
'''simple docstring'''


import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_ :  int     =					logging.get_logger(__name__)



def  __snake_case     (    _UpperCAmelCase					:       Tuple):
   UpperCamelCase       =  SwinConfig.from_pretrained(
       '''microsoft/swin-tiny-patch4-window7-224''',      out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''])
   UpperCamelCase       =  MaskFormerConfig(backbone_config=lowerCAmelCase__)

   UpperCamelCase       =  '''huggingface/label-files'''
   if "ade20k-full" in model_name:
      # this should be ok
      UpperCamelCase       =  847
      UpperCamelCase       =  '''maskformer-ade20k-full-id2label.json'''
   elif "ade" in model_name:
      # this should be ok
      UpperCamelCase       =  150
      UpperCamelCase       =  '''ade20k-id2label.json'''
   elif "coco-stuff" in model_name:
      # this should be ok
      UpperCamelCase       =  171
      UpperCamelCase       =  '''maskformer-coco-stuff-id2label.json'''
   elif "coco" in model_name:
      # TODO
      UpperCamelCase       =  133
      UpperCamelCase       =  '''coco-panoptic-id2label.json'''
   elif "cityscapes" in model_name:
      # this should be ok
      UpperCamelCase       =  19
      UpperCamelCase       =  '''cityscapes-id2label.json'''
   elif "vistas" in model_name:
      # this should be ok
      UpperCamelCase       =  65
      UpperCamelCase       =  '''mapillary-vistas-id2label.json'''

   UpperCamelCase       =  json.load(open(hf_hub_download(lowerCAmelCase__,      lowerCAmelCase__,      repo_type='''dataset'''),      '''r'''))
   UpperCamelCase       =  {int(lowerCAmelCase__): v for k, v in idalabel.items()}

   return config



def  __snake_case     (    _UpperCAmelCase					:       Any):
   UpperCamelCase       =  []
   # stem
   # fmt: off
   rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight'''))
   rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias'''))
   rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight'''))
   rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.attn.proj.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.norm2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight'))
         rename_keys.append((f'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', f'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias'))

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

   # FPN
   rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight'''))
   rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight'''))
   rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias'''))
   for source_index, target_index in zip(range(3,      0,      -1),      range(0,      3)):
      rename_keys.append((f'sem_seg_head.adapter_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight'))
      rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight'))
      rename_keys.append((f'sem_seg_head.adapter_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias'))
      rename_keys.append((f'sem_seg_head.layer_{source_index}.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight'))
      rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.weight', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight'))
      rename_keys.append((f'sem_seg_head.layer_{source_index}.norm.bias', f'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias'))
   rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight'''))
   rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias'''))

   # Transformer decoder
   for idx in range(config.decoder_config.decoder_layers):
      # self-attention out projection
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight'))
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias'))
      # cross-attention out projection
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight'))
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias'))
      # MLP 1
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', f'model.transformer_module.decoder.layers.{idx}.fc1.weight'))
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', f'model.transformer_module.decoder.layers.{idx}.fc1.bias'))
      # MLP 2
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', f'model.transformer_module.decoder.layers.{idx}.fc2.weight'))
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', f'model.transformer_module.decoder.layers.{idx}.fc2.bias'))
      # layernorm 1 (self-attention layernorm)
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight'))
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', f'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias'))
      # layernorm 2 (cross-attention layernorm)
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight'))
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', f'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias'))
      # layernorm 3 (final layernorm)
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight'))
      rename_keys.append((f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', f'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias'))

   rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight'''))
   rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias'''))

   # heads on top
   rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight'''))

   rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight'''))
   rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias'''))

   rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight'''))
   rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias'''))

   for i in range(3):
      rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.weight', f'mask_embedder.{i}.0.weight'))
      rename_keys.append((f'sem_seg_head.predictor.mask_embed.layers.{i}.bias', f'mask_embedder.{i}.0.bias'))
   # fmt: on

   return rename_keys



def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Optional[Any]):
   UpperCamelCase       =  dct.pop(lowerCAmelCase__)
   UpperCamelCase       =  val



