: "batch"}
                         lowerCamelCase__	:       int					=						{0: "batch", 1: "past_decoder_sequence + sequence"}
                   else:
                         lowerCamelCase__	:       Tuple					=						{0: "batch", 1: "decoder_sequence"}
                         lowerCamelCase__	:       str					=						{0: "batch", 1: "decoder_sequence"}

                   if self.use_past:
                         self.fill_with_past_key_values_(__lowerCamelCase      ,   direction="inputs" )
             elif self.task == "causal-lm":
                   # TODO: figure this case out.
                   lowerCamelCase__	:       Tuple					=						OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ] )
                   if self.use_past:
                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.num_layers
                         for i in range(__lowerCamelCase ):
                               lowerCamelCase__	:       Union[str, Any]					=						{0: "batch", 2: "past_sequence + sequence"}
                               lowerCamelCase__	:       Optional[int]					=						{0: "batch", 2: "past_sequence + sequence"}
             else:
                   lowerCamelCase__	:       Any					=						OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                           ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                           ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                       ] )

             return common_inputs





       @property
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Union[str, Any]					=						super().outputs
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self ).outputs
                   if self.use_past:
                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.num_layers
                         for i in range(__lowerCamelCase ):
                               lowerCamelCase__	:       Tuple					=						{0: "batch", 2: "past_sequence + sequence"}
                               lowerCamelCase__	:       Any					=						{0: "batch", 2: "past_sequence + sequence"}
             return common_outputs





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             # Generate decoder inputs
             lowerCamelCase__	:       List[str]					=						seq_length if not self.use_past else 1
             lowerCamelCase__	:       List[Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
             lowerCamelCase__	:       Optional[Any]					=						dict(**__lowerCamelCase      ,   **__lowerCamelCase )

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
                   else:
                         import torch
                   lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						common_inputs["input_ids"].shape
                   lowerCamelCase__	:       int					=						common_inputs["decoder_input_ids"].shape[1]
                   lowerCamelCase__					,     lowerCamelCase__	:       Optional[Any]					=						self.num_attention_heads
                   lowerCamelCase__	:       str					=						(
                       batch,
                       num_encoder_attention_heads,
                       encoder_seq_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   lowerCamelCase__	:       Optional[int]					=						decoder_seq_length + 3
                   lowerCamelCase__	:       Dict					=						(
                       batch,
                       num_decoder_attention_heads,
                       decoder_past_length,
                       self._config.hidden_size // num_decoder_attention_heads,
                   )

                   lowerCamelCase__	:       List[Any]					=						torch.cat(
                       [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase )]      ,   dim=1 )

                   lowerCamelCase__	:       Optional[Any]					=						[]
                   # If the number of encoder and decoder layers are present in the model configuration, both are considered
                   lowerCamelCase__					,     lowerCamelCase__	:       str					=						self.num_layers
                   lowerCamelCase__	:       Union[str, Any]					=						min(__lowerCamelCase      ,   __lowerCamelCase )
                   lowerCamelCase__	:       Union[str, Any]					=						max(__lowerCamelCase      ,   __lowerCamelCase ) - min_num_layers
                   lowerCamelCase__	:       str					=						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

                   for _ in range(__lowerCamelCase ):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                             ) )
                   # TODO: test this.
                   lowerCamelCase__	:       Optional[int]					=						encoder_shape if remaining_side_name == "encoder" else decoder_shape
                   for _ in range(__lowerCamelCase      ,   __lowerCamelCase ):
                         common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) )
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             if self.use_past:
                   if not is_torch_available():
                         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
                   else:
                         import torch
                   lowerCamelCase__					,     lowerCamelCase__	:       int					=						common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   lowerCamelCase__	:       str					=						seqlen + 2
                   lowerCamelCase__					,     lowerCamelCase__	:       Optional[int]					=						self.num_layers
                   lowerCamelCase__					,     lowerCamelCase__	:       int					=						self.num_attention_heads
                   lowerCamelCase__	:       Tuple					=						(
                       batch,
                       num_encoder_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )

                   lowerCamelCase__	:       Union[str, Any]					=						common_inputs["attention_mask"].dtype
                   lowerCamelCase__	:       List[str]					=						torch.cat(
                       [common_inputs["attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase      ,   dtype=__lowerCamelCase )]      ,   dim=1 )
                   lowerCamelCase__	:       Tuple					=						[
                       (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
                   ]
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						compute_effective_axis_dimension(
                 __lowerCamelCase      ,   fixed_dimension=OnnxConfig.default_fixed_batch      ,   num_token_to_add=0 )

             # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
             lowerCamelCase__	:       List[str]					=						tokenizer.num_special_tokens_to_add(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						compute_effective_axis_dimension(
                 __lowerCamelCase      ,   fixed_dimension=OnnxConfig.default_fixed_sequence      ,   num_token_to_add=__lowerCamelCase )

             # Generate dummy inputs according to compute batch and sequence
             lowerCamelCase__	:       Optional[int]					=						[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
             lowerCamelCase__	:       Optional[Any]					=						dict(tokenizer(__lowerCamelCase      ,   return_tensors=__lowerCamelCase ) )
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Optional[int]					=						self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

             elif self.task == "causal-lm":
                   lowerCamelCase__	:       Any					=						self._generate_dummy_inputs_for_causal_lm(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Any					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

             return common_inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Dict					=						super()._flatten_past_key_values_(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self )._flatten_past_key_values_(
                       __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )






import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


A   :					Union[str, Any]	=      logging.get_logger(__name__)

A   :					Union[str, Any]	=      list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
A   :					Optional[Any]	=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     _lowercase				:


       """simple docstring"""




