'''simple docstring'''





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:     Tuple  =logging.get_logger()




@dataclass
class        __A      :
    a__       :						nn.Module
    a__       :						List[nn.Module]          =  field(default_factory=UpperCamelCase__	)
    a__       :						list          =  field(default_factory=UpperCamelCase__	)



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    int   ,			__a    :    Tensor   ,			__a    :    Tensor ):
         UpperCAmelCase_           =   len(list(m.modules() ) ) == 1 or isinstance(__a   ,			nn.Convad ) or isinstance(__a   ,			nn.BatchNormad )
         if has_not_submodules:
              self.traced.append(__a )



    def __call__(self    :    int   ,			__a    :    Tensor ):
         for m in self.module.modules():
              self.handles.append(m.register_forward_hook(self._forward_hook ) )
         self.module(__a )
         [x.remove() for x in self.handles]
         return self






    @property
    def 					_lowercase						(self    :    Optional[Any] ):
         # check the len of the state_dict keys to see if we have learnable params
         return list(filter(lambda __a : len(list(x.state_dict().keys() ) ) > 0   ,			self.traced ) )




@dataclass
class        __A      :
    a__       :						nn.Module
    a__       :						nn.Module
    a__       :						int          =  1
    a__       :						List          =  field(default_factory=UpperCamelCase__	)
    a__       :						List          =  field(default_factory=UpperCamelCase__	)
    a__       :						bool          =  True



    def __call__(self    :    List[Any]   ,			__a    :    Tensor ):
         UpperCAmelCase_           =   Tracker(self.dest )(__a ).parametrized
         UpperCAmelCase_           =   Tracker(self.src )(__a ).parametrized

         UpperCAmelCase_           =   list(filter(lambda __a : type(__a ) not in self.src_skip   ,			__a ) )
         UpperCAmelCase_           =   list(filter(lambda __a : type(__a ) not in self.dest_skip   ,			__a ) )

         if len(__a ) != len(__a ) and self.raise_if_mismatch:
              raise Exception(
                  f"""Numbers of operations are different. Source module has {len(__a )} operations while"""
                  f""" destination module has {len(__a )}.""" )

         for dest_m, src_m in zip(__a   ,			__a ):
              dest_m.load_state_dict(src_m.state_dict() )
              if self.verbose == 1:
                   print(f"""Transfered from={src_m} to={dest_m}""" )




class        __A      ( nn.Module	):



    def __init__(self    :    str   ,			__a    :    nn.Module ):
         super().__init__()

         UpperCAmelCase_           =   []
         # - 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}"""
              UpperCAmelCase_           =   len(__a ) + 1
              feature_blocks.append((f"""res{block_index}""", v) )

         UpperCAmelCase_           =   nn.ModuleDict(__a )






    def 					_lowercase						(self    :    Any   ,			__a    :    Tensor ):
         return get_trunk_forward_outputs(
             __a   ,			out_feat_keys=__a   ,			feature_blocks=self._feature_blocks   ,			)




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    str   ,			__a    :    str ):
         UpperCAmelCase_           =   x.split("-" )
         return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )






    def __getitem__(self    :    Any   ,			__a    :    str ):
         # default to timm!
         if x not in self:
              UpperCAmelCase_           =   self.convert_name_to_timm(__a )
              UpperCAmelCase_           =   partial(lambda: (timm.create_model(__a   ,			pretrained=__a ).eval(), None) )

         else:
              UpperCAmelCase_           =   super().__getitem__(__a )

         return val




class        __A      ( UpperCamelCase__	):



    def __getitem__(self    :    List[Any]   ,			__a    :    str ):
         if "seer" in x and "in1k" not in x:
              UpperCAmelCase_           =   RegNetModel
         else:
              UpperCAmelCase_           =   RegNetForImageClassification
         return val





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : int					,    snake_case_       : List[Tuple[str, str]]			)    ->      Union[str, Any]:


