

import importlib

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						OmegaConf.load(SCREAMING_SNAKE_CASE_			)
    if display:
        print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_			)			)			)
    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			,						SCREAMING_SNAKE_CASE_=None			)->						Optional[int]:



    """simple docstring"""






    if conf_path is None:
        UpperCamelCase_			  =						"./model_checkpoints/vqgan_only.yaml"
    UpperCamelCase_			  =						load_config(SCREAMING_SNAKE_CASE_			,						display=SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						VQModel(**config.model.params			)
    if ckpt_path is None:
        UpperCamelCase_			  =						"./model_checkpoints/vqgan_only.pt"
    UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			,						map_location=SCREAMING_SNAKE_CASE_			)
    if ".ckpt" in ckpt_path:
        UpperCamelCase_			  =						sd["state_dict"]
    model.load_state_dict(SCREAMING_SNAKE_CASE_			,						strict=SCREAMING_SNAKE_CASE_			)
    model.to(SCREAMING_SNAKE_CASE_			)
    del sd
    return model




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """simple docstring"""






    UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						model.encode(SCREAMING_SNAKE_CASE_			)
    print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"			)
    UpperCamelCase_			  =						model.decode(SCREAMING_SNAKE_CASE_			)
    return xrec




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						Optional[Any]:



    """simple docstring"""






    UpperCamelCase_  ,       UpperCamelCase_			  =						string.rsplit("."			,						1			)
    if reload:
        UpperCamelCase_			  =						importlib.import_module(SCREAMING_SNAKE_CASE_			)
        importlib.reload(SCREAMING_SNAKE_CASE_			)
    return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_			,						package=SCREAMING_SNAKE_CASE_			)			,						cls			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Any:



    """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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=True			,						SCREAMING_SNAKE_CASE_=True			)->						Optional[int]:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[str]:



    """simple docstring"""






    if ckpt:
        UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			,						map_location="cpu"			)
        UpperCamelCase_			  =						pl_sd["global_step"]
        print(f"loaded model from global step {global_step}."			)
    else:
        UpperCamelCase_			  =						{"state_dict": None}
        UpperCamelCase_			  =						None
    UpperCamelCase_			  =						load_model_from_config(config.model			,						pl_sd["state_dict"]			,						gpu=SCREAMING_SNAKE_CASE_			,						eval_mode=SCREAMING_SNAKE_CASE_			)["model"]
    return model, global_step



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     None

SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Optional[Any]						=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE		:int						=     {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE		:List[str]						=     {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE		:Optional[Any]						=     """▁"""






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      VOCAB_FILES_NAMES
     UpperCamelCase_	:Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_	:Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_	:Any										=      ["""input_ids""", """attention_mask"""]
     UpperCamelCase_	:List[Any]										=      BarthezTokenizer






     def __init__(				self		,						_lowercase=None		,						_lowercase=None		,						_lowercase="<s>"		,						_lowercase="</s>"		,						_lowercase="</s>"		,						_lowercase="<s>"		,						_lowercase="<unk>"		,						_lowercase="<pad>"		,						_lowercase="<mask>"		,						**_lowercase		,						)->     Optional[int]:
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase_			  =						AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase	) if isinstance(_lowercase		,						_lowercase	) else mask_token

         super().__init__(
             _lowercase		,						tokenizer_file=_lowercase		,						bos_token=_lowercase		,						eos_token=_lowercase		,						unk_token=_lowercase		,						sep_token=_lowercase		,						cls_token=_lowercase		,						pad_token=_lowercase		,						mask_token=_lowercase		,						**_lowercase		,						)

         UpperCamelCase_			  =						vocab_file
         UpperCamelCase_			  =						False if not self.vocab_file else True






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCamelCase_			  =						[self.cls_token_id]
         UpperCamelCase_			  =						[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:
         UpperCamelCase_			  =						[self.sep_token_id]
         UpperCamelCase_			  =						[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 + sep + token_ids_a + sep	) * [0]






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Tuple[str]:
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                 "tokenizer."	)

         if not os.path.isdir(_lowercase	):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
             return
         UpperCamelCase_			  =						os.path.join(
             _lowercase		,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(_lowercase	):
             copyfile(self.vocab_file		,						_lowercase	)

         return (out_vocab_file,)
