'''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()
_a   :	Optional[Any] 							=   logging.get_logger(__name__)

def _lowercase	(      lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		SwinConfig(
           embed_dim=192							,    depths=(2, 2, 18, 2)							,    num_heads=(6, 12, 24, 48)							,    window_size=12							,    out_features=["stage2", "stage3", "stage4"]							,    )

       __UpperCAmelCase  :				Dict       				=		DetaConfig(
           backbone_config=a_							,    num_queries=900							,    encoder_ffn_dim=2048							,    decoder_ffn_dim=2048							,    num_feature_levels=5							,    assign_first_stage=a_							,    with_box_refine=a_							,    two_stage=a_							,    )

       # set labels
       __UpperCAmelCase  :				Dict       				=		'''huggingface/label-files'''
       if "o365" in model_name:
              __UpperCAmelCase  :				int       				=		366
              __UpperCAmelCase  :				str       				=		'''object365-id2label.json'''
       else:
              __UpperCAmelCase  :				List[Any]       				=		91
              __UpperCAmelCase  :				int       				=		'''coco-detection-id2label.json'''

       __UpperCAmelCase  :				List[Any]       				=		num_labels
       __UpperCAmelCase  :				Union[str, Any]       				=		json.load(open(cached_download(hf_hub_url(a_							,    a_							,    repo_type="dataset"						)						)							,    "r"						)						)
       __UpperCAmelCase  :				List[Any]       				=		{int(a_						): v for k, v in idalabel.items()}
       __UpperCAmelCase  :				Dict       				=		idalabel
       __UpperCAmelCase  :				List[str]       				=		{v: k for k, v in idalabel.items()}

       return config

def _lowercase	(      lowerCamelCase__						)					->       List[str]:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		[]

       # 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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		dct.pop(a_						)
       __UpperCAmelCase  :				Tuple       				=		val

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
       for i in range(len(backbone_config.depths						)						):
              __UpperCAmelCase  :				Optional[int]       				=		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)
                     __UpperCAmelCase  :				List[str]       				=		state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""						)
                     __UpperCAmelCase  :				Dict       				=		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
                     __UpperCAmelCase  :				Tuple       				=		in_proj_weight[:dim, :]
                     __UpperCAmelCase  :				List[str]       				=		in_proj_bias[: dim]
                     __UpperCAmelCase  :				Optional[Any]       				=		in_proj_weight[
                         dim : dim * 2, :
                     ]
                     __UpperCAmelCase  :				Optional[int]       				=		in_proj_bias[
                         dim : dim * 2
                     ]
                     __UpperCAmelCase  :				Dict       				=		in_proj_weight[
                         -dim :, :
                     ]
                     __UpperCAmelCase  :				Union[str, Any]       				=		in_proj_bias[-dim :]
                     # fmt: on

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		config.d_model
       for i in range(config.decoder_layers						):
              # read in weights + bias of input projection layer of self-attention
              __UpperCAmelCase  :				Union[str, Any]       				=		state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
              __UpperCAmelCase  :				Any       				=		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
              __UpperCAmelCase  :				Dict       				=		in_proj_weight[:hidden_size, :]
              __UpperCAmelCase  :				Any       				=		in_proj_bias[:hidden_size]
              __UpperCAmelCase  :				Any       				=		in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              __UpperCAmelCase  :				Any       				=		in_proj_bias[hidden_size : hidden_size * 2]
              __UpperCAmelCase  :				Optional[int]       				=		in_proj_weight[-hidden_size:, :]
              __UpperCAmelCase  :				Union[str, Any]       				=		in_proj_bias[-hidden_size:]

def _lowercase	(      )					->       Tuple:





       """simple docstring"""


       __UpperCAmelCase  :				Any       				=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       __UpperCAmelCase  :				List[Any]       				=		Image.open(requests.get(a_							,    stream=a_						).raw						)

       return im

@torch.no_grad()
def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Tuple       				=		get_deta_config(a_						)

       # load original state dict
       if model_name == "deta-swin-large":
              __UpperCAmelCase  :				Optional[Any]       				=		hf_hub_download(repo_id="nielsr/deta-checkpoints"							,    filename="adet_swin_ft.pth"						)
       elif model_name == "deta-swin-large-o365":
              __UpperCAmelCase  :				Optional[Any]       				=		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"""						)

       __UpperCAmelCase  :				int       				=		torch.load(a_							,    map_location="cpu"						)['''model''']

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

       # rename keys
       __UpperCAmelCase  :				Union[str, Any]       				=		create_rename_keys(a_						)
       for src, dest in rename_keys:
              rename_key(a_							,    a_							,    a_						)
       read_in_swin_q_k_v(a_							,    config.backbone_config						)
       read_in_decoder_q_k_v(a_							,    a_						)

       # fix some prefixes
       for key in state_dict.copy().keys():
              if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                     __UpperCAmelCase  :				List[str]       				=		state_dict.pop(a_						)
                     __UpperCAmelCase  :				List[str]       				=		val
              if "input_proj" in key:
                     __UpperCAmelCase  :				Optional[Any]       				=		state_dict.pop(a_						)
                     __UpperCAmelCase  :				str       				=		val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                     __UpperCAmelCase  :				Dict       				=		state_dict.pop(a_						)
                     __UpperCAmelCase  :				str       				=		val

