'''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


_a   :	Tuple 							=   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 		__A       :

       '''simple docstring'''





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






       def __call__(      self				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				List[Any]       				=		{k: np.array(UpperCamelCase_      ) for k, v in kwargs.items()}
              return self.model.run(UpperCamelCase_				,     UpperCamelCase_      )






       @staticmethod
       def 			_snake_case						(      UpperCamelCase_				,     UpperCamelCase_=None				,     UpperCamelCase_=None      ):
              if provider is None:
                     logger.info("No onnxruntime provider specified, using CPUExecutionProvider"      )
                     __UpperCAmelCase  :				Dict       				=		"CPUExecutionProvider"

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		file_name if file_name is not None else ONNX_WEIGHTS_NAME

              __UpperCAmelCase  :				Optional[int]       				=		self.model_save_dir.joinpath(self.latest_model_name      )
              __UpperCAmelCase  :				str       				=		Path(UpperCamelCase_      ).joinpath(UpperCamelCase_      )
              try:
                     shutil.copyfile(UpperCamelCase_				,     UpperCamelCase_      )
              except shutil.SameFileError:
                     pass

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






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







       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_ = True				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Dict       				=		None
              if len(str(UpperCamelCase_      ).split("@"      )      ) == 2:
                     __UpperCAmelCase  :				int       				=		model_id.split("@"      )

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







'''simple docstring'''




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       list[float]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase  :				str       				=		f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if colsa != 1:
              __UpperCAmelCase  :				Optional[Any]       				=		f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if rowsa != rowsa:
              __UpperCAmelCase  :				Optional[int]       				=		(
                  "Coefficient and constant matrices dimensions must be nxn and nx1 but "
                  f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if len(lowerCamelCase__						) != rowsa:
              __UpperCAmelCase  :				List[str]       				=		(
                  "Number of initial values must be equal to number of rows in coefficient "
                  f"""matrix but received {len(lowerCamelCase__						)} and {rowsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if iterations <= 0:
              raise ValueError("Iterations must be at least 1"						)

       __UpperCAmelCase  :				NDArray[floataa]       				=		np.concatenate(
           (coefficient_matrix, constant_matrix)							,    axis=1						)

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		table.shape

       strictly_diagonally_dominant(lowerCamelCase__						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				int       				=		[]
              for row in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				List[str]       				=		0
                     for col in range(lowerCamelCase__						):
                            if col == row:
                                   __UpperCAmelCase  :				int       				=		table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase  :				Any       				=		table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase  :				List[Any]       				=		(temp + val) / denom
                     new_val.append(lowerCamelCase__						)
              __UpperCAmelCase  :				str       				=		new_val

       return [float(lowerCamelCase__						) for i in new_val]

def _lowercase	(      lowerCamelCase__						)					->       bool:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		table.shape

       __UpperCAmelCase  :				str       				=		True

       for i in range(0							,    lowerCamelCase__						):
              __UpperCAmelCase  :				Union[str, Any]       				=		0
              for j in range(0							,    cols - 1						):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("Coefficient matrix is not strictly diagonally dominant"						)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






