





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__   :						List[str]    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """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       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase=None						,   **lowerCamelCase	):
       logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."	)
       _snake_case     		=				model
       _snake_case     		=				kwargs.get("model_save_dir"						,   lowerCamelCase	)
       _snake_case     		=				kwargs.get("latest_model_name"						,   lowerCamelCase	)

     def __call__(					self						,   **lowerCamelCase	):
       _snake_case     		=				{k: np.array(lowerCamelCase	) for k, v in kwargs.items()}
       return self.model.run(lowerCamelCase						,   lowerCamelCase	)

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

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

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase	):
       _snake_case     		=				file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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

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






     @classmethod
     def 	UpperCamelCase(					cls						,   lowerCamelCase						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				None
       if len(str(lowerCamelCase	).split("@"	)	) == 2:
         _snake_case		,		_snake_case     		=				model_id.split("@"	)

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









'''simple docstring'''



import math


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				0.0
       _snake_case     		=				0.0
       for i in range(len(lowerCamelCase	)	):
         da += math.pow((sample[i] - weights[0][i])						,   2	)
         da += math.pow((sample[i] - weights[1][i])						,   2	)
         return 0 if da > da else 1
       return 0






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       for i in range(len(lowerCamelCase	)	):
         weights[j][i] += alpha * (sample[i] - weights[j][i])
       return weights







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _snake_case     		=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _snake_case     		=				SelfOrganizingMap()
  _snake_case     		=				3
  _snake_case     		=				0.5

  for _ in range(SCREAMING_SNAKE_CASE__       ):
    for j in range(len(SCREAMING_SNAKE_CASE__       )       ):
      # training sample
      _snake_case     		=				training_samples[j]

      # Compute the winning vector
      _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

      # Update the winning vector
      _snake_case     		=				self_organizing_map.update(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

    # classify test sample
  _snake_case     		=				[0, 0, 0, 1]
  _snake_case     		=				self_organizing_map.get_winner(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''       )
  print(f'''Weights that have been trained : {weights}'''       )


# running the main() function
if __name__ == "__main__":
 main()


