






'''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()
UpperCamelCase    :      Optional[Any]  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			str						):
						lowerCamelCase__         =  SwinConfig.from_pretrained(
						    """microsoft/swin-tiny-patch4-window7-224"""   ,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]						)
						lowerCamelCase__         =  MaskFormerConfig(backbone_config=__lowerCAmelCase						)

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

						lowerCamelCase__         =  json.load(open(hf_hub_download(__lowerCAmelCase   ,    __lowerCAmelCase   ,    repo_type="""dataset"""						)   ,    """r"""						)						)
						lowerCamelCase__         =  {int(__lowerCAmelCase						): v for k, v in idalabel.items()}

						return config
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  []
						# 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 						A__			(				__lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  dct.pop(__lowerCAmelCase						)
						lowerCamelCase__         =  val
def 						A__			(				__lowerCAmelCase		:			Dict   ,    __lowerCAmelCase		:			List[str]						):
						lowerCamelCase__         =  [int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
						for i in range(len(backbone_config.depths						)						):
												lowerCamelCase__         =  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__         =  state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''						)
																		lowerCamelCase__         =  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
																		lowerCamelCase__         =  in_proj_weight[:dim, :]
																		lowerCamelCase__         =  in_proj_bias[: dim]
																		lowerCamelCase__         =  in_proj_weight[
																		    dim : dim * 2, :
																		]
																		lowerCamelCase__         =  in_proj_bias[
																		    dim : dim * 2
																		]
																		lowerCamelCase__         =  in_proj_weight[
																		    -dim :, :
																		]
																		lowerCamelCase__         =  in_proj_bias[-dim :]
																		# fmt: on
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Dict						):
						# fmt: off
						lowerCamelCase__         =  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)
												lowerCamelCase__         =  state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''						)
												lowerCamelCase__         =  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
												lowerCamelCase__         =  in_proj_weight[: hidden_size, :]
												lowerCamelCase__         =  in_proj_bias[:config.hidden_size]
												lowerCamelCase__         =  in_proj_weight[hidden_size : hidden_size * 2, :]
												lowerCamelCase__         =  in_proj_bias[hidden_size : hidden_size * 2]
												lowerCamelCase__         =  in_proj_weight[-hidden_size :, :]
												lowerCamelCase__         =  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)
												lowerCamelCase__         =  state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''						)
												lowerCamelCase__         =  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
												lowerCamelCase__         =  in_proj_weight[: hidden_size, :]
												lowerCamelCase__         =  in_proj_bias[:config.hidden_size]
												lowerCamelCase__         =  in_proj_weight[hidden_size : hidden_size * 2, :]
												lowerCamelCase__         =  in_proj_bias[hidden_size : hidden_size * 2]
												lowerCamelCase__         =  in_proj_weight[-hidden_size :, :]
												lowerCamelCase__         =  in_proj_bias[-hidden_size :]
						# fmt: on
def 						A__			(				):
						lowerCamelCase__         =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowerCamelCase__         =  Image.open(requests.get(__lowerCAmelCase   ,    stream=__lowerCAmelCase						).raw						)
						return im
@torch.no_grad()
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			bool = False						):
						lowerCamelCase__         =  get_maskformer_config(__lowerCAmelCase						)

						# load original state_dict
						with open(__lowerCAmelCase   ,    """rb"""						) as f:
												lowerCamelCase__         =  pickle.load(__lowerCAmelCase						)
						lowerCamelCase__         =  data["""model"""]

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

						# rename keys
						lowerCamelCase__         =  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():
												lowerCamelCase__         =  torch.from_numpy(__lowerCAmelCase						)

						# load 🤗 model
						lowerCamelCase__         =  MaskFormerForInstanceSegmentation(__lowerCAmelCase						)
						model.eval()

