

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


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     {
    '''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     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase=None      , **_UpperCamelCase   )      ->							int:






              """simple docstring"""


              logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""   )
              __snake_case       	=							model
              __snake_case       	=							kwargs.get("""model_save_dir"""      , _UpperCamelCase   )
              __snake_case       	=							kwargs.get("""latest_model_name"""      , _UpperCamelCase   )


       def __call__(		self      , **_UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


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


       @staticmethod
       def 			a		(		_UpperCamelCase      , _UpperCamelCase=None      , _UpperCamelCase=None   )      ->							Optional[Any]:






              """simple docstring"""


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

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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __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(_UpperCamelCase   ).joinpath(_UpperCamelCase   )
              try:
                     shutil.copyfile(_UpperCamelCase      , _UpperCamelCase   )
              except shutil.SameFileError:
                     pass

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


       def 			a		(		self      , _UpperCamelCase      , **_UpperCamelCase      , )      ->							List[Any]:






              """simple docstring"""


              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 			a		(		cls      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = False      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							str:






              """simple docstring"""


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






       @classmethod
       def 			a		(		cls      , _UpperCamelCase      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							Any:






              """simple docstring"""


              __snake_case       	=							None
              if len(str(_UpperCamelCase   ).split("""@"""   )   ) == 2:
                     __snake_case	,    __snake_case       	=							model_id.split("""@"""   )

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




from __future__ import annotations

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							text, pattern
              __snake_case	,    __snake_case       	=							len(_UpperCamelCase   ), len(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              for i in range(self.patLen - 1      , -1      , -1   ):
                     if char == self.pattern[i]:
                            return i
              return -1


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              for i in range(self.patLen - 1      , -1      , -1   ):
                     if self.pattern[i] != self.text[current_pos + i]:
                            return current_pos + i
              return -1






       def 			a		(		self   )      ->							list[int]:






              """simple docstring"""


              __snake_case       	=							[]
              for i in range(self.textLen - self.patLen + 1   ):
                     __snake_case       	=							self.mismatch_in_text(_UpperCamelCase   )
                     if mismatch_index == -1:
                            positions.append(_UpperCamelCase   )
                     else:
                            __snake_case       	=							self.match_in_pattern(self.text[mismatch_index]   )
                            __snake_case       	=							(
                                mismatch_index - match_index
                            )  # shifting index lgtm [py/multiple-definition]
              return positions






UpperCamelCase__		 =     '''ABAABA'''
UpperCamelCase__		 =     '''AB'''
UpperCamelCase__		 =     BoyerMooreSearch(text, pattern)
UpperCamelCase__		 =     bms.bad_character_heuristic()

if len(positions) == 0:
    print('''No match found''')
else:
    print('''Pattern found in following positions: ''')
    print(positions)

