






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


__magic_name__   :  Tuple        =							logging.get_logger(__name__)

__magic_name__   :  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      :    Optional[int]   ,						_SCREAMING_SNAKE_CASE      :    Optional[int]=None   ,						**_SCREAMING_SNAKE_CASE      :    List[Any] ):






           """simple docstring"""




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







     def __call__( self      :    int   ,						**_SCREAMING_SNAKE_CASE      :    Optional[int] ):






           """simple docstring"""




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







     @staticmethod
     def   _SCREAMING_SNAKE_CASE     ( _SCREAMING_SNAKE_CASE      :    Union[str, Path]   ,						_SCREAMING_SNAKE_CASE      :    Dict=None   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=None ):






           """simple docstring"""




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

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Path]   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = None   ,						**_SCREAMING_SNAKE_CASE      :    str ):






           """simple docstring"""




           UpperCamelCase					       =  file_name if file_name is not None else ONNX_WEIGHTS_NAME

           UpperCamelCase					       =  self.model_save_dir.joinpath(self.latest_model_name )
           UpperCamelCase					       =  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)
           UpperCamelCase					       =  self.model_save_dir.joinpath(_SCREAMING_SNAKE_CASE )
           if src_path.exists():
                 UpperCamelCase					       =  Path(_SCREAMING_SNAKE_CASE ).joinpath(_SCREAMING_SNAKE_CASE )
                 try:
                       shutil.copyfile(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                 except shutil.SameFileError:
                       pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple   ,						_SCREAMING_SNAKE_CASE      :    Union[str, os.PathLike]   ,						**_SCREAMING_SNAKE_CASE      :    List[Any]   ,						):






           """simple docstring"""




           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   _SCREAMING_SNAKE_CASE     ( cls      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Path]   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[bool, str, None]] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[Union[str, None]] = None   ,						_SCREAMING_SNAKE_CASE      :    bool = False   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional["ort.SessionOptions"] = None   ,						**_SCREAMING_SNAKE_CASE      :    Dict   ,						):






           """simple docstring"""




           UpperCamelCase					       =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
           # load model from local directory
           if os.path.isdir(_SCREAMING_SNAKE_CASE ):
                 UpperCamelCase					       =  OnnxRuntimeModel.load_model(
                     os.path.join(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )   ,						provider=_SCREAMING_SNAKE_CASE   ,						sess_options=_SCREAMING_SNAKE_CASE )
                 UpperCamelCase					       =  Path(_SCREAMING_SNAKE_CASE )
           # load model from hub
           else:
                 # download model
                 UpperCamelCase					       =  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   ,						)
                 UpperCamelCase					       =  Path(_SCREAMING_SNAKE_CASE ).parent
                 UpperCamelCase					       =  Path(_SCREAMING_SNAKE_CASE ).name
                 UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( cls      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Path]   ,						_SCREAMING_SNAKE_CASE      :    bool = True   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = None   ,						_SCREAMING_SNAKE_CASE      :    Optional[str] = None   ,						**_SCREAMING_SNAKE_CASE      :    str   ,						):






           """simple docstring"""




           UpperCamelCase					       =  None
           if len(str(_SCREAMING_SNAKE_CASE ).split('@' ) ) == 2:
                 UpperCamelCase       ,  UpperCamelCase					       =  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   ,						)











from __future__ import annotations

import math
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			list:
      """simple docstring"""

      if len(_UpperCamelCase) != 2 or len(a[0]) != 2 or len(_UpperCamelCase) != 2 or len(b[0]) != 2:
            raise Exception('Matrices are not 2x2')
      UpperCamelCase					       =  [
          [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
          [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
      ]
      return new_matrix
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Union[str, Any]:
      """simple docstring"""

      return [
          [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]))]
          for row in range(len(_UpperCamelCase))
      ]
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      return [
          [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]))]
          for row in range(len(_UpperCamelCase))
      ]
def 		lowercase__  (				_UpperCamelCase) ->			tuple[list, list, list, list]:
      """simple docstring"""

      if len(_UpperCamelCase) % 2 != 0 or len(a[0]) % 2 != 0:
            raise Exception('Odd matrices are not supported!')

      UpperCamelCase					       =  len(_UpperCamelCase)
      UpperCamelCase					       =  matrix_length // 2

      UpperCamelCase					       =  [[a[i][j] for j in range(_UpperCamelCase       ,							_UpperCamelCase)] for i in range(_UpperCamelCase)]
      UpperCamelCase					       =  [
          [a[i][j] for j in range(_UpperCamelCase       ,							_UpperCamelCase)] for i in range(_UpperCamelCase       ,							_UpperCamelCase)
      ]

