






import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__UpperCAmelCase								=						{
    'E': 1_2.7_0,
    'T': 9.0_6,
    'A': 8.1_7,
    'O': 7.5_1,
    'I': 6.9_7,
    'N': 6.7_5,
    'S': 6.3_3,
    'H': 6.0_9,
    'R': 5.9_9,
    'D': 4.2_5,
    'L': 4.0_3,
    'C': 2.7_8,
    'U': 2.7_6,
    'M': 2.4_1,
    'W': 2.3_6,
    'F': 2.2_3,
    'G': 2.0_2,
    'Y': 1.9_7,
    'P': 1.9_3,
    'B': 1.2_9,
    'V': 0.9_8,
    'K': 0.7_7,
    'J': 0.1_5,
    'X': 0.1_5,
    'Q': 0.1_0,
    'Z': 0.0_7,
}
__UpperCAmelCase								=						'ETAOINSHRDLCUMWFGYPBVKJXQZ'
__UpperCAmelCase								=						'ABCDEFGHIJKLMNOPQRSTUVWXYZ'




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						{letter: 0 for letter in string.ascii_uppercase}
					for letter in message.upper():
										if letter in LETTERS:
															letter_count[letter] += 1

					return letter_count




def      lowercase__      (     __snake_case    :							tuple  ):


					'''simple docstring'''


					return x[0]




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						get_letter_count(__snake_case  )
					UpperCAmelCase_    :      dict[int, list[str]]            =						{
					    freq: [] for letter, freq in letter_to_freq.items()
					}
					for letter in LETTERS:
										freq_to_letter[letter_to_freq[letter]].append(__snake_case  )

					UpperCAmelCase_    :      dict[int, str]            =						{}

					for freq in freq_to_letter:
										freq_to_letter[freq].sort(key=ETAOIN.find     ,       reverse=__snake_case  )
										UpperCAmelCase_    :      List[str]            =						''.join(freq_to_letter[freq]  )

					UpperCAmelCase_    :      int            =						list(freq_to_letter_str.items()  )
					freq_pairs.sort(key=__snake_case     ,       reverse=__snake_case  )

					UpperCAmelCase_    :      list[str]            =						[freq_pair[1] for freq_pair in freq_pairs]

					return "".join(__snake_case  )




def      lowercase__      (     __snake_case    :							str  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[Any]            =						get_frequency_order(__snake_case  )
					UpperCAmelCase_    :      Union[str, Any]            =						0
					for common_letter in ETAOIN[:6]:
										if common_letter in freq_order[:6]:
															match_score += 1

					for uncommon_letter in ETAOIN[-6:]:
										if uncommon_letter in freq_order[-6:]:
															match_score += 1

					return match_score


if __name__ == "__main__":
		import doctest

		doctest.testmod()














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   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							Dict:
							logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'   )
							UpperCAmelCase_    :      Any            =						model
							UpperCAmelCase_    :      int            =						kwargs.get('model_save_dir'				,		_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						kwargs.get('latest_model_name'				,		_UpperCamelCase   )



		def __call__(							self				,		**_UpperCamelCase   )	->							str:
							UpperCAmelCase_    :      Optional[int]            =						{k: np.array(_UpperCamelCase   ) for k, v in kwargs.items()}
							return self.model.run(_UpperCamelCase				,		_UpperCamelCase   )



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase				,		_UpperCamelCase=None				,		_UpperCamelCase=None   )	->							List[Any]:
							if provider is None:
												logger.info('No onnxruntime provider specified, using CPUExecutionProvider'   )
												UpperCAmelCase_    :      List[str]            =						'CPUExecutionProvider'

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      Any            =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

							UpperCAmelCase_    :      Optional[Any]            =						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[Any]            =						self.model_save_dir.joinpath(_UpperCamelCase   )
							if src_path.exists():
												UpperCAmelCase_    :      List[Any]            =						Path(_UpperCamelCase   ).joinpath(_UpperCamelCase   )
												try:
																	shutil.copyfile(_UpperCamelCase				,		_UpperCamelCase   )
												except shutil.SameFileError:
																	pass



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		**_UpperCamelCase				,		)	->							List[str]:
							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				,		)	->							List[str]:
							UpperCAmelCase_    :      List[str]            =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
							# load model from local directory
							if os.path.isdir(_UpperCamelCase   ):
												UpperCAmelCase_    :      Union[str, Any]            =						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_    :      List[str]            =						hf_hub_download(
												    repo_id=_UpperCamelCase				,		filename=_UpperCamelCase				,		use_auth_token=_UpperCamelCase				,		revision=_UpperCamelCase				,		cache_dir=_UpperCamelCase				,		force_download=_UpperCamelCase				,		)
												UpperCAmelCase_    :      Union[str, Any]            =						Path(_UpperCamelCase   ).parent
												UpperCAmelCase_    :      List[str]            =						Path(_UpperCamelCase   ).name
												UpperCAmelCase_    :      Union[str, Any]            =						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				,		)	->							Optional[int]:
							UpperCAmelCase_    :      List[str]            =						None
							if len(str(_UpperCamelCase   ).split('@'   )   ) == 2:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						model_id.split('@'   )

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