    # finally, create HuggingFace model and load state dict
       __UpperCAmelCase  :				Tuple       				=		DetaForObjectDetection(a_						)
       model.load_state_dict(a_						)
       model.eval()

       __UpperCAmelCase  :				Any       				=		'''cuda''' if torch.cuda.is_available() else '''cpu'''
       model.to(a_						)

       # load image processor
       __UpperCAmelCase  :				Tuple       				=		DetaImageProcessor(format="coco_detection"						)

       # verify our conversion on image
       __UpperCAmelCase  :				int       				=		prepare_img()
       __UpperCAmelCase  :				List[str]       				=		processor(images=a_							,    return_tensors="pt"						)
       __UpperCAmelCase  :				int       				=		encoding['''pixel_values''']
       __UpperCAmelCase  :				List[str]       				=		model(pixel_values.to(a_						)						)

       # verify logits
       print("Logits:"							,    outputs.logits[0, :3, :3]						)
       print("Boxes:"							,    outputs.pred_boxes[0, :3, :3]						)
       if model_name == "deta-swin-large":
              __UpperCAmelCase  :				str       				=		torch.tensor(
                  [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]						)
              __UpperCAmelCase  :				Optional[int]       				=		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":
              __UpperCAmelCase  :				Optional[int]       				=		torch.tensor(
                  [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]						)
              __UpperCAmelCase  :				List[Any]       				=		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(a_						)							,    atol=1e-4						)
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]							,    expected_boxes.to(a_						)							,    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(a_						).mkdir(exist_ok=a_						)
              model.save_pretrained(a_						)
              processor.save_pretrained(a_						)

       # 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__":
   _a   :	Tuple 							=   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."
   )
   _a   :	List[str] 							=   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


_a   :	str 							=   logging.get_logger(__name__)

_a   :	Tuple 							=   "▁"

_a   :	Optional[int] 							=   {"vocab_file": "sentencepiece.bpe.model"}

_a   :	Tuple 							=   {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_a   :	Optional[Any] 							=   {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}






class 		__A       (__magic_name__   ):
       snake_case       :Union[str, Any]				    =					VOCAB_FILES_NAMES
       snake_case       :Any				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Tuple				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :Optional[int]				    =					["input_ids", "attention_mask"]






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="<unk>"				,     UpperCamelCase_="<pad>"				,     UpperCamelCase_="<mask>"				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              # Mask token behave like a normal word, i.e. include the space before it
              __UpperCAmelCase  :				Optional[int]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else mask_token

              __UpperCAmelCase  :				int       				=		{} if sp_model_kwargs is None else sp_model_kwargs

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

              __UpperCAmelCase  :				Optional[int]       				=		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
              self.sp_model.Load(str(UpperCamelCase_      )      )
              __UpperCAmelCase  :				Union[str, Any]       				=		vocab_file

              # Original fairseq vocab and spm vocab must be "aligned":
              # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
              # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
              # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
              # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

              # Mimic fairseq token-to-id alignment for the first 4 token
              __UpperCAmelCase  :				Optional[Any]       				=		{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

              # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
              __UpperCAmelCase  :				List[Any]       				=		1

              __UpperCAmelCase  :				Optional[Any]       				=		len(self.sp_model      ) + self.fairseq_offset
              __UpperCAmelCase  :				str       				=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def __getstate__(      self      ):
              __UpperCAmelCase  :				List[str]       				=		self.__dict__.copy()
              __UpperCAmelCase  :				str       				=		None
              __UpperCAmelCase  :				str       				=		self.sp_model.serialized_model_proto()
              return state






       def __setstate__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		d

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

              __UpperCAmelCase  :				Dict       				=		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):

              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              __UpperCAmelCase  :				List[Any]       				=		[self.cls_token_id]
              __UpperCAmelCase  :				Union[str, Any]       				=		[self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False      ):

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

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				Dict       				=		[self.sep_token_id]
              __UpperCAmelCase  :				List[Any]       				=		[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 			_snake_case						(      self      ):
              return len(self.sp_model      ) + self.fairseq_offset + 1  # Add the <mask> token






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		{self.convert_ids_to_tokens(UpperCamelCase_      ): i for i in range(self.vocab_size      )}
              vocab.update(self.added_tokens_encoder      )
              return vocab






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              return self.sp_model.encode(UpperCamelCase_				,     out_type=UpperCamelCase_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              __UpperCAmelCase  :				Optional[int]       				=		self.sp_model.PieceToId(UpperCamelCase_      )

              # Need to return unknown token if the SP model returned 0
              return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if index in self.fairseq_ids_to_tokens:
                     return self.fairseq_ids_to_tokens[index]
              return self.sp_model.IdToPiece(index - self.fairseq_offset      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		"".join(UpperCamelCase_      ).replace(UpperCamelCase_				,     " "      ).strip()
              return out_string







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              if not os.path.isdir(UpperCamelCase_      ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
                     return
              __UpperCAmelCase  :				List[str]       				=		os.path.join(
                  UpperCamelCase_				,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

              if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase_      ) and os.path.isfile(self.vocab_file      ):
                     copyfile(self.vocab_file				,     UpperCamelCase_      )
              elif not os.path.isfile(self.vocab_file      ):
                     with open(UpperCamelCase_				,     "wb"      ) as fi:
                            __UpperCAmelCase  :				Optional[int]       				=		self.sp_model.serialized_model_proto()
                            fi.write(UpperCamelCase_      )

              return (out_vocab_file,)