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

						lowerCamelCase__       ,			lowerCamelCase__         =  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
						lowerCamelCase__         =  prepare_img()
						if "vistas" in model_name:
												lowerCamelCase__         =  65
						elif "cityscapes" in model_name:
												lowerCamelCase__         =  6_5535
						else:
												lowerCamelCase__         =  255
						lowerCamelCase__         =  True if """ade""" in model_name else False
						lowerCamelCase__         =  MaskFormerImageProcessor(ignore_index=__lowerCAmelCase   ,    reduce_labels=__lowerCAmelCase						)

						lowerCamelCase__         =  image_processor(__lowerCAmelCase   ,    return_tensors="""pt"""						)

						lowerCamelCase__         =  model(**__lowerCAmelCase						)

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

						if model_name == "maskformer-swin-tiny-ade":
												lowerCamelCase__         =  torch.tensor(
												    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]						)
						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__":
							UpperCamelCase    :      Union[str, Any]  			=		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.'
							)

							UpperCamelCase    :      Optional[Any]  			=		parser.parse_args()
							convert_maskformer_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)









'''simple docstring'''







import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase = "▁"   ,_lowerCAmelCase = True   ,_lowerCAmelCase = "<unk>"   ,_lowerCAmelCase = "</s>"   ,_lowerCAmelCase = "<pad>"   ,):
								lowerCamelCase__         =  {
								    """pad""": {"""id""": 0, """token""": pad_token},
								    """eos""": {"""id""": 1, """token""": eos_token},
								    """unk""": {"""id""": 2, """token""": unk_token},
								}

								lowerCamelCase__         =  [None] * len(self.special_tokens       )
								for token_dict in self.special_tokens.values():
														lowerCamelCase__         =  token_dict["""token"""]

								lowerCamelCase__         =  Tokenizer(Unigram()       )

								lowerCamelCase__         =  normalizers.Sequence(
								    [
								        normalizers.Nmt(),
								        normalizers.NFKC(),
								        normalizers.Replace(Regex(""" {2,}"""       )   ,""" """       ),
								        normalizers.Lowercase(),
								    ]       )
								lowerCamelCase__         =  pre_tokenizers.Sequence(
								    [
								        pre_tokenizers.Metaspace(replacement=_lowerCAmelCase   ,add_prefix_space=_lowerCAmelCase       ),
								        pre_tokenizers.Digits(individual_digits=_lowerCAmelCase       ),
								        pre_tokenizers.Punctuation(),
								    ]       )
								lowerCamelCase__         =  decoders.Metaspace(replacement=_lowerCAmelCase   ,add_prefix_space=_lowerCAmelCase       )

								lowerCamelCase__         =  TemplateProcessing(
								    single=F'''$A {self.special_tokens["eos"]["token"]}'''   ,special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])]   ,)

								lowerCamelCase__         =  {
								    """model""": """SentencePieceUnigram""",
								    """replacement""": replacement,
								    """add_prefix_space""": add_prefix_space,
								}

								super().__init__(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = 80_00   ,_lowerCAmelCase = True   ,):
								lowerCamelCase__         =  trainers.UnigramTrainer(
								    vocab_size=_lowerCAmelCase   ,special_tokens=self.special_tokens_list   ,show_progress=_lowerCAmelCase   ,)

								if isinstance(_lowerCAmelCase   ,_lowerCAmelCase       ):
														lowerCamelCase__         =  [files]
								self._tokenizer.train(_lowerCAmelCase   ,trainer=_lowerCAmelCase       )

								self.add_unk_id()






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = 80_00   ,_lowerCAmelCase = True   ,):
								lowerCamelCase__         =  trainers.UnigramTrainer(
								    vocab_size=_lowerCAmelCase   ,special_tokens=self.special_tokens_list   ,show_progress=_lowerCAmelCase   ,)

								self._tokenizer.train_from_iterator(_lowerCAmelCase   ,trainer=_lowerCAmelCase       )

								self.add_unk_id()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  json.loads(self._tokenizer.to_str()       )

								lowerCamelCase__         =  self.special_tokens["""unk"""]["""id"""]

								lowerCamelCase__         =  Tokenizer.from_str(json.dumps(_lowerCAmelCase       )       )