     '''simple docstring'''




     for from_key, to_key in keys:
          UpperCAmelCase_           =   from_state_dict[from_key].clone()
          print(f"""Copied key={from_key} to={to_key}"""			)
     return to_state_dict





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : Callable[[], nn.Module]					,    snake_case_       : Callable[[], nn.Module]					,    snake_case_       : RegNetConfig					,    snake_case_       : Path					,    snake_case_       : bool = True					,    )    ->      int:


     '''simple docstring'''




     print(f"""Converting {name}..."""			)
     with torch.no_grad():
          UpperCAmelCase_    , UpperCAmelCase_           =   from_model_func()
          UpperCAmelCase_           =   our_model_func(snake_case_			).eval()
          UpperCAmelCase_           =   ModuleTransfer(src=snake_case_					,    dest=snake_case_					,    raise_if_mismatch=snake_case_			)
          UpperCAmelCase_           =   torch.randn((1, 3, 2_24, 2_24)			)
          module_transfer(snake_case_			)

     if from_state_dict is not None:
          UpperCAmelCase_           =   []
          # for seer - in1k finetuned we have to manually copy the head
          if "seer" in name and "in1k" in name:
               UpperCAmelCase_           =   [("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
          UpperCAmelCase_           =   manually_copy_vissl_head(snake_case_					,    our_model.state_dict()					,    snake_case_			)
          our_model.load_state_dict(snake_case_			)

     UpperCAmelCase_           =   our_model(snake_case_					,    output_hidden_states=snake_case_			)
     UpperCAmelCase_           =   (
         our_outputs.logits if isinstance(snake_case_					,    snake_case_			) else our_outputs.last_hidden_state
     )

     UpperCAmelCase_           =   from_model(snake_case_			)
     UpperCAmelCase_           =   from_output[-1] if type(snake_case_			) is list else from_output

     # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
     if "seer" in name and "in1k" in name:
          UpperCAmelCase_           =   our_outputs.hidden_states[-1]

     assert torch.allclose(snake_case_					,    snake_case_			), "The model logits don't match the original one."

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / name					,    commit_message="Add model"					,    use_temp_dir=snake_case_					,    )

          UpperCAmelCase_           =   2_24 if "seer" not in name else 3_84
          # we can use the convnext one
          UpperCAmelCase_           =   AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"					,    size=snake_case_			)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / name					,    commit_message="Add image processor"					,    use_temp_dir=snake_case_					,    )

          print(f"""Pushed {name}"""			)





def 	lowerCAmelCase_				(					snake_case_       : Path					,    snake_case_       : str = None					,    snake_case_       : bool = True			)    ->      Tuple:


     '''simple docstring'''




     UpperCAmelCase_           =   "imagenet-1k-id2label.json"
     UpperCAmelCase_           =   10_00
     UpperCAmelCase_           =   (1, num_labels)

     UpperCAmelCase_           =   "huggingface/label-files"
     UpperCAmelCase_           =   num_labels
     UpperCAmelCase_           =   json.load(open(cached_download(hf_hub_url(snake_case_					,    snake_case_					,    repo_type="dataset"			)			)					,    "r"			)			)
     UpperCAmelCase_           =   {int(snake_case_			): v for k, v in idalabel.items()}

     UpperCAmelCase_           =   idalabel
     UpperCAmelCase_           =   {v: k for k, v in idalabel.items()}

     UpperCAmelCase_           =   partial(snake_case_					,    num_labels=snake_case_					,    idalabel=snake_case_					,    labelaid=snake_case_			)

