




'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							:       Optional[int]						=			logging.get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	List[Any]					):

       '''simple docstring'''






       snake_case_      =				SwinConfig(
           embed_dim=1_9_2							,    depths=(2, 2, 1_8, 2)							,    num_heads=(6, 1_2, 2_4, 4_8)							,    window_size=1_2							,    out_features=["stage2", "stage3", "stage4"]							,    )

       snake_case_      =				DetaConfig(
           backbone_config=snake_case							,    num_queries=9_0_0							,    encoder_ffn_dim=2_0_4_8							,    decoder_ffn_dim=2_0_4_8							,    num_feature_levels=5							,    assign_first_stage=snake_case							,    with_box_refine=snake_case							,    two_stage=snake_case							,    )

       # set labels
       snake_case_      =				"huggingface/label-files"
       if "o365" in model_name:
              snake_case_      =				3_6_6
              snake_case_      =				"object365-id2label.json"
       else:
              snake_case_      =				9_1
              snake_case_      =				"coco-detection-id2label.json"

       snake_case_      =				num_labels
       snake_case_      =				json.load(open(cached_download(hf_hub_url(snake_case							,    snake_case							,    repo_type="dataset"					)					)							,    "r"					)					)
       snake_case_      =				{int(snake_case					): v for k, v in idalabel.items()}
       snake_case_      =				idalabel
       snake_case_      =				{v: k for k, v in idalabel.items()}

       return config


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]					):

       '''simple docstring'''






       snake_case_      =				[]

       # stem
       # fmt: off
       rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")					)
       rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")					)
       rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")					)
       rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")					)
       # stages
       for i in range(len(config.backbone_config.depths					)					):
              for j in range(config.backbone_config.depths[i]					):
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')					)

              if i < 3:
                     rename_keys.append((f'backbone.0.body.layers.{i}.downsample.reduction.weight', f'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.weight', f'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')					)
                     rename_keys.append((f'backbone.0.body.layers.{i}.downsample.norm.bias', f'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')					)

       rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")					)
       rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")					)
       rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")					)
       rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")					)
       rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")					)
       rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")					)

       # transformer encoder
       for i in range(config.encoder_layers					):
              rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')					)
              rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')					)

       # transformer decoder
       for i in range(config.decoder_layers					):
              rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')					)
              rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')					)

       # fmt: on

       return rename_keys


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	str							,    snake_case							:	Union[str, Any]					):

       '''simple docstring'''






       snake_case_      =				dct.pop(snake_case					)
       snake_case_      =				val


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]					):

       '''simple docstring'''






       snake_case_      =				[int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
       for i in range(len(backbone_config.depths					)					):
              snake_case_      =				num_features[i]
              for j in range(backbone_config.depths[i]					):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     snake_case_      =				state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'					)
                     snake_case_      =				state_dict.pop(f'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'					)
                     # next, add query, keys and values (in that order) to the state dict
                     snake_case_      =				in_proj_weight[:dim, :]
                     snake_case_      =				in_proj_bias[: dim]
                     snake_case_      =				in_proj_weight[
                         dim : dim * 2, :
                     ]
                     snake_case_      =				in_proj_bias[
                         dim : dim * 2
                     ]
                     snake_case_      =				in_proj_weight[
                         -dim :, :
                     ]
                     snake_case_      =				in_proj_bias[-dim :]
                     # fmt: on


def 					UpperCamelCase_(     snake_case							:	Tuple							,    snake_case							:	int					):

       '''simple docstring'''






       snake_case_      =				config.d_model
       for i in range(config.decoder_layers					):
              # read in weights + bias of input projection layer of self-attention
              snake_case_      =				state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'					)
              snake_case_      =				state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias'					)
              # next, add query, keys and values (in that order) to the state dict
              snake_case_      =				in_proj_weight[:hidden_size, :]
              snake_case_      =				in_proj_bias[:hidden_size]
              snake_case_      =				in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              snake_case_      =				in_proj_bias[hidden_size : hidden_size * 2]
              snake_case_      =				in_proj_weight[-hidden_size:, :]
              snake_case_      =				in_proj_bias[-hidden_size:]


def 					UpperCamelCase_(     ):

       '''simple docstring'''






       snake_case_      =				"http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case_      =				Image.open(requests.get(snake_case							,    stream=snake_case					).raw					)

       return im


@torch.no_grad()
def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	int							,    snake_case							:	Optional[Any]					):

       '''simple docstring'''






       snake_case_      =				get_deta_config(snake_case					)

       # load original state dict
       if model_name == "deta-swin-large":
              snake_case_      =				hf_hub_download(repo_id="nielsr/deta-checkpoints"							,    filename="adet_swin_ft.pth"					)
       elif model_name == "deta-swin-large-o365":
              snake_case_      =				hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"							,    filename="deta_swin_pt_o365.pth"					)
       else:
              raise ValueError(f'Model name {model_name} not supported'					)

       snake_case_      =				torch.load(snake_case							,    map_location="cpu"					)["model"]

