



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_snake_case	=						{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_snake_case	=						{"mobilebert-uncased": 512}


_snake_case	=						{}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	VOCAB_FILES_NAMES
							lowerCamelCase__         =	PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__         =	PRETRAINED_INIT_CONFIGURATION
							lowerCamelCase__         =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase__         =	MobileBertTokenizer







							def __init__(     self,    __a=None,    __a=None,    __a=True,    __a="[UNK]",    __a="[SEP]",    __a="[PAD]",    __a="[CLS]",    __a="[MASK]",    __a=True,    __a=None,    **__a,    ):

														'''simple docstring'''

														super().__init__(
														    UpperCAmelCase__,    tokenizer_file=UpperCAmelCase__,    do_lower_case=UpperCAmelCase__,    unk_token=UpperCAmelCase__,    sep_token=UpperCAmelCase__,    pad_token=UpperCAmelCase__,    cls_token=UpperCAmelCase__,    mask_token=UpperCAmelCase__,    tokenize_chinese_chars=UpperCAmelCase__,    strip_accents=UpperCAmelCase__,    **UpperCAmelCase__,    )

														_lowerCAmelCase  :							Union[str, Any]				     =					json.loads(self.backend_tokenizer.normalizer.__getstate__())
														if (
														    normalizer_state.get("lowercase",    UpperCAmelCase__) != do_lower_case
														    or normalizer_state.get("strip_accents",    UpperCAmelCase__) != strip_accents
														    or normalizer_state.get("handle_chinese_chars",    UpperCAmelCase__) != tokenize_chinese_chars
														):
																					_lowerCAmelCase  :							Dict				     =					getattr(UpperCAmelCase__,    normalizer_state.pop("type"))
																					_lowerCAmelCase  :							Tuple				     =					do_lower_case
																					_lowerCAmelCase  :							List[Any]				     =					strip_accents
																					_lowerCAmelCase  :							str				     =					tokenize_chinese_chars
																					_lowerCAmelCase  :							int				     =					normalizer_class(**UpperCAmelCase__)

														_lowerCAmelCase  :							Any				     =					do_lower_case







							def      snake_case__   (     self,    __a,    __a=None):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					[self.sep_token_id]
														_lowerCAmelCase  :							List[str]				     =					[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) * [0] + len(token_ids_a + sep) * [1]







							def      snake_case__   (     self,    __a,    __a = None):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					self._tokenizer.model.save(UpperCAmelCase__,    name=UpperCAmelCase__)
														return tuple(UpperCAmelCase__)





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					OmegaConf.load(_lowerCamelCase						)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase						)						)						)
							return config






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=None       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							if conf_path is None:
														_lowerCAmelCase  :							Union[str, Any]				     =					"./model_checkpoints/vqgan_only.yaml"
							_lowerCAmelCase  :							Tuple				     =					load_config(_lowerCamelCase       ,				display=_lowerCamelCase						)
							_lowerCAmelCase  :							str				     =					VQModel(**config.model.params						)
							if ckpt_path is None:
														_lowerCAmelCase  :							Optional[int]				     =					"./model_checkpoints/vqgan_only.pt"
							_lowerCAmelCase  :							int				     =					torch.load(_lowerCamelCase       ,				map_location=_lowerCamelCase						)
							if ".ckpt" in ckpt_path:
														_lowerCAmelCase  :							List[Any]				     =					sd["state_dict"]
							model.load_state_dict(_lowerCamelCase       ,				strict=_lowerCamelCase						)
							model.to(_lowerCamelCase						)
							del sd
							return model






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Tuple				     =					model.encode(_lowerCamelCase						)
							print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"						)
							_lowerCAmelCase  :							int				     =					model.decode(_lowerCamelCase						)
							return xrec






def        A       (      _lowerCamelCase       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					string.rsplit("."       ,				1						)
							if reload:
														_lowerCAmelCase  :							Dict				     =					importlib.import_module(_lowerCamelCase						)
														importlib.reload(_lowerCamelCase						)
							return getattr(importlib.import_module(_lowerCamelCase       ,				package=_lowerCamelCase						)       ,				cls						)






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if "target" not in config:
														raise KeyError("Expected key `target` to instantiate."						)
							return get_obj_from_str(config["target"]						)(**config.get("params"       ,				{}						)						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=True       ,				_lowerCamelCase=True						):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					instantiate_from_config(_lowerCamelCase						)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase						)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if ckpt:
														_lowerCAmelCase  :							Optional[int]				     =					torch.load(_lowerCamelCase       ,				map_location="cpu"						)
														_lowerCAmelCase  :							int				     =					pl_sd["global_step"]
														print(F"loaded model from global step {global_step}."						)
							else:
														_lowerCAmelCase  :							Optional[int]				     =					{"state_dict": None}
														_lowerCAmelCase  :							Any				     =					None
							_lowerCAmelCase  :							Optional[int]				     =					load_model_from_config(config.model       ,				pl_sd["state_dict"]       ,				gpu=_lowerCamelCase       ,				eval_mode=_lowerCamelCase						)["model"]
							return model, global_step