     UpperCAmelCase_           =   {
         "regnet-x-002": ImageNetPreTrainedConfig(
             depths=[1, 1, 4, 7]					,    hidden_sizes=[24, 56, 1_52, 3_68]					,    groups_width=8					,    layer_type="x"			),
         "regnet-x-004": ImageNetPreTrainedConfig(
             depths=[1, 2, 7, 12]					,    hidden_sizes=[32, 64, 1_60, 3_84]					,    groups_width=16					,    layer_type="x"			),
         "regnet-x-006": ImageNetPreTrainedConfig(
             depths=[1, 3, 5, 7]					,    hidden_sizes=[48, 96, 2_40, 5_28]					,    groups_width=24					,    layer_type="x"			),
         "regnet-x-008": ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 5]					,    hidden_sizes=[64, 1_28, 2_88, 6_72]					,    groups_width=16					,    layer_type="x"			),
         "regnet-x-016": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 2]					,    hidden_sizes=[72, 1_68, 4_08, 9_12]					,    groups_width=24					,    layer_type="x"			),
         "regnet-x-032": ImageNetPreTrainedConfig(
             depths=[2, 6, 15, 2]					,    hidden_sizes=[96, 1_92, 4_32, 10_08]					,    groups_width=48					,    layer_type="x"			),
         "regnet-x-040": ImageNetPreTrainedConfig(
             depths=[2, 5, 14, 2]					,    hidden_sizes=[80, 2_40, 5_60, 13_60]					,    groups_width=40					,    layer_type="x"			),
         "regnet-x-064": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]					,    hidden_sizes=[1_68, 3_92, 7_84, 16_24]					,    groups_width=56					,    layer_type="x"			),
         "regnet-x-080": ImageNetPreTrainedConfig(
             depths=[2, 5, 15, 1]					,    hidden_sizes=[80, 2_40, 7_20, 19_20]					,    groups_width=1_20					,    layer_type="x"			),
         "regnet-x-120": ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]					,    hidden_sizes=[2_24, 4_48, 8_96, 22_40]					,    groups_width=1_12					,    layer_type="x"			),
         "regnet-x-160": ImageNetPreTrainedConfig(
             depths=[2, 6, 13, 1]					,    hidden_sizes=[2_56, 5_12, 8_96, 20_48]					,    groups_width=1_28					,    layer_type="x"			),
         "regnet-x-320": ImageNetPreTrainedConfig(
             depths=[2, 7, 13, 1]					,    hidden_sizes=[3_36, 6_72, 13_44, 25_20]					,    groups_width=1_68					,    layer_type="x"			),
         # y variant
         "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]					,    hidden_sizes=[24, 56, 1_52, 3_68]					,    groups_width=8			),
         "regnet-y-004": ImageNetPreTrainedConfig(
             depths=[1, 3, 6, 6]					,    hidden_sizes=[48, 1_04, 2_08, 4_40]					,    groups_width=8			),
         "regnet-y-006": ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 4]					,    hidden_sizes=[48, 1_12, 2_56, 6_08]					,    groups_width=16			),
         "regnet-y-008": ImageNetPreTrainedConfig(
             depths=[1, 3, 8, 2]					,    hidden_sizes=[64, 1_28, 3_20, 7_68]					,    groups_width=16			),
         "regnet-y-016": ImageNetPreTrainedConfig(
             depths=[2, 6, 17, 2]					,    hidden_sizes=[48, 1_20, 3_36, 8_88]					,    groups_width=24			),
         "regnet-y-032": ImageNetPreTrainedConfig(
             depths=[2, 5, 13, 1]					,    hidden_sizes=[72, 2_16, 5_76, 15_12]					,    groups_width=24			),
         "regnet-y-040": ImageNetPreTrainedConfig(
             depths=[2, 6, 12, 2]					,    hidden_sizes=[1_28, 1_92, 5_12, 10_88]					,    groups_width=64			),
         "regnet-y-064": ImageNetPreTrainedConfig(
             depths=[2, 7, 14, 2]					,    hidden_sizes=[1_44, 2_88, 5_76, 12_96]					,    groups_width=72			),
         "regnet-y-080": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]					,    hidden_sizes=[1_68, 4_48, 8_96, 20_16]					,    groups_width=56			),
         "regnet-y-120": ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]					,    hidden_sizes=[2_24, 4_48, 8_96, 22_40]					,    groups_width=1_12			),
         "regnet-y-160": ImageNetPreTrainedConfig(
             depths=[2, 4, 11, 1]					,    hidden_sizes=[2_24, 4_48, 12_32, 30_24]					,    groups_width=1_12			),
         "regnet-y-320": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]					,    hidden_sizes=[2_32, 6_96, 13_92, 37_12]					,    groups_width=2_32			),
         # models created by SEER -> https://arxiv.org/abs/2202.08360
         "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]					,    hidden_sizes=[2_32, 6_96, 13_92, 37_12]					,    groups_width=2_32			),
         "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]					,    hidden_sizes=[3_28, 9_84, 19_68, 49_20]					,    groups_width=3_28			),
         "regnet-y-1280-seer": RegNetConfig(
             depths=[2, 7, 17, 1]					,    hidden_sizes=[5_28, 10_56, 29_04, 73_92]					,    groups_width=2_64			),
         "regnet-y-2560-seer": RegNetConfig(
             depths=[3, 7, 16, 1]					,    hidden_sizes=[6_40, 16_96, 25_44, 50_88]					,    groups_width=6_40			),
         "regnet-y-10b-seer": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]					,    hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]					,    groups_width=10_10			),
         # finetuned on imagenet
         "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]					,    hidden_sizes=[2_32, 6_96, 13_92, 37_12]					,    groups_width=2_32			),
         "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]					,    hidden_sizes=[3_28, 9_84, 19_68, 49_20]					,    groups_width=3_28			),
         "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]					,    hidden_sizes=[5_28, 10_56, 29_04, 73_92]					,    groups_width=2_64			),
         "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
             depths=[3, 7, 16, 1]					,    hidden_sizes=[6_40, 16_96, 25_44, 50_88]					,    groups_width=6_40			),
         "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]					,    hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]					,    groups_width=10_10			),
     }