       A__      		=    field(
           default=lowercase__							,						metadata={"help": "Model type selected in the list: " + ", ".join(lowercase__)})
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."})
       A__      		=    field(
           default=1_28							,						metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }							,						)
       A__      		=    field(
           default=1_28							,						metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."}							,						)
       A__      		=    field(
           default=64							,						metadata={
               "help": (
                   "The maximum number of tokens for the question. Questions longer than this will "
                   "be truncated to this length."
               )
           }							,						)
       A__      		=    field(
           default=30							,						metadata={
               "help": (
                   "The maximum length of an answer that can be generated. This is needed because the start "
                   "and end predictions are not conditioned on one another."
               )
           }							,						)
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "Overwrite the cached training and evaluation sets"})
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."})
       A__      		=    field(
           default=0.0							,						metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."})
       A__      		=    field(
           default=20							,						metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."})
       A__      		=    field(
           default=0							,						metadata={
               "help": (
                   "language id of input for language-specific xlm models (see"
                   " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
               )
           }							,						)
       A__      		=    field(default=1							,						metadata={"help": "multiple threads for converting example to features"})







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "train"
       A__      		=    "dev"







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    42
       A__      		=    42
       A__      		=    42
       A__      		=    42





       def __init__(     self							:    Optional[int]      ,   __lowerCamelCase							:    SquadDataTrainingArguments      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    Union[str, Split] = Split.train      ,   __lowerCamelCase							:    Optional[bool] = False      ,   __lowerCamelCase							:    Optional[str] = None      ,   __lowerCamelCase							:    Optional[str] = "pt"      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						args
             lowerCamelCase__	:       Tuple					=						is_language_sensitive
             lowerCamelCase__	:       int					=						SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
             if isinstance(__lowerCamelCase      ,   __lowerCamelCase ):
                   try:
                         lowerCamelCase__	:       List[str]					=						Split[mode]
                   except KeyError:
                         raise KeyError("mode is not a valid split name" )
             lowerCamelCase__	:       str					=						mode
             # Load data features from cache or dataset file
             lowerCamelCase__	:       Any					=						"v2" if args.version_2_with_negative else "v1"
             lowerCamelCase__	:       List[str]					=						os.path.join(
                 cache_dir if cache_dir is not None else args.data_dir      ,   f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"      ,   )

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             lowerCamelCase__	:       List[str]					=						cached_features_file + ".lock"
             with FileLock(__lowerCamelCase ):
                   if os.path.exists(__lowerCamelCase ) and not args.overwrite_cache:
                         lowerCamelCase__	:       str					=						time.time()
                         lowerCamelCase__	:       Tuple					=						torch.load(__lowerCamelCase )

                         # Legacy cache files have only features, while new cache files
                         # will have dataset and examples also.
                         lowerCamelCase__	:       Optional[Any]					=						self.old_features["features"]
                         lowerCamelCase__	:       Optional[int]					=						self.old_features.get("dataset"      ,   __lowerCamelCase )
                         lowerCamelCase__	:       Optional[Any]					=						self.old_features.get("examples"      ,   __lowerCamelCase )
                         logger.info(
                             f"Loading features from cached file {cached_features_file} [took %.3f s]"      ,   time.time() - start )

                         if self.dataset is None or self.examples is None:
                               logger.warning(
                                   f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
                                   " future run" )
                   else:
                         if mode == Split.dev:
                               lowerCamelCase__	:       List[Any]					=						self.processor.get_dev_examples(args.data_dir )
                         else:
                               lowerCamelCase__	:       str					=						self.processor.get_train_examples(args.data_dir )

                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						squad_convert_examples_to_features(
                             examples=self.examples      ,   tokenizer=__lowerCamelCase      ,   max_seq_length=args.max_seq_length      ,   doc_stride=args.doc_stride      ,   max_query_length=args.max_query_length      ,   is_training=mode == Split.train      ,   threads=args.threads      ,   return_dataset=__lowerCamelCase      ,   )

                         lowerCamelCase__	:       int					=						time.time()
                         torch.save(
                             {"features": self.features, "dataset": self.dataset, "examples": self.examples}      ,   __lowerCamelCase      ,   )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )





       def __len__(     self							:    List[Any] ):


             '''simple docstring'''



             return len(self.features )





       def __getitem__(     self							:    List[str]      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.features[i]

             lowerCamelCase__	:       Tuple					=						torch.tensor(feature.input_ids      ,   dtype=torch.long )
             lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.attention_mask      ,   dtype=torch.long )
             lowerCamelCase__	:       Tuple					=						torch.tensor(feature.token_type_ids      ,   dtype=torch.long )
             lowerCamelCase__	:       Any					=						torch.tensor(feature.cls_index      ,   dtype=torch.long )
             lowerCamelCase__	:       Any					=						torch.tensor(feature.p_mask      ,   dtype=torch.float )
             lowerCamelCase__	:       Union[str, Any]					=						torch.tensor(feature.is_impossible      ,   dtype=torch.float )

             lowerCamelCase__	:       List[str]					=						{
                 "input_ids": input_ids,
                 "attention_mask": attention_mask,
                 "token_type_ids": token_type_ids,
             }

             if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
                   del inputs["token_type_ids"]

             if self.args.model_type in ["xlnet", "xlm"]:
                   inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
                   if self.args.version_2_with_negative:
                         inputs.update({"is_impossible": is_impossible} )
                   if self.is_language_sensitive:
                         inputs.update({"langs": (torch.ones(input_ids.shape      ,   dtype=torch.intaa ) * self.args.lang_id)} )

             if self.mode == Split.train:
                   lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.start_position      ,   dtype=torch.long )
                   lowerCamelCase__	:       List[Any]					=						torch.tensor(feature.end_position      ,   dtype=torch.long )
                   inputs.update({"start_positions": start_positions, "end_positions": end_positions} )

             return inputs


