

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


lowercase_									=   logging.get_logger(__name__)

lowercase_									=   {
    '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  __lowerCAmelCase     :




  def __init__(				self							,   lowerCAmelCase=None							,   **lowerCAmelCase					)  -> Optional[int]:
        '''simple docstring'''






        logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'					)
        _lowercase	       =model
        _lowercase	       =kwargs.get('model_save_dir'							,   lowerCAmelCase					)
        _lowercase	       =kwargs.get('latest_model_name'							,   lowerCAmelCase					)




  def __call__(				self							,   **lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       ={k: np.array(lowerCAmelCase					) for k, v in kwargs.items()}
        return self.model.run(lowerCAmelCase							,   lowerCAmelCase					)




  @staticmethod
  def   A__     (				lowerCAmelCase							,   lowerCAmelCase=None							,   lowerCAmelCase=None					)  -> str:
        '''simple docstring'''






        if provider is None:
              logger.info('No onnxruntime provider specified, using CPUExecutionProvider'					)
              _lowercase	       ='CPUExecutionProvider'

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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =file_name if file_name is not None else ONNX_WEIGHTS_NAME

        _lowercase	       =self.model_save_dir.joinpath(self.latest_model_name					)
        _lowercase	       =Path(lowerCAmelCase					).joinpath(lowerCAmelCase					)
        try:
              shutil.copyfile(lowerCAmelCase							,   lowerCAmelCase					)
        except shutil.SameFileError:
              pass

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




  def   A__     (				self							,   lowerCAmelCase							,   **lowerCAmelCase							,   )  -> Optional[Any]:
        '''simple docstring'''






        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							,   )  -> Any:
        '''simple docstring'''






        _lowercase	       =file_name if file_name is not None else ONNX_WEIGHTS_NAME
        # load model from local directory
        if os.path.isdir(lowerCAmelCase					):
              _lowercase	       =OnnxRuntimeModel.load_model(
                  os.path.join(lowerCAmelCase							,   lowerCAmelCase					)							,   provider=lowerCAmelCase							,   sess_options=lowerCAmelCase					)
              _lowercase	       =Path(lowerCAmelCase					)
        # load model from hub
        else:
              # download model
              _lowercase	       =hf_hub_download(
                  repo_id=lowerCAmelCase							,   filename=lowerCAmelCase							,   use_auth_token=lowerCAmelCase							,   revision=lowerCAmelCase							,   cache_dir=lowerCAmelCase							,   force_download=lowerCAmelCase							,   )
              _lowercase	       =Path(lowerCAmelCase					).parent
              _lowercase	       =Path(lowerCAmelCase					).name
              _lowercase	       =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[Any]:
        '''simple docstring'''






        _lowercase	       =None
        if len(str(lowerCAmelCase					).split('@'					)					) == 2:
              _lowercase       ,       _lowercase	       =model_id.split('@'					)

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_									=   {'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['XLNetTokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['XLNetTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLNetForMultipleChoice',
      'XLNetForQuestionAnswering',
      'XLNetForQuestionAnsweringSimple',
      'XLNetForSequenceClassification',
      'XLNetForTokenClassification',
      'XLNetLMHeadModel',
      'XLNetModel',
      'XLNetPreTrainedModel',
      'load_tf_weights_in_xlnet',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLNetForMultipleChoice',
      'TFXLNetForQuestionAnsweringSimple',
      'TFXLNetForSequenceClassification',
      'TFXLNetForTokenClassification',
      'TFXLNetLMHeadModel',
      'TFXLNetMainLayer',
      'TFXLNetModel',
      'TFXLNetPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


