



'''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()
A__    :   List[Any]           =					logging.get_logger()



@dataclass
class  snake_case__						:
 A__				= 42
 A__				= field(default_factory=SCREAMING_SNAKE_CASE_				)
 A__				= field(default_factory=SCREAMING_SNAKE_CASE_				)
 def A_   (   self						:							int      ,     __a						:							List[str]      ,     __a						:							Tensor      ,     __a						:							Tensor  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   List[Any]													=    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						:							List[Any]      ,     __a						:							Tensor  )      ->   Any:







       '''simple docstring'''
       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 A_   (   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       # 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  snake_case__						:
 A__				= 42
 A__				= 42
 A__				= 1
 A__				= field(default_factory=SCREAMING_SNAKE_CASE_				)
 A__				= field(default_factory=SCREAMING_SNAKE_CASE_				)
 A__				= True
 def __call__(   self						:							List[Any]      ,     __a						:							Tensor  )      ->   Any:







       '''simple docstring'''
       __snake_case :   Dict													=    Tracker(self.dest  )(__a  ).parametrized
       __snake_case :   List[Any]													=    Tracker(self.src  )(__a  ).parametrized

       __snake_case :   List[str]													=    list(filter(lambda __a  : type(__a  ) not in self.src_skip      ,     __a  )  )
       __snake_case :   Tuple													=    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  snake_case__						(    nn.Module				):
 def __init__(   self						:							List[Any]      ,     __a						:							nn.Module  )      ->   Optional[Any]:







       '''simple docstring'''
       super().__init__()

       __snake_case :   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}'''
             __snake_case :   Any													=    len(__a  ) + 1
             feature_blocks.append((f'''res{block_index}''', v)  )

       __snake_case :   Tuple													=    nn.ModuleDict(__a  )





 def A_   (   self						:							Union[str, Any]      ,     __a						:							Tensor  )      ->   Union[str, Any]:







       '''simple docstring'''
       return get_trunk_forward_outputs(
           __a      ,     out_feat_keys=__a      ,     feature_blocks=self._feature_blocks      ,     )



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def A_   (   self						:							Optional[int]      ,     __a						:							str  )      ->   str:







       '''simple docstring'''
       __snake_case :   str													=    x.split('-'  )
       return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]  )





 def __getitem__(   self						:							Optional[int]      ,     __a						:							str  )      ->   Callable[[], Tuple[nn.Module, Dict]]:







       '''simple docstring'''
       # default to timm!
       if x not in self:
             __snake_case :   Optional[int]													=    self.convert_name_to_timm(__a  )
             __snake_case :   Any													=    partial(lambda: (timm.create_model(__a      ,     pretrained=__a  ).eval(), None)  )

       else:
             __snake_case :   Tuple													=    super().__getitem__(__a  )

       return val



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 def __getitem__(   self						:							List[str]      ,     __a						:							str  )      ->   Callable[[], nn.Module]:







       '''simple docstring'''
       if "seer" in x and "in1k" not in x:
             __snake_case :   Optional[int]													=    RegNetModel
       else:
             __snake_case :   int													=    RegNetForImageClassification
       return val





def   a_   (		_UpperCAmelCase    :      Optional[int] ,_UpperCAmelCase    :      List[str] ,_UpperCAmelCase    :      List[Tuple[str, str]]				)    ->  Dict:
      for from_key, to_key in keys:
            __snake_case :   Optional[Any]													=    from_state_dict[from_key].clone()
            print(f'''Copied key={from_key} to={to_key}'''				)
      return to_state_dict





def   a_   (		_UpperCAmelCase    :      str ,_UpperCAmelCase    :      Callable[[], nn.Module] ,_UpperCAmelCase    :      Callable[[], nn.Module] ,_UpperCAmelCase    :      RegNetConfig ,_UpperCAmelCase    :      Path ,_UpperCAmelCase    :      bool = True ,)    ->  Dict:
      print(f'''Converting {name}...'''				)
      with torch.no_grad():
            __snake_case							,     __snake_case :   Union[str, Any]													=    from_model_func()
            __snake_case :   Optional[int]													=    our_model_func(_UpperCAmelCase				).eval()
            __snake_case :   Tuple													=    ModuleTransfer(src=_UpperCAmelCase ,dest=_UpperCAmelCase ,raise_if_mismatch=_UpperCAmelCase				)
            __snake_case :   int													=    torch.randn((1, 3, 2_24, 2_24)				)
            module_transfer(_UpperCAmelCase				)

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

      __snake_case :   Any													=    our_model(_UpperCAmelCase ,output_hidden_states=_UpperCAmelCase				)
      __snake_case :   Optional[int]													=    (
          our_outputs.logits if isinstance(_UpperCAmelCase ,_UpperCAmelCase				) else our_outputs.last_hidden_state
      )

      __snake_case :   Dict													=    from_model(_UpperCAmelCase				)
      __snake_case :   Union[str, Any]													=    from_output[-1] if type(_UpperCAmelCase				) 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:
            __snake_case :   Tuple													=    our_outputs.hidden_states[-1]

      assert torch.allclose(_UpperCAmelCase ,_UpperCAmelCase				), "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=_UpperCAmelCase ,)