      UpperCamelCase					       =  [[a[i][j] for j in range(_UpperCamelCase)] for i in range(_UpperCamelCase)]
      UpperCamelCase					       =  [[a[i][j] for j in range(_UpperCamelCase)] for i in range(_UpperCamelCase       ,							_UpperCamelCase)]

      return top_left, top_right, bot_left, bot_right
def 		lowercase__  (				_UpperCamelCase) ->			tuple[int, int]:
      """simple docstring"""

      return len(_UpperCamelCase), len(matrix[0])
def 		lowercase__  (				_UpperCamelCase) ->			None:
      """simple docstring"""

      print('\n'.join(str(_UpperCamelCase) for line in matrix))
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			list:
      """simple docstring"""

      if matrix_dimensions(_UpperCamelCase) == (2, 2):
            return default_matrix_multiplication(_UpperCamelCase       ,							_UpperCamelCase)

      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  split_matrix(_UpperCamelCase)
      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  split_matrix(_UpperCamelCase)

      UpperCamelCase					       =  actual_strassen(_UpperCamelCase       ,							matrix_subtraction(_UpperCamelCase       ,							_UpperCamelCase))
      UpperCamelCase					       =  actual_strassen(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							_UpperCamelCase)
      UpperCamelCase					       =  actual_strassen(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							_UpperCamelCase)
      UpperCamelCase					       =  actual_strassen(_UpperCamelCase       ,							matrix_subtraction(_UpperCamelCase       ,							_UpperCamelCase))
      UpperCamelCase					       =  actual_strassen(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							matrix_addition(_UpperCamelCase       ,							_UpperCamelCase))
      UpperCamelCase					       =  actual_strassen(matrix_subtraction(_UpperCamelCase       ,							_UpperCamelCase)       ,							matrix_addition(_UpperCamelCase       ,							_UpperCamelCase))
      UpperCamelCase					       =  actual_strassen(matrix_subtraction(_UpperCamelCase       ,							_UpperCamelCase)       ,							matrix_addition(_UpperCamelCase       ,							_UpperCamelCase))

      UpperCamelCase					       =  matrix_addition(matrix_subtraction(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							_UpperCamelCase)       ,							_UpperCamelCase)
      UpperCamelCase					       =  matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)
      UpperCamelCase					       =  matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)
      UpperCamelCase					       =  matrix_subtraction(matrix_subtraction(matrix_addition(_UpperCamelCase       ,							_UpperCamelCase)       ,							_UpperCamelCase)       ,							_UpperCamelCase)

      # construct the new matrix from our 4 quadrants
      UpperCamelCase					       =  []
      for i in range(len(_UpperCamelCase)):
            new_matrix.append(top_left[i] + top_right[i])
      for i in range(len(_UpperCamelCase)):
            new_matrix.append(bot_left[i] + bot_right[i])
      return new_matrix
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			list:
      """simple docstring"""

      if matrix_dimensions(_UpperCamelCase)[1] != matrix_dimensions(_UpperCamelCase)[0]:
            UpperCamelCase					       =  (
                'Unable to multiply these matrices, please check the dimensions.\n'
                F'Matrix A: {matrixa}\n'
                F'Matrix B: {matrixa}'
            )
            raise Exception(_UpperCamelCase)
      UpperCamelCase					       =  matrix_dimensions(_UpperCamelCase)
      UpperCamelCase					       =  matrix_dimensions(_UpperCamelCase)

      if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
            return [matrixa, matrixa]

      UpperCamelCase					       =  max(*_UpperCamelCase       ,							*_UpperCamelCase)
      UpperCamelCase					       =  int(math.pow(2       ,							math.ceil(math.loga(_UpperCamelCase))))
      UpperCamelCase					       =  matrixa
      UpperCamelCase					       =  matrixa

      # Adding zeros to the matrices so that the arrays dimensions are the same and also
      # power of 2
      for i in range(0       ,							_UpperCamelCase):
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]       ,							_UpperCamelCase):
                        new_matrixa[i].append(0)
            else:
                  new_matrixa.append([0] * maxim)
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]       ,							_UpperCamelCase):
                        new_matrixa[i].append(0)
            else:
                  new_matrixa.append([0] * maxim)

      UpperCamelCase					       =  actual_strassen(_UpperCamelCase       ,							_UpperCamelCase)

      # Removing the additional zeros
      for i in range(0       ,							_UpperCamelCase):
            if i < dimensiona[0]:
                  for _ in range(dimensiona[1]       ,							_UpperCamelCase):
                        final_matrix[i].pop()
            else:
                  final_matrix.pop()
      return final_matrix


if __name__ == "__main__":
       __magic_name__   :  Optional[Any]        =							[
           [2, 3, 4, 5],
           [6, 4, 3, 1],
           [2, 3, 6, 7],
           [3, 1, 2, 4],
           [2, 3, 4, 5],
           [6, 4, 3, 1],
           [2, 3, 6, 7],
           [3, 1, 2, 4],
           [2, 3, 4, 5],
           [6, 2, 3, 1],
       ]
       __magic_name__   :  List[str]        =							[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
       print(strassen(matrixa, matrixa))



