



from typing import Dict, List, Optional

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


_snake_case	=						logging.get_logger(__name__)


_snake_case	=						{
    "nielsr/canine-s": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_snake_case	=						111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_snake_case	=						0

_snake_case	=						0Xe_0_0_0
_snake_case	=						0Xe_0_0_1
_snake_case	=						0Xe_0_0_2
_snake_case	=						0Xe_0_0_3
_snake_case	=						0Xe_0_0_4

# Maps special codepoints to human-readable names.
_snake_case	=						{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_snake_case	=						{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







							def __init__(     self,    __a=chr(__a),    __a=chr(__a),    __a=chr(__a),    __a=chr(__a),    __a=chr(__a),    __a=chr(__a),    __a=False,    __a=2048,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					AddedToken(__a,    lstrip=__a,    rstrip=__a) if isinstance(__a,    __a) else bos_token
														_lowerCAmelCase  :							List[Any]				     =					AddedToken(__a,    lstrip=__a,    rstrip=__a) if isinstance(__a,    __a) else eos_token
														_lowerCAmelCase  :							Optional[Any]				     =					AddedToken(__a,    lstrip=__a,    rstrip=__a) if isinstance(__a,    __a) else sep_token
														_lowerCAmelCase  :							Tuple				     =					AddedToken(__a,    lstrip=__a,    rstrip=__a) if isinstance(__a,    __a) else cls_token
														_lowerCAmelCase  :							str				     =					AddedToken(__a,    lstrip=__a,    rstrip=__a) if isinstance(__a,    __a) else pad_token

														# Mask token behave like a normal word, i.e. include the space before it
														_lowerCAmelCase  :							Tuple				     =					AddedToken(__a,    lstrip=__a,    rstrip=__a) if isinstance(__a,    __a) else mask_token

														super().__init__(
														    bos_token=__a,    eos_token=__a,    sep_token=__a,    cls_token=__a,    pad_token=__a,    mask_token=__a,    add_prefix_space=__a,    model_max_length=__a,    **__a,    )

														# Creates a mapping for looking up the IDs of special symbols.
														_lowerCAmelCase  :							Dict[str, int]				     =					{}
														for codepoint, name in SPECIAL_CODEPOINTS.items():
																					_lowerCAmelCase  :							Dict				     =					codepoint

														# Creates a mapping for looking up the string forms of special symbol IDs.
														_lowerCAmelCase  :							Dict[int, str]				     =					{
														    codepoint: name for name, codepoint in self._special_codepoints.items()
														}

														_lowerCAmelCase  :							Optional[int]				     =					UNICODE_VOCAB_SIZE
														_lowerCAmelCase  :							Any				     =					len(self._special_codepoints)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return self._unicode_vocab_size







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return list(__a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														try:
																					return ord(__a)
														except TypeError:
																					raise ValueError(f"invalid token: '{token}'")







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														try:
																					if index in SPECIAL_CODEPOINTS:
																												return SPECIAL_CODEPOINTS[index]
																					return chr(__a)
														except TypeError:
																					raise ValueError(f"invalid id: {index}")







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														return "".join(__a)







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

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					[self.sep_token_id]
														_lowerCAmelCase  :							Optional[Any]				     =					[self.cls_token_id]

														_lowerCAmelCase  :							Optional[Any]				     =					cls + token_ids_a + sep
														if token_ids_a is not None:
																					result += token_ids_a + sep
														return result







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

														'''simple docstring'''

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

														_lowerCAmelCase  :							Union[str, Any]				     =					[1] + ([0] * len(__a)) + [1]
														if token_ids_a is not None:
																					result += ([0] * len(__a)) + [1]
														return result







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

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					[self.sep_token_id]
														_lowerCAmelCase  :							int				     =					[self.cls_token_id]

														_lowerCAmelCase  :							Any				     =					len(cls + token_ids_a + sep) * [0]
														if token_ids_a is not None:
																					result += len(token_ids_a + sep) * [1]
														return result







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

														'''simple docstring'''

														return ()





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
