code: 
  
  
  
  
  
  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)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  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'"							)
  
  
  
  
  
  
  