
"""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


lowerCamelCase__   					=		logging.get_logger(__name__)

lowerCamelCase__   					=		{
    """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  A__       :

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		**_SCREAMING_SNAKE_CASE ):
			logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.' )
			__lowerCAmelCase			:						Optional[int]									=      model
			__lowerCAmelCase			:						List[str]									=      kwargs.get('model_save_dir'       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						str									=      kwargs.get('latest_model_name'       ,		_SCREAMING_SNAKE_CASE )

		def __call__(      self       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						int									=      {k: np.array(_SCREAMING_SNAKE_CASE ) for k, v in kwargs.items()}
			return self.model.run(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

		@staticmethod
		def 							__lowerCamelCase							(      _SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None ):
			if provider is None:
				logger.info('No onnxruntime provider specified, using CPUExecutionProvider' )
				__lowerCAmelCase			:						Any									=      'CPUExecutionProvider'

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

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None       ,		**_SCREAMING_SNAKE_CASE ):
			__lowerCAmelCase			:						Union[str, Any]									=      file_name if file_name is not None else ONNX_WEIGHTS_NAME

			__lowerCAmelCase			:						Any									=      self.model_save_dir.joinpath(self.latest_model_name )
			__lowerCAmelCase			:						Union[str, Any]									=      Path(_SCREAMING_SNAKE_CASE ).joinpath(_SCREAMING_SNAKE_CASE )
			try:
				shutil.copyfile(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			except shutil.SameFileError:
				pass

			# copy external weights (for models >2GB)
			__lowerCAmelCase			:						int									=      self.model_save_dir.joinpath(_SCREAMING_SNAKE_CASE )
			if src_path.exists():
				__lowerCAmelCase			:						Tuple									=      Path(_SCREAMING_SNAKE_CASE ).joinpath(_SCREAMING_SNAKE_CASE )
				try:
					shutil.copyfile(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
				except shutil.SameFileError:
					pass

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE       ,		):
			if os.path.isfile(_SCREAMING_SNAKE_CASE ):
				logger.error(f"Provided path ({save_directory}) should be a directory, not a file" )
				return

			os.makedirs(_SCREAMING_SNAKE_CASE       ,		exist_ok=_SCREAMING_SNAKE_CASE )

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

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







		@classmethod
		def 							__lowerCamelCase							(      cls       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		**_SCREAMING_SNAKE_CASE       ,		):
			__lowerCAmelCase			:						int									=      None
			if len(str(_SCREAMING_SNAKE_CASE ).split('@' ) ) == 2:
				__lowerCAmelCase			,   __lowerCAmelCase			:						Any									=      model_id.split('@' )

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







"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{
    '''vocab_file''': {
        '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''unc-nlp/lxmert-base-uncased''': (
            '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

_SCREAMING_SNAKE_CASE					:				Any					    =						{
    '''unc-nlp/lxmert-base-uncased''': 512,
}

_SCREAMING_SNAKE_CASE					:				int					    =						{
    '''unc-nlp/lxmert-base-uncased''': {'''do_lower_case''': True},
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     int						= VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:     Any						= PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE						:     Any						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= LxmertTokenizer






    def __init__(       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							str=True       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="[UNK]"       ,						__SCREAMING_SNAKE_CASE     :							int="[SEP]"       ,						__SCREAMING_SNAKE_CASE     :							int="[PAD]"       ,						__SCREAMING_SNAKE_CASE     :							List[Any]="[CLS]"       ,						__SCREAMING_SNAKE_CASE     :							Any="[MASK]"       ,						__SCREAMING_SNAKE_CASE     :							List[str]=True       ,						__SCREAMING_SNAKE_CASE     :							List[str]=None       ,						**__SCREAMING_SNAKE_CASE     :							Any       ,						)     ->		Any:
      super().__init__(
          __SCREAMING_SNAKE_CASE       ,						tokenizer_file=__SCREAMING_SNAKE_CASE       ,						do_lower_case=__SCREAMING_SNAKE_CASE       ,						unk_token=__SCREAMING_SNAKE_CASE       ,						sep_token=__SCREAMING_SNAKE_CASE       ,						pad_token=__SCREAMING_SNAKE_CASE       ,						cls_token=__SCREAMING_SNAKE_CASE       ,						mask_token=__SCREAMING_SNAKE_CASE       ,						tokenize_chinese_chars=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      lowerCamelCase_         =						json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
      if (
          normalizer_state.get('lowercase'       ,						__SCREAMING_SNAKE_CASE					) != do_lower_case
          or normalizer_state.get('strip_accents'       ,						__SCREAMING_SNAKE_CASE					) != strip_accents
          or normalizer_state.get('handle_chinese_chars'       ,						__SCREAMING_SNAKE_CASE					) != tokenize_chinese_chars
      ):
        lowerCamelCase_         =						getattr(__SCREAMING_SNAKE_CASE       ,						normalizer_state.pop('type'					)					)
        lowerCamelCase_         =						do_lower_case
        lowerCamelCase_         =						strip_accents
        lowerCamelCase_         =						tokenize_chinese_chars
        lowerCamelCase_         =						normalizer_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						do_lower_case






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Tuple=None					)     ->		Dict:
      lowerCamelCase_         =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

      return output






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							List[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[List[int]] = None					)     ->		List[int]:
      lowerCamelCase_         =						[self.sep_token_id]
      lowerCamelCase_         =						[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    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None					)     ->		Tuple[str]:
      lowerCamelCase_         =						self._tokenizer.model.save(__SCREAMING_SNAKE_CASE       ,						name=__SCREAMING_SNAKE_CASE					)
      return tuple(__SCREAMING_SNAKE_CASE					)




