





import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase			:			Optional[Any]	  = logging.get_logger()







@dataclass
class        __lowerCAmelCase :
      _lowercase       :      nn.Module
      _lowercase       :      List[nn.Module]        =    field(default_factory=UpperCamelCase__)
      _lowercase       :      list        =    field(default_factory=UpperCamelCase__)
      _lowercase       :      Dict[str, nn.Module]        =    field(default_factory=UpperCamelCase__)







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		str:



        '''simple docstring'''




        a__			:      Dict            =len(list(m.modules()							)							) == 1 or isinstance(lowerCAmelCase__	,   nn.Convad							) or isinstance(lowerCAmelCase__	,   nn.BatchNormad							)
        if has_not_submodules:
          self.traced.append(lowerCAmelCase__							)
          a__			:      List[Any]            =m







      def __call__(					self	,   lowerCAmelCase__							)     ->		Optional[Any]:



        '''simple docstring'''




        for name, m in self.module.named_modules():
          self.handles.append(m.register_forward_hook(partial(self._forward_hook	,   name=lowerCAmelCase__							)							)							)
        self.module(lowerCAmelCase__							)
        [x.remove() for x in self.handles]
        return self







      @property
      def      _lowercase						(					self							)     ->		Optional[Any]:



        '''simple docstring'''




        return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}







class        __lowerCAmelCase (					nn.Module):







      def __init__(					self	,   lowerCAmelCase__							)     ->		List[Any]:



        '''simple docstring'''




        super().__init__()

        a__			:      List[Tuple[str, nn.Module]]            =[]
        # - get the stem
        feature_blocks.append(("conv1", model.stem)							)
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
          assert k.startswith("block"							), F'''Unexpected layer name {k}'''
          a__			:      int            =len(lowerCAmelCase__							) + 1
          feature_blocks.append((F'''res{block_index}''', v)							)

        a__			:      List[str]            =nn.ModuleDict(lowerCAmelCase__							)







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:



        '''simple docstring'''




        return get_trunk_forward_outputs(
            lowerCAmelCase__	,   out_feat_keys=lowerCAmelCase__	,   feature_blocks=self._feature_blocks	,   )







class        __lowerCAmelCase (					UpperCamelCase__):







      def      _lowercase						(					self							)     ->		Tuple:



        '''simple docstring'''




        return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 1_7, 8, 1.0), (8, 2, 1, 8, 1.0)]
def   _A (	SCREAMING_SNAKE_CASE  :     str		):





  """simple docstring"""



  a__			:      Union[str, Any]            =RegNetConfig(depths=[2, 7, 17, 1] , hidden_sizes=[8, 8, 8, 8] , groups_width=8		)
  if "in1k" in model_name:
    a__			:      Dict            =RegNetForImageClassification(SCREAMING_SNAKE_CASE		)
  else:
    a__			:      List[Any]            =RegNetModel(SCREAMING_SNAKE_CASE		)
  # create from model (with small weights)
  a__			:      Tuple            =FakeRegNetVisslWrapper(
      RegNet(FakeRegNetParams(depth=27 , group_width=1_010 , w_a=1_744 , w_a=620.83 , w_m=2.5_2		)		)		)

  with torch.no_grad():
    a__			:      int            =from_model.eval()
    a__			:      int            =our_model.eval()

    a__			:      Dict            =torch.randn((1, 3, 32, 32)		)
    # trace both
    a__			:      Tuple            =Tracker(SCREAMING_SNAKE_CASE		)
    a__			:      List[Any]            =dest_tracker(SCREAMING_SNAKE_CASE		).parametrized

    pprint(dest_tracker.nameamodule		)
    a__			:      Optional[Any]            =Tracker(SCREAMING_SNAKE_CASE		)
    a__			:      Tuple            =src_tracker(SCREAMING_SNAKE_CASE		).parametrized

  # convert the keys -> module dict to keys -> params
  def to_params_dict(SCREAMING_SNAKE_CASE  :     Any		):
    a__			:      List[Any]            =OrderedDict()
    for name, module in dict_with_modules.items():
      for param_name, param in module.state_dict().items():
        a__			:      Optional[Any]            =param
    return params_dict

  a__			:      Union[str, Any]            ={}

  a__			:      Optional[int]            =to_params_dict(SCREAMING_SNAKE_CASE		)
  a__			:      List[Any]            =to_params_dict(SCREAMING_SNAKE_CASE		)

  for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items() , dst_state_dict.items()		):
    a__			:      List[str]            =dest_key
    logger.info(f'''{src_key} -> {dest_key}'''		)
  # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
  if "in1k" in model_name:
    a__			:      str            ="classifier.1.weight"
    a__			:      Optional[Any]            ="classifier.1.bias"

  return from_to_ours_keys
def   _A (	SCREAMING_SNAKE_CASE  :     Path , SCREAMING_SNAKE_CASE  :     str = None , SCREAMING_SNAKE_CASE  :     bool = True		):





