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     :  Optional[int]					     =       logging.get_logger(__name__)

A     :  Optional[Any]					     =       {
    "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					:
  """simple docstring"""







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







  def __call__(							self :       str  ,     **__magic_name__ :       str   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  {k: np.array(__magic_name__   ) for k, v in kwargs.items()}
        return self.model.run(__magic_name__  ,     __magic_name__   )







  @staticmethod
  def    __A     (							__magic_name__ :       Union[str, Path]  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       List[str]=None   )  ->	Optional[Any]:
        if provider is None:
              logger.info("No onnxruntime provider specified, using CPUExecutionProvider"   )
              SCREAMING_SNAKE_CASE_          =  "CPUExecutionProvider"

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







  def    __A     (							self :       Dict  ,     __magic_name__ :       Union[str, Path]  ,     __magic_name__ :       Optional[str] = None  ,     **__magic_name__ :       int   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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







  def    __A     (							self :       str  ,     __magic_name__ :       Union[str, os.PathLike]  ,     **__magic_name__ :       int  ,     )  ->	Any:
        if os.path.isfile(__magic_name__   ):
              logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''   )
              return

        os.makedirs(__magic_name__  ,     exist_ok=__magic_name__   )

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







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Union[str, Path]  ,     __magic_name__ :       Optional[Union[bool, str, None]] = None  ,     __magic_name__ :       Optional[Union[str, None]] = None  ,     __magic_name__ :       bool = False  ,     __magic_name__ :       Optional[str] = None  ,     __magic_name__ :       Optional[str] = None  ,     __magic_name__ :       Optional[str] = None  ,     __magic_name__ :       Optional["ort.SessionOptions"] = None  ,     **__magic_name__ :       Any  ,     )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
        # load model from local directory
        if os.path.isdir(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  OnnxRuntimeModel.load_model(
                  os.path.join(__magic_name__  ,     __magic_name__   )  ,     provider=__magic_name__  ,     sess_options=__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  Path(__magic_name__   )
        # load model from hub
        else:
              # download model
              SCREAMING_SNAKE_CASE_          =  hf_hub_download(
                  repo_id=__magic_name__  ,     filename=__magic_name__  ,     use_auth_token=__magic_name__  ,     revision=__magic_name__  ,     cache_dir=__magic_name__  ,     force_download=__magic_name__  ,     )
              SCREAMING_SNAKE_CASE_          =  Path(__magic_name__   ).parent
              SCREAMING_SNAKE_CASE_          =  Path(__magic_name__   ).name
              SCREAMING_SNAKE_CASE_          =  OnnxRuntimeModel.load_model(__magic_name__  ,     provider=__magic_name__  ,     sess_options=__magic_name__   )
        return cls(model=__magic_name__  ,     **__magic_name__   )







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Union[str, Path]  ,     __magic_name__ :       bool = True  ,     __magic_name__ :       Optional[str] = None  ,     __magic_name__ :       Optional[str] = None  ,     **__magic_name__ :       List[str]  ,     )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  None
        if len(str(__magic_name__   ).split("@"   )   ) == 2:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  model_id.split("@"   )

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



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


