




import mpmath  # for roots of unity
import numpy as np




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	str       ,     a       :	List[str]=None       ,     a       :	Optional[int]=None			)			->					Dict:
										# Input as list
										SCREAMING_SNAKE_CASE           =					list(poly_a or [0]			)[:]
										SCREAMING_SNAKE_CASE           =					list(poly_b or [0]			)[:]

										# Remove leading zero coefficients
										while self.polyA[-1] == 0:
													self.polyA.pop()
										SCREAMING_SNAKE_CASE           =					len(self.polyA			)

										while self.polyB[-1] == 0:
													self.polyB.pop()
										SCREAMING_SNAKE_CASE           =					len(self.polyB			)

										# Add 0 to make lengths equal a power of 2
										SCREAMING_SNAKE_CASE           =					int(
										    2 ** np.ceil(np.loga(len(self.polyA			) + len(self.polyB			) - 1			)			)			)

										while len(self.polyA			) < self.c_max_length:
													self.polyA.append(0			)
										while len(self.polyB			) < self.c_max_length:
													self.polyB.append(0			)
										# A complex root used for the fourier transform
										SCREAMING_SNAKE_CASE           =					complex(mpmath.root(x=1       ,     n=self.c_max_length       ,     k=1			)			)

										# The product
										SCREAMING_SNAKE_CASE           =					self.__multiply()





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	Union[str, Any]			)			->					str:
										SCREAMING_SNAKE_CASE           =					[[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
										# Corner case
										if len(a			) <= 1:
													return dft[0]
										#
										SCREAMING_SNAKE_CASE           =					self.c_max_length // 2
										while next_ncol > 0:
													SCREAMING_SNAKE_CASE           =					[[] for i in range(a			)]
													SCREAMING_SNAKE_CASE           =					self.root**next_ncol

													# First half of next step
													SCREAMING_SNAKE_CASE           =					1
													for j in range(self.c_max_length // (next_ncol * 2)			):
																for i in range(a			):
																			new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]			)
																current_root *= root
													# Second half of next step
													SCREAMING_SNAKE_CASE           =					1
													for j in range(self.c_max_length // (next_ncol * 2)			):
																for i in range(a			):
																			new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]			)
																current_root *= root
													# Update
													SCREAMING_SNAKE_CASE           =					new_dft
													SCREAMING_SNAKE_CASE           =					next_ncol // 2
										return dft[0]





							def    _UpperCAmelCase     (					self       :	Any			)			->					str:
										SCREAMING_SNAKE_CASE           =					self.__dft("""A"""			)
										SCREAMING_SNAKE_CASE           =					self.__dft("""B"""			)
										SCREAMING_SNAKE_CASE           =					[[dft_a[i] * dft_b[i] for i in range(self.c_max_length			)]]
										del dft_a
										del dft_b

										# Corner Case
										if len(inverce_c[0]			) <= 1:
													return inverce_c[0]
										# Inverse DFT
										SCREAMING_SNAKE_CASE           =					2
										while next_ncol <= self.c_max_length:
													SCREAMING_SNAKE_CASE           =					[[] for i in range(a			)]
													SCREAMING_SNAKE_CASE           =					self.root ** (next_ncol // 2)
													SCREAMING_SNAKE_CASE           =					1
													# First half of next step
													for j in range(self.c_max_length // next_ncol			):
																for i in range(next_ncol // 2			):
																			# Even positions
																			new_inverse_c[i].append(
																			    (
																			        inverce_c[i][j]
																			        + inverce_c[i][j + self.c_max_length // next_ncol]
																			    )
																			    / 2			)
																			# Odd positions
																			new_inverse_c[i + next_ncol // 2].append(
																			    (
																			        inverce_c[i][j]
																			        - inverce_c[i][j + self.c_max_length // next_ncol]
																			    )
																			    / (2 * current_root)			)
																current_root *= root
													# Update
													SCREAMING_SNAKE_CASE           =					new_inverse_c
													next_ncol *= 2
										# Unpack
										SCREAMING_SNAKE_CASE           =					[round(x[0].real       ,     8			) + round(x[0].imag       ,     8			) * 1j for x in inverce_c]

										# Remove leading 0's
										while inverce_c[-1] == 0:
													inverce_c.pop()
										return inverce_c







							def __str__(					self       :	int			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""A = """ + """ + """.join(
										    f"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]			)			)
										SCREAMING_SNAKE_CASE           =					"""B = """ + """ + """.join(
										    f"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]			)			)
										SCREAMING_SNAKE_CASE           =					"""A*B = """ + """ + """.join(
										    f"""{coef}*x^{i}""" for coef, i in enumerate(self.product			)			)

										return f"""{a}\n{b}\n{c}"""


# Unit tests
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


__A						:							str							=  logging.get_logger(__name__)

__A						:							str							=  {
    """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 			UpperCAmelCase_      :




							'''simple docstring'''



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





							def __call__(					self       :	List[str]       ,     **a       :	Any			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					{k: np.array(a			) for k, v in kwargs.items()}
										return self.model.run(a       ,     a			)





							@staticmethod
							def    _UpperCAmelCase     (					a       :	Union[str, Path]       ,     a       :	Any=None       ,     a       :	Optional[int]=None			)			->					Optional[Any]:
										if provider is None:
													logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""			)
													SCREAMING_SNAKE_CASE           =					"""CPUExecutionProvider"""

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





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	Union[str, Path]       ,     a       :	Optional[str] = None       ,     **a       :	str			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Union[str, os.PathLike]       ,     **a       :	Tuple       ,     )			->					str:
										if os.path.isfile(a			):
													logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""			)
													return

										os.makedirs(a       ,     exist_ok=a			)

										# saving model weights/files
										self._save_pretrained(a       ,     **a			)





							@classmethod
							def    _UpperCAmelCase     (					cls       :	List[Any]       ,     a       :	Union[str, Path]       ,     a       :	Optional[Union[bool, str, None]] = None       ,     a       :	Optional[Union[str, None]] = None       ,     a       :	bool = False       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     a       :	Optional["ort.SessionOptions"] = None       ,     **a       :	Union[str, Any]       ,     )			->					List[str]:
										SCREAMING_SNAKE_CASE           =					file_name if file_name is not None else ONNX_WEIGHTS_NAME
										# load model from local directory
										if os.path.isdir(a			):
													SCREAMING_SNAKE_CASE           =					OnnxRuntimeModel.load_model(
													    os.path.join(a       ,     a			)       ,     provider=a       ,     sess_options=a			)
													SCREAMING_SNAKE_CASE           =					Path(a			)
										# load model from hub
										else:
													# download model
													SCREAMING_SNAKE_CASE           =					hf_hub_download(
													    repo_id=a       ,     filename=a       ,     use_auth_token=a       ,     revision=a       ,     cache_dir=a       ,     force_download=a       ,     )
													SCREAMING_SNAKE_CASE           =					Path(a			).parent
													SCREAMING_SNAKE_CASE           =					Path(a			).name
													SCREAMING_SNAKE_CASE           =					OnnxRuntimeModel.load_model(a       ,     provider=a       ,     sess_options=a			)
										return cls(model=a       ,     **a			)







							@classmethod
							def    _UpperCAmelCase     (					cls       :	List[Any]       ,     a       :	Union[str, Path]       ,     a       :	bool = True       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     **a       :	Union[str, Any]       ,     )			->					Any:
										SCREAMING_SNAKE_CASE           =					None
										if len(str(a			).split("""@"""			)			) == 2:
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					model_id.split("""@"""			)

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






