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:
	_UpperCAmelCase            =		None

_UpperCAmelCase            =		logging.get_logger(__name__)
_UpperCAmelCase            =		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase            =		{
    """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""",
    },
}

_UpperCAmelCase            =		{
    """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,
}

_UpperCAmelCase            =		"""▁"""





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = VOCAB_FILES_NAMES
				lowerCamelCase_    = PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_    = AlbertTokenizer
				def __init__(						self		,     lowercase=None		,     lowercase=None		,     lowercase=True		,     lowercase=True		,     lowercase=False		,     lowercase="[CLS]"		,     lowercase="[SEP]"		,     lowercase="<unk>"		,     lowercase="[SEP]"		,     lowercase="<pad>"		,     lowercase="[CLS]"		,     lowercase="[MASK]"		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				(
						    AddedToken(lowercase		,     lstrip=lowercase		,     rstrip=lowercase		,     normalized=lowercase )
						    if isinstance(lowercase		,     lowercase )
						    else mask_token
						)

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

						A_		:    str         =				do_lower_case
						A_		:    str         =				remove_space
						A_		:    Optional[int]         =				keep_accents
						A_		:    int         =				vocab_file
						A_		:    str         =				False if not self.vocab_file else True
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				[self.sep_token_id]
						A_		:    Union[str, Any]         =				[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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						A_		:    int         =				[self.sep_token_id]
						A_		:    List[Any]         =				[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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None ):



						"""simple docstring"""





						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
						A_		:    Dict         =				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,)







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


_UpperCAmelCase            =		logging.get_logger(__name__)

_UpperCAmelCase            =		{
    """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						:


				'''simple docstring'''






				def __init__(						self		,     lowercase=None		,     **lowercase ):



						"""simple docstring"""





						logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
						A_		:    List[Any]         =				model
						A_		:    Dict         =				kwargs.get('model_save_dir'		,     lowercase )
						A_		:    List[str]         =				kwargs.get('latest_model_name'		,     lowercase )
				def __call__(						self		,     **lowercase ):



						"""simple docstring"""





						A_		:    str         =				{k: np.array(lowercase ) for k, v in kwargs.items()}
						return self.model.run(lowercase		,     lowercase )
				@staticmethod
				def   lowerCAmelCase_       (						lowercase		,     lowercase=None		,     lowercase=None ):



						"""simple docstring"""





						if provider is None:
								logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
								A_		:    List[Any]         =				'CPUExecutionProvider'

						return ort.InferenceSession(lowercase		,     providers=[provider]		,     sess_options=lowercase )
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase = None		,     **lowercase ):



						"""simple docstring"""





						A_		:    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_		:    int         =				Path(lowercase ).joinpath(lowercase )
						try:
								shutil.copyfile(lowercase		,     lowercase )
						except shutil.SameFileError:
								pass

						# copy external weights (for models >2GB)
						A_		:    Optional[Any]         =				self.model_save_dir.joinpath(lowercase )
						if src_path.exists():
								A_		:    int         =				Path(lowercase ).joinpath(lowercase )
								try:
										shutil.copyfile(lowercase		,     lowercase )
								except shutil.SameFileError:
										pass
				def   lowerCAmelCase_       (						self		,     lowercase		,     **lowercase		,     ):



						"""simple docstring"""





						if os.path.isfile(lowercase ):
								logger.error(F'''Provided path ({save_directory}) should be a directory, not a file''' )
								return

						os.makedirs(lowercase		,     exist_ok=lowercase )

						# saving model weights/files
						self._save_pretrained(lowercase		,     **lowercase )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = None		,     lowercase = None		,     lowercase = False		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Any         =				file_name if file_name is not None else ONNX_WEIGHTS_NAME
						# load model from local directory
						if os.path.isdir(lowercase ):
								A_		:    Optional[int]         =				OnnxRuntimeModel.load_model(
								    os.path.join(lowercase		,     lowercase )		,     provider=lowercase		,     sess_options=lowercase )
								A_		:    Dict         =				Path(lowercase )
						# load model from hub
						else:
								# download model
								A_		:    List[str]         =				hf_hub_download(
								    repo_id=lowercase		,     filename=lowercase		,     use_auth_token=lowercase		,     revision=lowercase		,     cache_dir=lowercase		,     force_download=lowercase		,     )
								A_		:    int         =				Path(lowercase ).parent
								A_		:    Optional[Any]         =				Path(lowercase ).name
								A_		:    Any         =				OnnxRuntimeModel.load_model(lowercase		,     provider=lowercase		,     sess_options=lowercase )
						return cls(model=lowercase		,     **lowercase )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     lowercase		,     lowercase = True		,     lowercase = None		,     lowercase = None		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    List[Any]         =				None
						if len(str(lowercase ).split('@' ) ) == 2:
								A_       ,      A_		:    int         =				model_id.split('@' )

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