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  [int(backbone_config.embed_dim * 2**i) for i in range(len(backbone_config.depths))]
   for i in range(len(backbone_config.depths)):
      UpperCamelCase       =  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       =  state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.weight')
         UpperCamelCase       =  state_dict.pop(f'backbone.layers.{i}.blocks.{j}.attn.qkv.bias')
         # next, add query, keys and values (in that order) to the state dict
         UpperCamelCase       =  in_proj_weight[:dim, :]
         UpperCamelCase       =  in_proj_bias[: dim]
         UpperCamelCase       =  in_proj_weight[
             dim : dim * 2, :
         ]
         UpperCamelCase       =  in_proj_bias[
             dim : dim * 2
         ]
         UpperCamelCase       =  in_proj_weight[
             -dim :, :
         ]
         UpperCamelCase       =  in_proj_bias[-dim :]
         # fmt: on



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Optional[Any]):
   # fmt: off
   UpperCamelCase       =  config.decoder_config.hidden_size
   for idx in range(config.decoder_config.decoder_layers):
      # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
      UpperCamelCase       =  state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight')
      UpperCamelCase       =  state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias')
      # next, add query, keys and values (in that order) to the state dict
      UpperCamelCase       =  in_proj_weight[: hidden_size, :]
      UpperCamelCase       =  in_proj_bias[:config.hidden_size]
      UpperCamelCase       =  in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCamelCase       =  in_proj_bias[hidden_size : hidden_size * 2]
      UpperCamelCase       =  in_proj_weight[-hidden_size :, :]
      UpperCamelCase       =  in_proj_bias[-hidden_size :]
      # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
      UpperCamelCase       =  state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight')
      UpperCamelCase       =  state_dict.pop(f'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias')
      # next, add query, keys and values (in that order) to the state dict
      UpperCamelCase       =  in_proj_weight[: hidden_size, :]
      UpperCamelCase       =  in_proj_bias[:config.hidden_size]
      UpperCamelCase       =  in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCamelCase       =  in_proj_bias[hidden_size : hidden_size * 2]
      UpperCamelCase       =  in_proj_weight[-hidden_size :, :]
      UpperCamelCase       =  in_proj_bias[-hidden_size :]
   # fmt: on



def  __snake_case     (    ):
   UpperCamelCase       =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCamelCase       =  Image.open(requests.get(lowerCAmelCase__,      stream=lowerCAmelCase__).raw)
   return im



@torch.no_grad()
def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       List[Any] = False):
   UpperCamelCase       =  get_maskformer_config(lowerCAmelCase__)

   # load original state_dict
   with open(lowerCAmelCase__,      '''rb''') as f:
      UpperCamelCase       =  pickle.load(lowerCAmelCase__)
   UpperCamelCase       =  data['''model''']

   # for name, param in state_dict.items():
   #     print(name, param.shape)

   # rename keys
   UpperCamelCase       =  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__)

   # update to torch tensors
   for key, value in state_dict.items():
      UpperCamelCase       =  torch.from_numpy(lowerCAmelCase__)

   # load 🤗 model
   UpperCamelCase       =  MaskFormerForInstanceSegmentation(lowerCAmelCase__)
   model.eval()

   for name, param in model.named_parameters():
      print(lowerCAmelCase__,      param.shape)

   UpperCamelCase     , UpperCamelCase       =  model.load_state_dict(lowerCAmelCase__,      strict=lowerCAmelCase__)
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(lowerCAmelCase__) == 0, f'Unexpected keys: {unexpected_keys}'

   # verify results
   UpperCamelCase       =  prepare_img()
   if "vistas" in model_name:
      UpperCamelCase       =  65
   elif "cityscapes" in model_name:
      UpperCamelCase       =  6_5535
   else:
      UpperCamelCase       =  255
   UpperCamelCase       =  True if '''ade''' in model_name else False
   UpperCamelCase       =  MaskFormerImageProcessor(ignore_index=lowerCAmelCase__,      reduce_labels=lowerCAmelCase__)

   UpperCamelCase       =  image_processor(lowerCAmelCase__,      return_tensors='''pt''')

   UpperCamelCase       =  model(**lowerCAmelCase__)

   print('''Logits:''',      outputs.class_queries_logits[0, :3, :3])

   if model_name == "maskformer-swin-tiny-ade":
      UpperCamelCase       =  torch.tensor(
          [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]])
   assert torch.allclose(outputs.class_queries_logits[0, :3, :3],      lowerCAmelCase__,      atol=1E-4)
   print('''Looks ok!''')