       # original state dict
       for name, param in state_dict.items():
              print(snake_case							,    param.shape					)

       # rename keys
       snake_case_      =				create_rename_keys(snake_case					)
       for src, dest in rename_keys:
              rename_key(snake_case							,    snake_case							,    snake_case					)
       read_in_swin_q_k_v(snake_case							,    config.backbone_config					)
       read_in_decoder_q_k_v(snake_case							,    snake_case					)

       # fix some prefixes
       for key in state_dict.copy().keys():
              if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                     snake_case_      =				state_dict.pop(snake_case					)
                     snake_case_      =				val
              if "input_proj" in key:
                     snake_case_      =				state_dict.pop(snake_case					)
                     snake_case_      =				val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     snake_case_      =				state_dict.pop(snake_case					)
                     snake_case_      =				val

    # finally, create HuggingFace model and load state dict
       snake_case_      =				DetaForObjectDetection(snake_case					)
       model.load_state_dict(snake_case					)
       model.eval()

       snake_case_      =				"cuda" if torch.cuda.is_available() else "cpu"
       model.to(snake_case					)

       # load image processor
       snake_case_      =				DetaImageProcessor(format="coco_detection"					)

       # verify our conversion on image
       snake_case_      =				prepare_img()
       snake_case_      =				processor(images=snake_case							,    return_tensors="pt"					)
       snake_case_      =				encoding["pixel_values"]
       snake_case_      =				model(pixel_values.to(snake_case					)					)

       # verify logits
       print("Logits:"							,    outputs.logits[0, :3, :3]					)
       print("Boxes:"							,    outputs.pred_boxes[0, :3, :3]					)
       if model_name == "deta-swin-large":
              snake_case_      =				torch.tensor(
                  [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]					)
              snake_case_      =				torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]					)
       elif model_name == "deta-swin-large-o365":
              snake_case_      =				torch.tensor(
                  [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]					)
              snake_case_      =				torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]					)
       assert torch.allclose(outputs.logits[0, :3, :3]							,    expected_logits.to(snake_case					)							,    atol=1e-4					)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,    expected_boxes.to(snake_case					)							,    atol=1e-4					)
       print("Everything ok!"					)

       if pytorch_dump_folder_path:
              # Save model and processor
              logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'					)
              Path(snake_case					).mkdir(exist_ok=snake_case					)
              model.save_pretrained(snake_case					)
              processor.save_pretrained(snake_case					)

       # Push to hub
       if push_to_hub:
              print("Pushing model and processor to hub..."					)
              model.push_to_hub(f'jozhang97/{model_name}'					)
              processor.push_to_hub(f'jozhang97/{model_name}'					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE							:       str						=			argparse.ArgumentParser()

  parser.add_argument(
      "--model_name",
      type=str,
      default="deta-swin-large",
      choices=["deta-swin-large", "deta-swin-large-o365"],
      help="Name of the model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      help="Path to the folder to output PyTorch model.",
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )
  _SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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 AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE							:       Tuple						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{"vocab_file": "sentencepiece.bpe.model"}

_SCREAMING_SNAKE_CASE							:       Tuple						=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_SCREAMING_SNAKE_CASE							:       Any						=			"▁"




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				List[Any]							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				Tuple							=   ["input_ids", "attention_mask"]




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

										'''simple docstring'''




										snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else mask_token

										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=a__    ,							eos_token=a__    ,							unk_token=a__    ,							sep_token=a__    ,							cls_token=a__    ,							pad_token=a__    ,							mask_token=a__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a__    ,							)

										snake_case_      =				vocab_file
										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(str(a__							)							)

										snake_case_      =				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										snake_case_      =				len(self.sp_model							) - 1
										snake_case_      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										snake_case_      =				[self.cls_token_id]
										snake_case_      =				[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = False							)    ->    List[int]:

										'''simple docstring'''




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

										if token_ids_a is None:
																	return [1] + ([0] * len(a__							)) + [1]
										return [1] + ([0] * len(a__							)) + [1, 1] + ([0] * len(a__							)) + [1]




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										snake_case_      =				[self.sep_token_id]
										snake_case_      =				[self.cls_token_id]

										if token_ids_a is None:
																	return len(cls + token_ids_a + sep							) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




			@property
			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										return len(self.sp_model							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

										'''simple docstring'''




										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										snake_case_      =				self.sp_model.PieceToId(a__							)

										return spm_id if spm_id else self.unk_token_id




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




										if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(a__							)




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

										'''simple docstring'''




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




			def __getstate__( self							)    ->    Dict:

										'''simple docstring'''




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




			def __setstate__( self    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				d

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

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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple[str]:

										'''simple docstring'''




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

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

										return (out_vocab_file,)