  """simple docstring"""



  a__			:      int            ="imagenet-1k-id2label.json"
  a__			:      int            =1_000

  a__			:      Optional[int]            ="huggingface/label-files"
  a__			:      Union[str, Any]            =num_labels
  a__			:      Optional[int]            =json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type="dataset"		)		) , "r"		)		)
  a__			:      Tuple            ={int(SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}

  a__			:      Any            =idalabel
  a__			:      Optional[int]            ={v: k for k, v in idalabel.items()}

  a__			:      str            =partial(SCREAMING_SNAKE_CASE , num_labels=SCREAMING_SNAKE_CASE , idalabel=SCREAMING_SNAKE_CASE , labelaid=SCREAMING_SNAKE_CASE		)

  a__			:      Tuple            ={
      "regnet-y-10b-seer": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1] , hidden_sizes=[2_020, 4_040, 11_110, 28_280] , groups_width=1_010		),
      # finetuned on imagenet
      "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1] , hidden_sizes=[2_020, 4_040, 11_110, 28_280] , groups_width=1_010		),
  }

  # add seer weights logic
  def load_using_classy_vision(SCREAMING_SNAKE_CASE  :     str		) -> Tuple[Dict, Dict]:
    a__			:      Optional[int]            =torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE , model_dir=str(SCREAMING_SNAKE_CASE		) , map_location="cpu"		)
    # check if we have a head, if yes add it
    a__			:      Optional[Any]            =files["classy_state_dict"]["base_model"]["model"]
    return model_state_dict["trunk"], model_state_dict["heads"]

  a__			:      int            ={
      "regnet-y-10b-seer": partial(
          SCREAMING_SNAKE_CASE , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch" , ),
      "regnet-y-10b-seer-in1k": partial(
          SCREAMING_SNAKE_CASE , "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch" , ),
  }

  a__			:      List[Any]            =get_from_to_our_keys(SCREAMING_SNAKE_CASE		)

  if not (save_directory / f'''{model_name}.pth''').exists():
    logger.info("Loading original state_dict."		)
    a__			:      Optional[int]            =names_to_from_model[model_name]()
    a__			:      Dict            =from_state_dict_trunk
    if "in1k" in model_name:
      # add the head
      a__			:      Optional[int]            ={**from_state_dict_trunk, **from_state_dict_head}
    logger.info("Done!"		)

    a__			:      List[str]            ={}

    a__			:      Any            =list(from_state_dict.keys()		)
    a__			:      Union[str, Any]            =r"\.block.-part."
    # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
    for key in from_state_dict.keys():
      # remove the weird "block[0,1]-part" from the key
      a__			:      int            =re.sub(SCREAMING_SNAKE_CASE , "" , SCREAMING_SNAKE_CASE		)
      # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
      a__			:      Optional[int]            =from_to_ours_keys[src_key]
      # store the parameter with our key
      a__			:      Tuple            =from_state_dict[key]
      not_used_keys.remove(SCREAMING_SNAKE_CASE		)
    # check that all keys have been updated
    assert len(SCREAMING_SNAKE_CASE		) == 0, f'''Some keys where not used {",".join(SCREAMING_SNAKE_CASE		)}'''

    logger.info(f'''The following keys were not used: {",".join(SCREAMING_SNAKE_CASE		)}'''		)

    # save our state dict to disk
    torch.save(SCREAMING_SNAKE_CASE , save_directory / f'''{model_name}.pth'''		)

    del converted_state_dict
  else:
    logger.info("The state_dict was already stored on disk."		)
  if push_to_hub:
    logger.info(f'''Token is {os.environ["HF_TOKEN"]}'''		)
    logger.info("Loading our model."		)
    # create our model
    a__			:      Tuple            =names_to_config[model_name]
    a__			:      Tuple            =RegNetModel
    if "in1k" in model_name:
      a__			:      Any            =RegNetForImageClassification
    a__			:      Tuple            =our_model_func(SCREAMING_SNAKE_CASE		)
    # place our model to the meta device (so remove all the weights)
    our_model.to(torch.device("meta"		)		)
    logger.info("Loading state_dict in our model."		)
    # load state dict
    a__			:      int            =our_model.state_dict().keys()
    PreTrainedModel._load_pretrained_model_low_mem(
        SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , [save_directory / f'''{model_name}.pth''']		)
    logger.info("Finally, pushing!"		)
    # push it to hub
    our_model.push_to_hub(
        repo_path_or_name=save_directory / model_name , commit_message="Add model" , output_dir=save_directory / model_name , )
    a__			:      Dict            =384
    # we can use the convnext one
    a__			:      Any            =AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" , size=SCREAMING_SNAKE_CASE		)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / model_name , commit_message="Add image processor" , output_dir=save_directory / model_name , )


if __name__ == "__main__":
     UpperCAmelCase			:			Tuple	  = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default=None,
         type=str,
         help=(
             """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
             """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=Path,
         required=True,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument(
         """--push_to_hub""",
         default=True,
         type=bool,
         required=False,
         help="""If True, push model and image processor to the hub.""",
     )

