

"""simple docstring"""




import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


a		:				Any  			=    logging.get_logger(__name__)

a		:				Tuple  			=    {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__=None   ,      **lowerCAmelCase__   )			->				str:
								logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."   )
								a   :	Optional[int]			=       model
								a   :	int			=       kwargs.get("model_save_dir"   ,      lowerCAmelCase__   )
								a   :	Tuple			=       kwargs.get("latest_model_name"   ,      lowerCAmelCase__   )






							def __call__(     self   ,      **lowerCAmelCase__   )			->				Dict:
								a   :	List[str]			=       {k: np.array(lowerCAmelCase__   ) for k, v in kwargs.items()}
								return self.model.run(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   )			->				Union[str, Any]:
								if provider is None:
									logger.info("No onnxruntime provider specified, using CPUExecutionProvider"   )
									a   :	List[str]			=       "CPUExecutionProvider"

								return ort.InferenceSession(lowerCAmelCase__   ,      providers=[provider]   ,      sess_options=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   )			->				int:
								a   :	List[str]			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME

								a   :	Optional[int]			=       self.model_save_dir.joinpath(self.latest_model_name   )
								a   :	List[str]			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
								try:
									shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
								except shutil.SameFileError:
									pass

								# copy external weights (for models >2GB)
								a   :	str			=       self.model_save_dir.joinpath(lowerCAmelCase__   )
								if src_path.exists():
									a   :	Any			=       Path(lowerCAmelCase__   ).joinpath(lowerCAmelCase__   )
									try:
										shutil.copyfile(lowerCAmelCase__   ,      lowerCAmelCase__   )
									except shutil.SameFileError:
										pass






							def 					__a     (     self   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )			->				str:
								if os.path.isfile(lowerCAmelCase__   ):
									logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""   )
									return

								os.makedirs(lowerCAmelCase__   ,      exist_ok=lowerCAmelCase__   )

								# saving model weights/files
								self._save_pretrained(lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = False   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				Optional[int]:
								a   :	Tuple			=       file_name if file_name is not None else ONNX_WEIGHTS_NAME
								# load model from local directory
								if os.path.isdir(lowerCAmelCase__   ):
									a   :	Tuple			=       OnnxRuntimeModel.load_model(
									    os.path.join(lowerCAmelCase__   ,      lowerCAmelCase__   )   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
									a   :	Tuple			=       Path(lowerCAmelCase__   )
								# load model from hub
								else:
									# download model
									a   :	Optional[Any]			=       hf_hub_download(
									    repo_id=lowerCAmelCase__   ,      filename=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      )
									a   :	Optional[int]			=       Path(lowerCAmelCase__   ).parent
									a   :	List[Any]			=       Path(lowerCAmelCase__   ).name
									a   :	int			=       OnnxRuntimeModel.load_model(lowerCAmelCase__   ,      provider=lowerCAmelCase__   ,      sess_options=lowerCAmelCase__   )
								return cls(model=lowerCAmelCase__   ,      **lowerCAmelCase__   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      lowerCAmelCase__ = None   ,      lowerCAmelCase__ = None   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								a   :	Any			=       None
								if len(str(lowerCAmelCase__   ).split("@"   )   ) == 2:
									a   :	Tuple			=       model_id.split("@"   )

								return cls._from_pretrained(
								    model_id=lowerCAmelCase__   ,      revision=lowerCAmelCase__   ,      cache_dir=lowerCAmelCase__   ,      force_download=lowerCAmelCase__   ,      use_auth_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )









"""simple docstring"""




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_albert import AlbertTokenizer
else:
		a		:				Tuple  			=    None

a		:				Any  			=    logging.get_logger(__name__)
a		:				List[Any]  			=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				str  			=    {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

a		:				str  			=    {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

a		:				Union[str, Any]  			=    '''▁'''


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Union[str, Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      List[Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      List[Any]         =AlbertTokenizer






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[MASK]"   ,      **lowerCAmelCase__   ,      )			->				Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								a   :	Optional[int]			=       (
								    AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ,      normalized=lowerCAmelCase__   )
								    if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								    else mask_token
								)

								super().__init__(
								    lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      do_lower_case=lowerCAmelCase__   ,      remove_space=lowerCAmelCase__   ,      keep_accents=lowerCAmelCase__   ,      bos_token=lowerCAmelCase__   ,      eos_token=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	Dict			=       do_lower_case
								a   :	Any			=       remove_space
								a   :	Optional[Any]			=       keep_accents
								a   :	List[str]			=       vocab_file
								a   :	Optional[Any]			=       False if not self.vocab_file else True






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	int			=       [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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = 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(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
									return
								a   :	Dict			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

								return (out_vocab_file,)