     UpperCAmelCase_           =   NameToOurModelFuncMap()
     UpperCAmelCase_           =   NameToFromModelFuncMap()
     # add seer weights logic

     def load_using_classy_vision(snake_case_       : str					,    snake_case_       : Callable[[], nn.Module]			) -> Tuple[nn.Module, Dict]:
          UpperCAmelCase_           =   torch.hub.load_state_dict_from_url(snake_case_					,    model_dir=str(snake_case_			)					,    map_location="cpu"			)
          UpperCAmelCase_           =   model_func()
          # check if we have a head, if yes add it
          UpperCAmelCase_           =   files["classy_state_dict"]["base_model"]["model"]
          UpperCAmelCase_           =   model_state_dict["trunk"]
          model.load_state_dict(snake_case_			)
          return model.eval(), model_state_dict["heads"]

     # pretrained
     UpperCAmelCase_           =   partial(
         snake_case_					,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"					,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)					,    )

     UpperCAmelCase_           =   partial(
         snake_case_					,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"					,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)					,    )

     UpperCAmelCase_           =   partial(
         snake_case_					,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"					,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()			)					,    )

     UpperCAmelCase_           =   partial(
         snake_case_					,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"					,    lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27					,    group_width=10_10					,    w_a=17_44					,    w_a=620.83					,    w_m=2.52			)			)			)					,    )

     # IN1K finetuned
     UpperCAmelCase_           =   partial(
         snake_case_					,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"					,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)					,    )

     UpperCAmelCase_           =   partial(
         snake_case_					,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"					,    lambda: FakeRegNetVisslWrapper(RegNetYaagf()			)					,    )

     UpperCAmelCase_           =   partial(
         snake_case_					,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"					,    lambda: FakeRegNetVisslWrapper(RegNetYaaagf()			)					,    )