     UpperCAmelCase			:			int	  = parser.parse_args()

     UpperCAmelCase			:			Path	  = args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)













import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


UpperCAmelCase			:			int	  = logging.get_logger(__name__)







@dataclass
class        __lowerCAmelCase :
      _lowercase       :      str
      _lowercase       :      str
      _lowercase       :      Optional[str]        =    None
      _lowercase       :      Optional[str]        =    None







      def      _lowercase						(					self							)     ->		List[str]:



        '''simple docstring'''




        return json.dumps(dataclasses.asdict(self							)	,   indent=2							) + "\n"







@dataclass(frozen=UpperCamelCase__)
class        __lowerCAmelCase :
      _lowercase       :      List[int]
      _lowercase       :      Optional[List[int]]        =    None
      _lowercase       :      Optional[List[int]]        =    None
      _lowercase       :      Optional[Union[int, float]]        =    None







      def      _lowercase						(					self							)     ->		Optional[int]:



        '''simple docstring'''




        return json.dumps(dataclasses.asdict(self							)							) + "\n"







class        __lowerCAmelCase :







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self							)     ->		Any:



        '''simple docstring'''




        raise NotImplementedError()







      def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Dict:



        '''simple docstring'''




        if len(self.get_labels()							) > 1:
          a__			:      Dict            =self.get_labels()[int(example.label							)]
        return example







      @classmethod
      def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		Union[str, Any]:



        '''simple docstring'''




        with open(lowerCAmelCase__	,   "r"	,   encoding="utf-8-sig"							) as f:
          return list(csv.reader(lowerCAmelCase__	,   delimiter="\t"	,   quotechar=lowerCAmelCase__							)							)







class        __lowerCAmelCase (					UpperCamelCase__):







      def __init__(					self	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__="classification"	,   lowerCAmelCase__=False							)     ->		Any:



        '''simple docstring'''




        a__			:      Any            =[] if labels is None else labels
        a__			:      List[Any]            =[] if examples is None else examples
        a__			:      List[str]            =mode
        a__			:      Tuple            =verbose







      def __len__(					self							)     ->		Optional[int]:



        '''simple docstring'''




        return len(self.examples							)







      def __getitem__(					self	,   lowerCAmelCase__							)     ->		int:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							):
          return SingleSentenceClassificationProcessor(labels=self.labels	,   examples=self.examples[idx]							)
        return self.examples[idx]







      @classmethod
      def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   **lowerCAmelCase__							)     ->		List[str]:



        '''simple docstring'''




        a__			:      Tuple            =cls(**lowerCAmelCase__							)
        processor.add_examples_from_csv(
            lowerCAmelCase__	,   split_name=lowerCAmelCase__	,   column_label=lowerCAmelCase__	,   column_text=lowerCAmelCase__	,   column_id=lowerCAmelCase__	,   skip_first_row=lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__	,   )
        return processor







      @classmethod
      def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   **lowerCAmelCase__							)     ->		int:



        '''simple docstring'''




        a__			:      List[str]            =cls(**lowerCAmelCase__							)
        processor.add_examples(lowerCAmelCase__	,   labels=lowerCAmelCase__							)
        return processor







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   )     ->		Dict:



        '''simple docstring'''




        a__			:      Dict            =self._read_tsv(lowerCAmelCase__							)
        if skip_first_row:
          a__			:      Tuple            =lines[1:]
        a__			:      List[Any]            =[]
        a__			:      int            =[]
        a__			:      str            =[]
        for i, line in enumerate(lowerCAmelCase__							):
          texts.append(line[column_text]							)
          labels.append(line[column_label]							)
          if column_id is not None:
            ids.append(line[column_id]							)
          else:
            a__			:      List[str]            =F'''{split_name}-{i}''' if split_name else str(lowerCAmelCase__							)
            ids.append(lowerCAmelCase__							)

        return self.add_examples(
            lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__							)







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False							)     ->		List[Any]:



