






'''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__     =       logging.get_logger()






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = len(list(m.modules()				)				) == 1 or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.Convad				) or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.BatchNormad				)
        if has_not_submodules:
              self.traced.append(_SCREAMING_SNAKE_CASE				)

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        for m in self.module.modules():
              self.handles.append(m.register_forward_hook(self._forward_hook				)				)
        self.module(_SCREAMING_SNAKE_CASE				)
        [x.remove() for x in self.handles]
        return self


  @property
  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        return list(filter(lambda _SCREAMING_SNAKE_CASE				: len(list(x.state_dict().keys()				)				) > 0      ,		self.traced				)				)






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42
  lowercase							=				1
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				True

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = Tracker(self.dest				)(_SCREAMING_SNAKE_CASE				).parametrized
        UpperCamelCase      = Tracker(self.src				)(_SCREAMING_SNAKE_CASE				).parametrized

        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.src_skip      ,		_SCREAMING_SNAKE_CASE				)				)
        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.dest_skip      ,		_SCREAMING_SNAKE_CASE				)				)

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

        for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              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      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        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(_SCREAMING_SNAKE_CASE				) + 1
              feature_blocks.append((F"res{block_index}", v)				)

        UpperCamelCase      = nn.ModuleDict(_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






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






class     a_						(      lowerCamelCase    ):

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = x.split("""-"""				)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)


  def __getitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Callable[[], Tuple[nn.Module, Dict]]:



        """simple docstring"""






        if x not in self:
              UpperCamelCase      = self.convert_name_to_timm(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = partial(lambda: (timm.create_model(_SCREAMING_SNAKE_CASE      ,		pretrained=_SCREAMING_SNAKE_CASE				).eval(), None)				)

        else:
              UpperCamelCase      = super().__getitem__(_SCREAMING_SNAKE_CASE				)

        return val






class     a_						(      lowerCamelCase    ):

  def __getitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Callable[[], nn.Module]:



        """simple docstring"""






        if "seer" in x and "in1k" not in x:
              UpperCamelCase      = RegNetModel
        else:
              UpperCamelCase      = RegNetForImageClassification
        return val







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Tuple:
      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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = True		,						)->			List[Any]:
      print(F"Converting {name}..."   )
      with torch.no_grad():
            UpperCamelCase  ,UpperCamelCase      = from_model_func()
            UpperCamelCase      = our_model_func(__UpperCamelCase   ).eval()
            UpperCamelCase      = ModuleTransfer(src=__UpperCamelCase		,						dest=__UpperCamelCase		,						raise_if_mismatch=__UpperCamelCase   )
            UpperCamelCase      = torch.randn((1, 3, 224, 224)   )
            module_transfer(__UpperCamelCase   )

      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(__UpperCamelCase		,						our_model.state_dict()		,						__UpperCamelCase   )
            our_model.load_state_dict(__UpperCamelCase   )

      UpperCamelCase      = our_model(__UpperCamelCase		,						output_hidden_states=__UpperCamelCase   )
      UpperCamelCase      = (
          our_outputs.logits if isinstance(__UpperCamelCase		,						__UpperCamelCase   ) else our_outputs.last_hidden_state
      )

      UpperCamelCase      = from_model(__UpperCamelCase   )
      UpperCamelCase      = 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:
            UpperCamelCase      = 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		,						)

            UpperCamelCase      = 224 if """seer""" not in name else 384
            # we can use the convnext one
            UpperCamelCase      = 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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = None		,						__UpperCamelCase = True   )->			int:
      UpperCamelCase      = """imagenet-1k-id2label.json"""
      UpperCamelCase      = 1000
      UpperCamelCase      = (1, num_labels)

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

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

      UpperCamelCase      = partial(__UpperCamelCase		,						num_labels=__UpperCamelCase		,						idalabel=__UpperCamelCase		,						labelaid=__UpperCamelCase   )