     UpperCAmelCase_           =   partial(
         snake_case_					,    "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"					,    lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27					,    group_width=10_10					,    w_a=17_44					,    w_a=620.83					,    w_m=2.52			)			)			)					,    )

     if model_name:
          convert_weight_and_push(
              snake_case_					,    names_to_from_model_map[model_name]					,    names_to_ours_model_map[model_name]					,    names_to_config[model_name]					,    snake_case_					,    snake_case_					,    )
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   snake_case_					,    names_to_from_model_map[model_name]					,    names_to_ours_model_map[model_name]					,    snake_case_					,    snake_case_					,    snake_case_					,    )
     return config, expected_shape


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Dict  =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.',
    )

    SCREAMING_SNAKE_CASE_:     List[str]  =parser.parse_args()

    SCREAMING_SNAKE_CASE_:     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)





'''simple docstring'''





import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_:     str  =logging.getLogger(__name__)




@dataclass(frozen=UpperCamelCase__	)
class        __A      :
    a__       :						str
    a__       :						str
    a__       :						Optional[str]          =  None
    a__       :						Optional[str]          =  None
    a__       :						Optional[str]          =  None




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


if is_torch_available():
    import torch
    from torch.utils.data import Dataset




    class        __A      ( UpperCamelCase__	):
        a__       :						List[InputFeatures]



        def __init__(self    :    Any   ,			__a    :    str   ,			__a    :    PreTrainedTokenizer   ,			__a    :    str   ,			__a    :    Optional[int] = None   ,			__a    :    Dict=False   ,			__a    :    bool = False   ,			):
             UpperCAmelCase_           =   hans_processors[task]()

             UpperCAmelCase_           =   os.path.join(
                 __a   ,			"cached_{}_{}_{}_{}".format(
                     "dev" if evaluate else "train"   ,			tokenizer.__class__.__name__   ,			str(__a )   ,			__a   ,			)   ,			)
             UpperCAmelCase_           =   processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  UpperCAmelCase_    , UpperCAmelCase_           =   label_list[2], label_list[1]
             UpperCAmelCase_           =   label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             UpperCAmelCase_           =   cached_features_file + ".lock"
             with FileLock(__a ):
                  if os.path.exists(__a ) and not overwrite_cache:
                       logger.info(f"""Loading features from cached file {cached_features_file}""" )
                       UpperCAmelCase_           =   torch.load(__a )
                  else:
                       logger.info(f"""Creating features from dataset file at {data_dir}""" )

                       UpperCAmelCase_           =   (
                           processor.get_dev_examples(__a ) if evaluate else processor.get_train_examples(__a )
                       )

                       logger.info("Training examples: %s"   ,			len(__a ) )
                       UpperCAmelCase_           =   hans_convert_examples_to_features(__a   ,			__a   ,			__a   ,			__a )
                       logger.info("Saving features into cached file %s"   ,			__a )
                       torch.save(self.features   ,			__a )



        def __len__(self    :    List[Any] ):
             return len(self.features )



        def __getitem__(self    :    Any   ,			__a    :    Optional[Any] ):
             return self.features[i]






        def 					_lowercase						(self    :    Union[str, Any] ):
             return self.label_list


if is_tf_available():
    import tensorflow as tf




    class        __A      :
        a__       :						List[InputFeatures]



        def __init__(self    :    Union[str, Any]   ,			__a    :    str   ,			__a    :    PreTrainedTokenizer   ,			__a    :    str   ,			__a    :    Optional[int] = 128   ,			__a    :    Any=False   ,			__a    :    bool = False   ,			):
             UpperCAmelCase_           =   hans_processors[task]()
             UpperCAmelCase_           =   processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  UpperCAmelCase_    , UpperCAmelCase_           =   label_list[2], label_list[1]
             UpperCAmelCase_           =   label_list

             UpperCAmelCase_           =   processor.get_dev_examples(__a ) if evaluate else processor.get_train_examples(__a )
             UpperCAmelCase_           =   hans_convert_examples_to_features(__a   ,			__a   ,			__a   ,			__a )

             def gen():
                  for ex_index, ex in tqdm.tqdm(enumerate(self.features )   ,			desc="convert examples to features" ):
                       if ex_index % 10000 == 0:
                            logger.info("Writing example %d of %d" % (ex_index, len(__a )) )

                       yield (
                           {
                               "example_id": 0,
                               "input_ids": ex.input_ids,
                               "attention_mask": ex.attention_mask,
                               "token_type_ids": ex.token_type_ids,
                           },
                           ex.label,
                       )