        '''simple docstring'''




        if labels is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
          raise ValueError(
              F'''Text and labels have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
        if ids is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
          raise ValueError(F'''Text and ids have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
        if ids is None:
          a__			:      Dict            =[None] * len(lowerCAmelCase__							)
        if labels is None:
          a__			:      str            =[None] * len(lowerCAmelCase__							)
        a__			:      Union[str, Any]            =[]
        a__			:      Optional[Any]            =set()
        for text_or_text_and_label, label, guid in zip(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							):
          if isinstance(lowerCAmelCase__	,   (tuple, list)							) and label is None:
            a__      ,							a__			:      int            =text_or_text_and_label
          else:
            a__			:      List[str]            =text_or_text_and_label
          added_labels.add(lowerCAmelCase__							)
          examples.append(InputExample(guid=lowerCAmelCase__	,   text_a=lowerCAmelCase__	,   text_b=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)

        # Update examples
        if overwrite_examples:
          a__			:      Dict            =examples
        else:
          self.examples.extend(lowerCAmelCase__							)

        # Update labels
        if overwrite_labels:
          a__			:      Any            =list(lowerCAmelCase__							)
        else:
          a__			:      List[Any]            =list(set(self.labels							).union(lowerCAmelCase__							)							)

        return self.examples







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=None	,   )     ->		int:



        '''simple docstring'''




        if max_length is None:
          a__			:      int            =tokenizer.max_len

        a__			:      int            ={label: i for i, label in enumerate(self.labels							)}

        a__			:      Union[str, Any]            =[]
        for ex_index, example in enumerate(self.examples							):
          if ex_index % 1_0_0_0_0 == 0:
            logger.info(F'''Tokenizing example {ex_index}'''							)

          a__			:      Dict            =tokenizer.encode(
              example.text_a	,   add_special_tokens=lowerCAmelCase__	,   max_length=min(lowerCAmelCase__	,   tokenizer.max_len							)	,   )
          all_input_ids.append(lowerCAmelCase__							)

        a__			:      List[Any]            =max(len(lowerCAmelCase__							) for input_ids in all_input_ids							)

        a__			:      Dict            =[]
        for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__	,   self.examples							)							):
          if ex_index % 1_0_0_0_0 == 0:
            logger.info(F'''Writing example {ex_index}/{len(self.examples							)}'''							)
          # The mask has 1 for real tokens and 0 for padding tokens. Only real
          # tokens are attended to.
          a__			:      Optional[Any]            =[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__							)

          # Zero-pad up to the sequence length.
          a__			:      List[str]            =batch_length - len(lowerCAmelCase__							)
          if pad_on_left:
            a__			:      Tuple            =([pad_token] * padding_length) + input_ids
            a__			:      Dict            =([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
          else:
            a__			:      List[str]            =input_ids + ([pad_token] * padding_length)
            a__			:      Any            =attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

          if len(lowerCAmelCase__							) != batch_length:
            raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
          if len(lowerCAmelCase__							) != batch_length:
            raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)

          if self.mode == "classification":
            a__			:      Dict            =label_map[example.label]
          elif self.mode == "regression":
            a__			:      Any            =float(example.label							)
          else:
            raise ValueError(self.mode							)

          if ex_index < 5 and self.verbose:
            logger.info("*** Example ***"							)
            logger.info(F'''guid: {example.guid}'''							)
            logger.info(F'''input_ids: {" ".join([str(lowerCAmelCase__							) for x in input_ids]							)}'''							)
            logger.info(F'''attention_mask: {" ".join([str(lowerCAmelCase__							) for x in attention_mask]							)}'''							)
            logger.info(F'''label: {example.label} (id = {label})'''							)

          features.append(InputFeatures(input_ids=lowerCAmelCase__	,   attention_mask=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)

        if return_tensors is None:
          return features
        elif return_tensors == "tf":
          if not is_tf_available():
            raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"							)
          import tensorflow as tf

          def gen():
            for ex in features:
              yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

          a__			:      Any            =tf.data.Dataset.from_generator(
              lowerCAmelCase__	,   ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,   ({"input_ids": tf.TensorShape([None]							), "attention_mask": tf.TensorShape([None]							)}, tf.TensorShape([]							))	,   )
          return dataset
        elif return_tensors == "pt":
          if not is_torch_available():
            raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"							)
          import torch
          from torch.utils.data import TensorDataset

          a__			:      str            =torch.tensor([f.input_ids for f in features]	,   dtype=torch.long							)
          a__			:      str            =torch.tensor([f.attention_mask for f in features]	,   dtype=torch.long							)
          if self.mode == "classification":
            a__			:      Dict            =torch.tensor([f.label for f in features]	,   dtype=torch.long							)
          elif self.mode == "regression":
            a__			:      Tuple            =torch.tensor([f.label for f in features]	,   dtype=torch.float							)

          a__			:      List[str]            =TensorDataset(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
          return dataset
        else:
          raise ValueError("return_tensors should be one of 'tf' or 'pt'"							)






