


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


SCREAMING_SNAKE_CASE       :		Any         =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :		Optional[int]         =				{
    "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   ,							UpperCamelCase_=None   ,							**UpperCamelCase_		):
								logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''		)
								lowercase_       :List[Any]     =       model
								lowercase_       :Any     =       kwargs.get('''model_save_dir'''   ,							UpperCamelCase_		)
								lowercase_       :List[str]     =       kwargs.get('''latest_model_name'''   ,							UpperCamelCase_		)
			def __call__(     self   ,							**UpperCamelCase_		):
								lowercase_       :Dict     =       {k: np.array(UpperCamelCase_		) for k, v in kwargs.items()}
								return self.model.run(UpperCamelCase_   ,							UpperCamelCase_		)
			@staticmethod
			def 		UpperCamelCase  (     UpperCamelCase_   ,							UpperCamelCase_=None   ,							UpperCamelCase_=None		):
								if provider is None:
													logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''		)
													lowercase_       :List[Any]     =       '''CPUExecutionProvider'''

								return ort.InferenceSession(UpperCamelCase_   ,							providers=[provider]   ,							sess_options=UpperCamelCase_		)
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_ = None   ,							**UpperCamelCase_		):
								lowercase_       :Optional[Any]     =       file_name if file_name is not None else ONNX_WEIGHTS_NAME

								lowercase_       :List[str]     =       self.model_save_dir.joinpath(self.latest_model_name		)
								lowercase_       :Tuple     =       Path(UpperCamelCase_		).joinpath(UpperCamelCase_		)
								try:
													shutil.copyfile(UpperCamelCase_   ,							UpperCamelCase_		)
								except shutil.SameFileError:
													pass

								# copy external weights (for models >2GB)
								lowercase_       :List[str]     =       self.model_save_dir.joinpath(UpperCamelCase_		)
								if src_path.exists():
													lowercase_       :Any     =       Path(UpperCamelCase_		).joinpath(UpperCamelCase_		)
													try:
																		shutil.copyfile(UpperCamelCase_   ,							UpperCamelCase_		)
													except shutil.SameFileError:
																		pass
			def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							**UpperCamelCase_   ,							):
								if os.path.isfile(UpperCamelCase_		):
													logger.error(f"Provided path ({save_directory}) should be a directory, not a file"		)
													return

								os.makedirs(UpperCamelCase_   ,							exist_ok=UpperCamelCase_		)

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




			@classmethod
			def 		UpperCamelCase  (     cls   ,							UpperCamelCase_   ,							UpperCamelCase_ = True   ,							UpperCamelCase_ = None   ,							UpperCamelCase_ = None   ,							**UpperCamelCase_   ,							):
								lowercase_       :List[str]     =       None
								if len(str(UpperCamelCase_		).split('''@'''		)		) == 2:
													lowercase_       :Any     =       model_id.split('''@'''		)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE       :		str         =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Dict         =				[
       "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TapasForMaskedLM",
       "TapasForQuestionAnswering",
       "TapasForSequenceClassification",
       "TapasModel",
       "TapasPreTrainedModel",
       "load_tf_weights_in_tapas",
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFTapasForMaskedLM",
       "TFTapasForQuestionAnswering",
       "TFTapasForSequenceClassification",
       "TFTapasModel",
       "TFTapasPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

