



"""simple docstring"""

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {"tokenizer_file": "tokenizer.json"}

lowercase__			   =  {
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
      _lowerCAmelCase							=					VOCAB_FILES_NAMES
      _lowerCAmelCase							=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCAmelCase							=					["input_ids", "attention_mask"]
      _lowerCAmelCase							=					None



      def __init__(self						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase="<unk>"						,     _lowercase="<s>"						,     _lowercase="</s>"						,     _lowercase="<pad>"						,     _lowercase=False						,     _lowercase=False						,     **_lowercase						,     ):





             '''simple docstring'''

             super().__init__(
                 _lowercase						,     _lowercase						,     tokenizer_file=_lowercase						,     unk_token=_lowercase						,     bos_token=_lowercase						,     eos_token=_lowercase						,     pad_token=_lowercase						,     add_prefix_space=_lowercase						,     clean_up_tokenization_spaces=_lowercase						,     **_lowercase						,     )
             __a		:		Optional[Any]									=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
             if pre_tok_state.get("""add_prefix_space"""						,     _lowercase							) != add_prefix_space:
                    __a		:		str									=       getattr(_lowercase						,     pre_tok_state.pop("""type"""							)							)
                    __a		:		Optional[int]									=       add_prefix_space
                    __a		:		Any									=       pre_tok_class(**_lowercase							)

             __a		:		List[Any]									=       add_prefix_space



      def     lowerCAmelCase__(self						,     *_lowercase						,     **_lowercase							):





             '''simple docstring'''

             __a		:		Dict									=       kwargs.get("""is_split_into_words"""						,     _lowercase							)
             if not (self.add_prefix_space or not is_split_into_words):
                    raise Exception(
                        F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
                        """ pretokenized inputs."""							)

             return super()._batch_encode_plus(*_lowercase						,     **_lowercase							)



      def     lowerCAmelCase__(self						,     *_lowercase						,     **_lowercase							):





             '''simple docstring'''

             __a		:		str									=       kwargs.get("""is_split_into_words"""						,     _lowercase							)

             if not (self.add_prefix_space or not is_split_into_words):
                    raise Exception(
                        F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
                        """ pretokenized inputs."""							)

             return super()._encode_plus(*_lowercase						,     **_lowercase							)



      def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase = None							):





             '''simple docstring'''

             __a		:		int									=       self._tokenizer.model.save(_lowercase						,     name=_lowercase							)
             return tuple(_lowercase							)





      def     lowerCAmelCase__(self						,     _lowercase							):





             '''simple docstring'''

             __a		:		Tuple									=       []
             for is_user, text in conversation.iter_texts():
                    input_ids.extend(self.encode(_lowercase						,     add_special_tokens=_lowercase							) + [self.eos_token_id]							)

             if len(_lowercase							) > self.model_max_length:
                    __a		:		int									=       input_ids[-self.model_max_length :]
             return input_ids






"""simple docstring"""

import importlib

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





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





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





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





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





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       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       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