      UpperCamelCase      = {
          """regnet-x-002""": ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]		,						hidden_sizes=[24, 56, 152, 368]		,						groups_width=8		,						layer_type="""x"""   ),
          """regnet-x-004""": ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]		,						hidden_sizes=[32, 64, 160, 384]		,						groups_width=16		,						layer_type="""x"""   ),
          """regnet-x-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]		,						hidden_sizes=[48, 96, 240, 528]		,						groups_width=24		,						layer_type="""x"""   ),
          """regnet-x-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]		,						hidden_sizes=[64, 128, 288, 672]		,						groups_width=16		,						layer_type="""x"""   ),
          """regnet-x-016""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]		,						hidden_sizes=[72, 168, 408, 912]		,						groups_width=24		,						layer_type="""x"""   ),
          """regnet-x-032""": ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]		,						hidden_sizes=[96, 192, 432, 1008]		,						groups_width=48		,						layer_type="""x"""   ),
          """regnet-x-040""": ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]		,						hidden_sizes=[80, 240, 560, 1360]		,						groups_width=40		,						layer_type="""x"""   ),
          """regnet-x-064""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]		,						hidden_sizes=[168, 392, 784, 1624]		,						groups_width=56		,						layer_type="""x"""   ),
          """regnet-x-080""": ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]		,						hidden_sizes=[80, 240, 720, 1920]		,						groups_width=120		,						layer_type="""x"""   ),
          """regnet-x-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]		,						hidden_sizes=[224, 448, 896, 2240]		,						groups_width=112		,						layer_type="""x"""   ),
          """regnet-x-160""": ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]		,						hidden_sizes=[256, 512, 896, 2048]		,						groups_width=128		,						layer_type="""x"""   ),
          """regnet-x-320""": ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]		,						hidden_sizes=[336, 672, 1344, 2520]		,						groups_width=168		,						layer_type="""x"""   ),
          # y variant
          """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]		,						hidden_sizes=[24, 56, 152, 368]		,						groups_width=8   ),
          """regnet-y-004""": ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]		,						hidden_sizes=[48, 104, 208, 440]		,						groups_width=8   ),
          """regnet-y-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]		,						hidden_sizes=[48, 112, 256, 608]		,						groups_width=16   ),
          """regnet-y-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]		,						hidden_sizes=[64, 128, 320, 768]		,						groups_width=16   ),
          """regnet-y-016""": ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]		,						hidden_sizes=[48, 120, 336, 888]		,						groups_width=24   ),
          """regnet-y-032""": ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]		,						hidden_sizes=[72, 216, 576, 1512]		,						groups_width=24   ),
          """regnet-y-040""": ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]		,						hidden_sizes=[128, 192, 512, 1088]		,						groups_width=64   ),
          """regnet-y-064""": ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]		,						hidden_sizes=[144, 288, 576, 1296]		,						groups_width=72   ),
          """regnet-y-080""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]		,						hidden_sizes=[168, 448, 896, 2016]		,						groups_width=56   ),
          """regnet-y-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]		,						hidden_sizes=[224, 448, 896, 2240]		,						groups_width=112   ),
          """regnet-y-160""": ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]		,						hidden_sizes=[224, 448, 1232, 3024]		,						groups_width=112   ),
          """regnet-y-320""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]		,						hidden_sizes=[232, 696, 1392, 3712]		,						groups_width=232   ),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]		,						hidden_sizes=[232, 696, 1392, 3712]		,						groups_width=232   ),
          """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]		,						hidden_sizes=[328, 984, 1968, 4920]		,						groups_width=328   ),
          """regnet-y-1280-seer""": RegNetConfig(
              depths=[2, 7, 17, 1]		,						hidden_sizes=[528, 1056, 2904, 7392]		,						groups_width=264   ),
          """regnet-y-2560-seer""": RegNetConfig(
              depths=[3, 7, 16, 1]		,						hidden_sizes=[640, 1696, 2544, 5088]		,						groups_width=640   ),
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]		,						hidden_sizes=[2020, 4040, 11110, 28280]		,						groups_width=1010   ),
          # finetuned on imagenet
          """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]		,						hidden_sizes=[232, 696, 1392, 3712]		,						groups_width=232   ),
          """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]		,						hidden_sizes=[328, 984, 1968, 4920]		,						groups_width=328   ),
          """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]		,						hidden_sizes=[528, 1056, 2904, 7392]		,						groups_width=264   ),
          """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]		,						hidden_sizes=[640, 1696, 2544, 5088]		,						groups_width=640   ),
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]		,						hidden_sizes=[2020, 4040, 11110, 28280]		,						groups_width=1010   ),
      }