   if pytorch_dump_folder_path is not None:
      print(f'Saving model and image processor to {pytorch_dump_folder_path}')
      Path(lowerCAmelCase__).mkdir(exist_ok=lowerCAmelCase__)
      model.save_pretrained(lowerCAmelCase__)
      image_processor.save_pretrained(lowerCAmelCase__)

   if push_to_hub:
      print('''Pushing model and image processor to the hub...''')
      model.push_to_hub(f'nielsr/{model_name}')
      image_processor.push_to_hub(f'nielsr/{model_name}')


if __name__ == "__main__":
    snake_case_ :  List[str]     =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='maskformer-swin-tiny-ade',
        type=str,
        help=('Name of the MaskFormer model you\'d like to convert',),
    )
    parser.add_argument(
        '--checkpoint_path',
        default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
        type=str,
        help='Path to the original state dict (.pth file).',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    snake_case_ :  Tuple     =					parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, 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_ :  Union[str, Any]     =					logging.get_logger(__name__)
snake_case_ :  Union[str, Any]     =					{'vocab_file': 'spiece.model'}

snake_case_ :  Any     =					{
    '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_ :  Dict     =					{
    'AI-Sweden/gpt-sw3-126m': 2_048,
    'AI-Sweden/gpt-sw3-350m': 2_048,
    'AI-Sweden/gpt-sw3-1.6b': 2_048,
    'AI-Sweden/gpt-sw3-6.7b': 2_048,
    'AI-Sweden/gpt-sw3-20b': 2_048,
}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							VOCAB_FILES_NAMES
   _snake_case		   =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case		   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		   =							['''input_ids''', '''attention_mask''']







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=False ,   lowerCamelCase__=False ,   lowerCamelCase__=False ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

      UpperCamelCase       =  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'''   )
         UpperCamelCase       =  '''None'''

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

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

      UpperCamelCase       =  do_lower_case
      UpperCamelCase       =  remove_space
      UpperCamelCase       =  keep_accents
      UpperCamelCase       =  vocab_file

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

      # Used for whitespace normalization in input texts
      # fmt : off
      UpperCamelCase       =  {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
      # fmt : on

      # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
      UpperCamelCase       =  re.compile(
          f'[{"".join(map(lowerCamelCase__ ,   list(range(0 ,   9   )   ) + list(range(1_1 ,   3_2   )   ) + list(range(1_2_7 ,   1_6_0   )   ) + [1_6_0, 1_7_3, 8_2_0_3]   )   )}]'   )







   def __getstate__(			self   ):

      '''simple docstring'''







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







   def __setstate__(			self ,   lowerCamelCase__   ):

      '''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   )







   @property
   # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return len(self.sp_model   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.non_printing_characters_re.sub('''''' ,   lowerCamelCase__   )

      # Normalize whitespaces
      UpperCamelCase       =  ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]   )

      # NFC Unicode normalization
      UpperCamelCase       =  unicodedata.normalize('''NFC''' ,   lowerCamelCase__   )
      return text







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.preprocess_text(lowerCamelCase__   )
      return self.sp_model.encode(lowerCamelCase__ ,   out_type=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.PieceToId(lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.IdToPiece(lowerCamelCase__   )







   @staticmethod
   def       UpperCAmelCase				(			lowerCamelCase__   ):

      '''simple docstring'''







      return out_string







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  []
      UpperCamelCase       =  ''''''
      UpperCamelCase       =  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(lowerCamelCase__   ) + token
            UpperCamelCase       =  True
            UpperCamelCase       =  []
         else:
            current_sub_tokens.append(lowerCamelCase__   )
            UpperCamelCase       =  False
      out_string += self.sp_model.decode(lowerCamelCase__   )

      return out_string







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = None   ):

      '''simple docstring'''







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

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

      return (out_vocab_file,)







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False   ):

      '''simple docstring'''







      if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  self.preprocess_text(lowerCamelCase__   )
         UpperCamelCase       =  self.sp_model.encode(lowerCamelCase__   )
      else:
         UpperCamelCase       =  [self.preprocess_text(lowerCamelCase__   ) for t in text]
         UpperCamelCase       =  self.sp_model.encode(lowerCamelCase__   )

      if return_tensors is True or return_tensors == "pt":
         UpperCamelCase       =  torch.tensor(lowerCamelCase__   )

      return token_ids







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return self.sp_model.decode(lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
      UpperCamelCase       =  (
          f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(lowerCamelCase__   ) + f'{self.bos_token}Bot:'
      )
      return self.encode(text=lowerCamelCase__   )