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

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





def   a_   (		_UpperCAmelCase    :      Path ,_UpperCAmelCase    :      str = None ,_UpperCAmelCase    :      bool = True				)    ->  int:
      __snake_case :   List[str]													=    'imagenet-1k-id2label.json'
      __snake_case :   str													=    10_00
      __snake_case :   int													=    (1, num_labels)

      __snake_case :   Dict													=    'huggingface/label-files'
      __snake_case :   Optional[Any]													=    num_labels
      __snake_case :   int													=    json.load(open(cached_download(hf_hub_url(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset'				)				) ,'r'				)				)
      __snake_case :   Optional[int]													=    {int(_UpperCAmelCase				): v for k, v in idalabel.items()}

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

      __snake_case :   str													=    partial(_UpperCAmelCase ,num_labels=_UpperCAmelCase ,idalabel=_UpperCAmelCase ,labelaid=_UpperCAmelCase				)

      __snake_case :   List[str]													=    {
          '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				),
      }

      __snake_case :   Tuple													=    NameToOurModelFuncMap()
      __snake_case :   Tuple													=    NameToFromModelFuncMap()
      # add seer weights logic

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

      # pretrained
      __snake_case :   str													=    partial(
          _UpperCAmelCase ,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' ,lambda: FakeRegNetVisslWrapper(RegNetYaagf()				) ,)

      __snake_case :   Dict													=    partial(
          _UpperCAmelCase ,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' ,lambda: FakeRegNetVisslWrapper(RegNetYaagf()				) ,)

      __snake_case :   Optional[Any]													=    partial(
          _UpperCAmelCase ,'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()				) ,)

      __snake_case :   int													=    partial(
          _UpperCAmelCase ,'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=6_2_0.8_3 ,w_m=2.5_2				)				)				) ,)

      # IN1K finetuned
      __snake_case :   Any													=    partial(
          _UpperCAmelCase ,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' ,lambda: FakeRegNetVisslWrapper(RegNetYaagf()				) ,)

      __snake_case :   int													=    partial(
          _UpperCAmelCase ,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' ,lambda: FakeRegNetVisslWrapper(RegNetYaagf()				) ,)

      __snake_case :   Optional[Any]													=    partial(
          _UpperCAmelCase ,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' ,lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				) ,)

      __snake_case :   Union[str, Any]													=    partial(
          _UpperCAmelCase ,'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=6_2_0.8_3 ,w_m=2.5_2				)				)				) ,)

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


if __name__ == "__main__":
    A__    :   List[str]           =					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.''',
    )

    A__    :   Optional[Any]           =					parser.parse_args()

    A__    :   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 json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= BlenderbotSmallTokenizer
 A__				= False
 def A_   (   self						:							Any  )      ->   str:







       '''simple docstring'''
       super().setUp()

       __snake_case :   Any													=    ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
       __snake_case :   str													=    dict(zip(__a      ,     range(len(__a  )  )  )  )

       __snake_case :   List[Any]													=    ['#version: 0.2', 'a p', 't e</w>', 'ap t</w>', 'a d', 'ad apt</w>', 'a c', 'ac t</w>', '']
       __snake_case :   Tuple													=    {'unk_token': '__unk__', 'bos_token': '__start__', 'eos_token': '__end__'}

       __snake_case :   str													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']  )
       __snake_case :   Union[str, Any]													=    os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']  )
       with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write(json.dumps(__a  ) + '\n'  )
       with open(self.merges_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             fp.write('\n'.join(__a  )  )
 def A_   (   self						:							Tuple      ,     **__a						:							Optional[int]  )      ->   List[str]:







       '''simple docstring'''
       kwargs.update(self.special_tokens_map  )
       return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname      ,     **__a  )
 def A_   (   self						:							int      ,     __a						:							int  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[str]													=    'adapt act apte'
       return input_text, output_text
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       __snake_case :   Dict													=    BlenderbotSmallTokenizer(self.vocab_file      ,     self.merges_file      ,     **self.special_tokens_map  )
       __snake_case :   List[str]													=    'adapt act apte'
       __snake_case :   List[Any]													=    ['adapt', 'act', 'ap@@', 'te']
       __snake_case :   Optional[int]													=    tokenizer.tokenize(__a  )
       self.assertListEqual(__a      ,     __a  )

       __snake_case :   List[str]													=    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

       __snake_case :   List[str]													=    [0, 1, 2, 3, 4, 5]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__a  )      ,     __a  )
 def A_   (   self						:							int  )      ->   str:







       '''simple docstring'''
       __snake_case :   int													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       assert tok('sam'  ).input_ids == [1384]
       __snake_case :   List[str]													=    'I am a small frog.'
       __snake_case :   List[Any]													=    tok([src_text]      ,     padding=__a      ,     truncation=__a  )['input_ids']
       __snake_case :   int													=    tok.batch_decode(__a      ,     skip_special_tokens=__a      ,     clean_up_tokenization_spaces=__a  )[0]
       assert src_text != decoded  # I wish it did!
       assert decoded == "i am a small frog ."





 def A_   (   self						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   Tuple													=    BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'  )
       __snake_case :   Optional[Any]													=    'I am a small frog .'
       __snake_case :   List[str]													=    '.'
       __snake_case :   List[str]													=    tok(__a  )['input_ids']
       __snake_case :   str													=    tok(__a  )['input_ids']

       assert encoded[-1] == encoded_dot[0]



