
'''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()
snake_case									=    logging.get_logger(__name__)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	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"]      ,			)

 lowerCAmelCase__     :   Any     		=	DetaConfig(
     backbone_config=lowerCamelCase_      ,			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=lowerCamelCase_      ,			with_box_refine=lowerCamelCase_      ,			two_stage=lowerCamelCase_      ,			)

 # set labels
 lowerCAmelCase__     :   str     		=	"huggingface/label-files"
 if "o365" in model_name:
  lowerCAmelCase__     :   Union[str, Any]     		=	3_6_6
  lowerCAmelCase__     :   Optional[int]     		=	"object365-id2label.json"
 else:
  lowerCAmelCase__     :   Optional[int]     		=	9_1
  lowerCAmelCase__     :   Optional[Any]     		=	"coco-detection-id2label.json"

 lowerCAmelCase__     :   Optional[int]     		=	num_labels
 lowerCAmelCase__     :   Optional[Any]     		=	json.load(open(cached_download(hf_hub_url(lowerCamelCase_      ,			lowerCamelCase_      ,			repo_type="dataset"			)			)      ,			"r"			)			)
 lowerCAmelCase__     :   List[Any]     		=	{int(lowerCamelCase_			): v for k, v in idalabel.items()}
 lowerCAmelCase__     :   int     		=	idalabel
 lowerCAmelCase__     :   Optional[Any]     		=	{v: k for k, v in idalabel.items()}

 return config


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	[]

 # 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_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	dct.pop(lowerCamelCase_			)
 lowerCAmelCase__     :   Dict     		=	val


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCAmelCase__     :   Optional[Any]     		=	Image.open(requests.get(lowerCamelCase_      ,			stream=lowerCamelCase_			).raw			)

 return im


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	get_deta_config(lowerCamelCase_			)

 # load original state dict
 if model_name == "deta-swin-large":
  lowerCAmelCase__     :   int     		=	hf_hub_download(repo_id="nielsr/deta-checkpoints"      ,			filename="adet_swin_ft.pth"			)
 elif model_name == "deta-swin-large-o365":
  lowerCAmelCase__     :   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'''			)

 lowerCAmelCase__     :   str     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)["model"]

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

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

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

    # finally, create HuggingFace model and load state dict
 lowerCAmelCase__     :   str     		=	DetaForObjectDetection(lowerCamelCase_			)
 model.load_state_dict(lowerCamelCase_			)
 model.eval()

 lowerCAmelCase__     :   List[str]     		=	"cuda" if torch.cuda.is_available() else "cpu"
 model.to(lowerCamelCase_			)

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

 # verify our conversion on image
 lowerCAmelCase__     :   Union[str, Any]     		=	prepare_img()
 lowerCAmelCase__     :   Optional[Any]     		=	processor(images=lowerCamelCase_      ,			return_tensors="pt"			)
 lowerCAmelCase__     :   Any     		=	encoding["pixel_values"]
 lowerCAmelCase__     :   str     		=	model(pixel_values.to(lowerCamelCase_			)			)

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

 # 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__":
     snake_case									=    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."""
     )
     snake_case									=    parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
     import torch


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


snake_case									=    logging.get_logger(__name__)
snake_case									=    {"""vocab_file""": """spiece.model"""}

snake_case									=    {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

snake_case									=    {
    """AI-Sweden/gpt-sw3-126m""": 20_48,
    """AI-Sweden/gpt-sw3-350m""": 20_48,
    """AI-Sweden/gpt-sw3-1.6b""": 20_48,
    """AI-Sweden/gpt-sw3-6.7b""": 20_48,
    """AI-Sweden/gpt-sw3-20b""": 20_48,
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Dict	=			VOCAB_FILES_NAMES
 A_				:				Optional[int]	=			PRETRAINED_VOCAB_FILES_MAP
 A_				:				Optional[Any]	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A_				:				Union[str, Any]	=			["""input_ids""", """attention_mask"""]


 def __init__(    self  : List[str]			,							a__  : Any			,							a__  : Dict=False			,							a__  : List[Any]=False			,							a__  : Dict=False			,							a__  : Any=None			,							a__  : Tuple=None			,							a__  : str=None			,							a__  : Optional[int]=None			,							a__  : Optional[Dict[str, Any]] = None			,							**a__  : Union[str, Any]			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	{} if sp_model_kwargs is None else sp_model_kwargs

  lowerCAmelCase__     :   List[str]     		=	kwargs.get("name_or_path"    )
  if name_or_path is None:
   logger.warning(
       "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
       " you are testing the model, this can safely be ignored"    )
   lowerCAmelCase__     :   Dict     		=	"None"

  # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
  lowerCAmelCase__     :   Tuple     		=	"<|endoftext|>" if eos_token is None else eos_token
  lowerCAmelCase__     :   List[str]     		=	"<unk>" if unk_token is None else unk_token
  if "gpt-sw3-7b" in name_or_path:
   lowerCAmelCase__     :   List[str]     		=	unk_token if pad_token is None else pad_token
   lowerCAmelCase__     :   Any     		=	eos_token if bos_token is None else bos_token
  else:
   lowerCAmelCase__     :   str     		=	"<pad>" if pad_token is None else pad_token
   lowerCAmelCase__     :   Union[str, Any]     		=	"<s>" if bos_token is None else bos_token

  super().__init__(
      do_lower_case=a__			,							remove_space=a__			,							keep_accents=a__			,							bos_token=a__			,							eos_token=a__			,							unk_token=a__			,							pad_token=a__			,							sp_model_kwargs=self.sp_model_kwargs			,							**a__			,							)

  lowerCAmelCase__     :   Dict     		=	do_lower_case
  lowerCAmelCase__     :   Union[str, Any]     		=	remove_space
  lowerCAmelCase__     :   Any     		=	keep_accents
  lowerCAmelCase__     :   List[str]     		=	vocab_file

  lowerCAmelCase__     :   Dict     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(a__    )

  # Used for whitespace normalization in input texts
  # fmt : off
  lowerCAmelCase__     :   List[str]     		=	{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
  # fmt : on

  # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
  lowerCAmelCase__     :   Dict     		=	re.compile(
      F'''[{''.join(map(a__			,							list(range(0			,							9    )    ) + list(range(11			,							32    )    ) + list(range(127			,							160    )    ) + [160, 173, 8203]    )    )}]'''    )


 def __getstate__(    self  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.__dict__.copy()
  lowerCAmelCase__     :   List[str]     		=	None
  return state


 def __setstate__(    self  : List[Any]			,							a__  : Optional[int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	d

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

  lowerCAmelCase__     :   str     		=	spm.SentencePieceProcessor(**self.sp_model_kwargs    )
  self.sp_model.Load(self.vocab_file    )


 @property
 # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
 def    _A	(    self  : str    ):
  '''simple docstring'''

  return len(self.sp_model    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	self.non_printing_characters_re.sub(""			,							a__    )

  # Normalize whitespaces
  lowerCAmelCase__     :   int     		=	"".join([char if char not in self.whitespaces else " " for char in text]    )

  # NFC Unicode normalization
  lowerCAmelCase__     :   Tuple     		=	unicodedata.normalize("NFC"			,							a__    )
  return text


 def    _A	(    self  : str			,							a__  : str			,							**a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	self.preprocess_text(a__    )
  return self.sp_model.encode(a__			,							out_type=a__    )


 def    _A	(    self  : List[str]			,							a__  : str    ):
  '''simple docstring'''

  return self.sp_model.PieceToId(a__    )


 def    _A	(    self  : Optional[int]			,							a__  : int    ):
  '''simple docstring'''

  return self.sp_model.IdToPiece(a__    )


 @staticmethod
 def    _A	(    a__  : str    ):
  '''simple docstring'''

  return out_string


 def    _A	(    self  : int			,							a__  : List[str]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	[]
  lowerCAmelCase__     :   Tuple     		=	""
  lowerCAmelCase__     :   str     		=	False
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
    if not prev_is_special:
     out_string += " "

    out_string += self.sp_model.decode(a__    ) + token
    lowerCAmelCase__     :   Union[str, Any]     		=	True
    lowerCAmelCase__     :   Dict     		=	[]
   else:
    current_sub_tokens.append(a__    )
    lowerCAmelCase__     :   Dict     		=	False
  out_string += self.sp_model.decode(a__    )

  return out_string


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

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


 def    _A	(    self  : str			,							a__  : str			,							a__  : Optional[str] = None    ):
  '''simple docstring'''

  if not os.path.isdir(a__    ):
   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
   return
  lowerCAmelCase__     :   List[str]     		=	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:
    lowerCAmelCase__     :   int     		=	self.sp_model.serialized_model_proto()
    fi.write(a__    )

  return (out_vocab_file,)


 def    _A	(    self  : List[Any]			,							a__  : Union[str, List[str]]			,							a__  : Union[str, bool] = False    ):
  '''simple docstring'''

  if isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   int     		=	self.preprocess_text(a__    )
   lowerCAmelCase__     :   Dict     		=	self.sp_model.encode(a__    )
  else:
   lowerCAmelCase__     :   Union[str, Any]     		=	[self.preprocess_text(a__    ) for t in text]
   lowerCAmelCase__     :   Optional[int]     		=	self.sp_model.encode(a__    )

  if return_tensors is True or return_tensors == "pt":
   lowerCAmelCase__     :   Union[str, Any]     		=	torch.tensor(a__    )

  return token_ids


 def    _A	(    self  : Dict			,							a__  : Union[int, List[int]]    ):
  '''simple docstring'''

  return self.sp_model.decode(a__    )


 def    _A	(    self  : int			,							a__  : "Conversation"    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
  lowerCAmelCase__     :   List[str]     		=	(
      F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(a__    ) + F'''{self.bos_token}Bot:'''
  )
  return self.encode(text=a__    )