             UpperCAmelCase_           =   tf.data.Dataset.from_generator(
                 __a   ,			(
                     {
                         "example_id": tf.intaa,
                         "input_ids": tf.intaa,
                         "attention_mask": tf.intaa,
                         "token_type_ids": tf.intaa,
                     },
                     tf.intaa,
                 )   ,			(
                     {
                         "example_id": tf.TensorShape([] ),
                         "input_ids": tf.TensorShape([None, None] ),
                         "attention_mask": tf.TensorShape([None, None] ),
                         "token_type_ids": tf.TensorShape([None, None] ),
                     },
                     tf.TensorShape([] ),
                 )   ,			)



        def 					_lowercase						(self    :    int ):
             return self.dataset



        def __len__(self    :    Any ):
             return len(self.features )



        def __getitem__(self    :    int   ,			__a    :    Union[str, Any] ):
             return self.features[i]






        def 					_lowercase						(self    :    int ):
             return self.label_list




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    List[Any]   ,			__a    :    Dict ):
         return self._create_examples(self._read_tsv(os.path.join(__a   ,			"heuristics_train_set.txt" ) )   ,			"train" )



    def 					_lowercase						(self    :    Any   ,			__a    :    List[Any] ):
         return self._create_examples(self._read_tsv(os.path.join(__a   ,			"heuristics_evaluation_set.txt" ) )   ,			"dev" )



    def 					_lowercase						(self    :    Any ):
         return ["contradiction", "entailment", "neutral"]






    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Optional[int]   ,			__a    :    Union[str, Any] ):
         UpperCAmelCase_           =   []
         for i, line in enumerate(__a ):
              if i == 0:
                   continue
              UpperCAmelCase_           =   "%s-%s" % (set_type, line[0])
              UpperCAmelCase_           =   line[5]
              UpperCAmelCase_           =   line[6]
              UpperCAmelCase_           =   line[7][2:] if line[7].startswith("ex" ) else line[7]
              UpperCAmelCase_           =   line[0]
              examples.append(InputExample(guid=__a   ,			text_a=__a   ,			text_b=__a   ,			label=__a   ,			pairID=__a ) )
         return examples





def 	lowerCAmelCase_				(					snake_case_       : List[InputExample]					,    snake_case_       : List[str]					,    snake_case_       : int					,    snake_case_       : PreTrainedTokenizer					,    )    ->      Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   {label: i for i, label in enumerate(snake_case_			)}

     UpperCAmelCase_           =   []
     for ex_index, example in tqdm.tqdm(enumerate(snake_case_			)					,    desc="convert examples to features"			):
          if ex_index % 1_00_00 == 0:
               logger.info("Writing example %d" % (ex_index)			)

          UpperCAmelCase_           =   tokenizer(
              example.text_a					,    example.text_b					,    add_special_tokens=snake_case_					,    max_length=snake_case_					,    padding="max_length"					,    truncation=snake_case_					,    return_overflowing_tokens=snake_case_					,    )

          UpperCAmelCase_           =   label_map[example.label] if example.label in label_map else 0

          UpperCAmelCase_           =   int(example.pairID			)

          features.append(InputFeatures(**snake_case_					,    label=snake_case_					,    pairID=snake_case_			)			)

     for i, example in enumerate(examples[:5]			):
          logger.info("*** Example ***"			)
          logger.info(f"""guid: {example}"""			)
          logger.info(f"""features: {features[i]}"""			)

     return features


SCREAMING_SNAKE_CASE_:     int  ={
    'hans': 3,
}






SCREAMING_SNAKE_CASE_:     Any  ={
    'hans': HansProcessor,
}