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

      def load_using_classy_vision(__UpperCamelCase		,						__UpperCamelCase   ) -> Tuple[nn.Module, Dict]:
            UpperCamelCase      = torch.hub.load_state_dict_from_url(__UpperCamelCase		,						model_dir=str(__UpperCamelCase   )		,						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(__UpperCamelCase   )
            return model.eval(), model_state_dict["heads"]

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

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

      UpperCamelCase      = 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()   )		,						)

      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""		,						lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27		,						group_width=1010		,						w_a=1744		,						w_a=620.83		,						w_m=2.52   )   )   )		,						)

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

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

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

      UpperCamelCase      = 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=1010		,						w_a=1744		,						w_a=620.83		,						w_m=2.52   )   )   )		,						)

      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__":
     SCREAMING_SNAKE_CASE__     =       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__     =       parser.parse_args()

     SCREAMING_SNAKE_CASE__     =       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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'spm_char.model'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

SCREAMING_SNAKE_CASE__     =       {
    'microsoft/speecht5_asr': 1_0_2_4,
    'microsoft/speecht5_tts': 1_0_2_4,
    'microsoft/speecht5_vc': 1_0_2_4,
}






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE="<s>"      ,		_SCREAMING_SNAKE_CASE="</s>"      ,		_SCREAMING_SNAKE_CASE="<unk>"      ,		_SCREAMING_SNAKE_CASE="<pad>"      ,		_SCREAMING_SNAKE_CASE = None      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				None:



        """simple docstring"""






        UpperCamelCase      = {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_SCREAMING_SNAKE_CASE      ,		eos_token=_SCREAMING_SNAKE_CASE      ,		unk_token=_SCREAMING_SNAKE_CASE      ,		pad_token=_SCREAMING_SNAKE_CASE      ,		sp_model_kwargs=self.sp_model_kwargs      ,		**_SCREAMING_SNAKE_CASE      ,		)

        UpperCamelCase      = vocab_file

        UpperCamelCase      = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return self.sp_model.get_piece_size()

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
        vocab.update(self.added_tokens_encoder				)
        return vocab

  def __getstate__(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.__dict__.copy()
        UpperCamelCase      = None
        return state

  def __setstate__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = d

        # for backward compatibility
        if not hasattr(self      ,		"""sp_model_kwargs"""				):
              UpperCamelCase      = {}

        UpperCamelCase      = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(self.vocab_file				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        return self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,		out_type=_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE				)
        return token

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = []
        UpperCamelCase      = """"""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				) + token
                    UpperCamelCase      = []
              else:
                    current_sub_tokens.append(_SCREAMING_SNAKE_CASE				)
        out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				)
        return out_string.strip()

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=None				)  ->				List[int]:



        """simple docstring"""






        if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return token_ids_a + token_ids_a + [self.eos_token_id]

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False				)  ->				List[int]:



        """simple docstring"""






        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_SCREAMING_SNAKE_CASE      ,		token_ids_a=_SCREAMING_SNAKE_CASE      ,		already_has_special_tokens=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = [1]
        if token_ids_a is None:
              return ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones
        return ([0] * len(_SCREAMING_SNAKE_CASE				)) + ([0] * len(_SCREAMING_SNAKE_CASE				)) + suffix_ones


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				Tuple[str]:



        """simple docstring"""






        if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
              return
        UpperCamelCase      = os.path.join(
            _SCREAMING_SNAKE_CASE      ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

        if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
              copyfile(self.vocab_file      ,		_SCREAMING_SNAKE_CASE				)
        elif not os.path.isfile(self.vocab_file				):
              with open(_SCREAMING_SNAKE_CASE      ,		"""wb"""				) as fi:
                    UpperCamelCase      = self.sp_model.serialized_model_proto()
                    fi.write(_SCREAMING_SNAKE_CASE				)

        return (out_vocab_file,)
