
"""simple docstring"""




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

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


if is_sentencepiece_available():
   from .tokenization_ta import TaTokenizer
else:
   _A			:       Tuple   		=       None


_A			:       Any   		=       logging.get_logger(__name__)

_A			:       Any   		=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A			:       Any   		=       {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_A			:       List[str]   		=       {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """attention_mask"""]
   __lowerCAmelCase            =				TaTokenizer

   __lowerCAmelCase            =				[]
   def __init__(			self	,    _a=None	,    _a=None	,    _a="</s>"	,    _a="<unk>"	,    _a="<pad>"	,    _a=100	,    _a=None	,    **_a	,    ):
          # Add extra_ids to the special token list
          if extra_ids > 0 and additional_special_tokens is None:
                 lowercase    :				List[str]       		=							[f"""<extra_id_{i}>""" for i in range(_a				)]
          elif extra_ids > 0 and additional_special_tokens is not None:
                 # Check that we have the right number of extra special tokens
                 lowercase    :				List[Any]       		=							len(set(filter(lambda _a				: bool("extra_id_" in str(_a				)				)	,    _a				)				)				)
                 if extra_tokens != extra_ids:
                        raise ValueError(
                            f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                            " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                            " tokens"				)

          super().__init__(
              _a	,    tokenizer_file=_a	,    eos_token=_a	,    unk_token=_a	,    pad_token=_a	,    extra_ids=_a	,    additional_special_tokens=_a	,    **_a	,    )

          lowercase    :				Any       		=							vocab_file
          lowercase    :				int       		=							False if not self.vocab_file else True
          lowercase    :				List[str]       		=							extra_ids
   @staticmethod
   def 			__magic_name__			(			_a	,    _a	,    _a				):
          if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                 lowercase    :				Optional[int]       		=							TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                 if init_max_model_length is not None and init_max_model_length != max_model_length:
                        return init_max_model_length
                 elif init_max_model_length is None:
                        warnings.warn(
                            "This tokenizer was incorrectly instantiated with a model max length of"
                            f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                            " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                            " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                            f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                            f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                            f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                            " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                            " instantiate this tokenizer with `model_max_length` set to your preferred value."	,    _a	,    )

          return max_model_length
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          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(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				Optional[Any]       		=							os.path.join(
              _a	,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

          if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				):
                 copyfile(self.vocab_file	,    _a				)
                 logger.info(f"""Copy vocab file to {out_vocab_file}"""				)

          return (out_vocab_file,)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Dict       		=							token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a
          else:
                 lowercase    :				int       		=							token_ids_a + [self.eos_token_id]
                 return self.prefix_tokens + token_ids_a + token_ids_a
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Tuple       		=							[self.eos_token_id]

          if token_ids_a is None:
                 return len(token_ids_a + eos				) * [0]
          return len(token_ids_a + eos + token_ids_a + eos				) * [0]
   def 			__magic_name__			(			self				):
          return list(
              set(filter(lambda _a				: bool(re.search(R"<extra_id_\d+>"	,    _a				)				) is not None	,    self.additional_special_tokens				)				)				)

   def 			__magic_name__			(			self				):
          return [self.convert_tokens_to_ids(_a				) for token in self.get_sentinel_tokens()]




"""simple docstring"""




import importlib

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


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


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


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


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


def   __magic_name__	(      __snake_case				:       str  ) ->				List[str]:
       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__	(      __snake_case				:       Any     , __snake_case				:       int     , __snake_case				:       List[Any]=True     , __snake_case				:       Dict=True  ) ->				str:
       lowercase    :				Optional[int]       		=							instantiate_from_config(__snake_case  )
       if sd is not None:
              model.load_state_dict(__snake_case  )
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any     , __snake_case				:       Optional[int]     , __snake_case				:       List[str]  ) ->				Any:
       # load the specified checkpoint
       if ckpt:
              lowercase    :				Dict       		=							torch.load(__snake_case     , map_location="cpu"  )
              lowercase    :				List[Any]       		=							pl_sd["global_step"]
              print(f"""loaded model from global step {global_step}."""  )
       else:
              lowercase    :				int       		=							{"state_dict": None}
              lowercase    :				Optional[Any]       		=							None
       lowercase    :				List[Any]       		=							load_model_from_config(config.model     , pl_sd["state_dict"]     , gpu=__snake_case     , eval_mode=__snake_case  )["model"]
       return model, global_step


