



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


lowercase_								=     logging.get_logger(__name__)

lowercase_								=     {
    """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			:




				def __init__(						self					:						str,					__lowercase					:						str=None,					**__lowercase					:						Optional[int]						):
							logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."						)
							lowercase__					  =				model
							lowercase__					  =				kwargs.get("model_save_dir",					__lowercase						)
							lowercase__					  =				kwargs.get("latest_model_name",					__lowercase						)




				def __call__(						self					:						Dict,					**__lowercase					:						str						):
							lowercase__					  =				{k: np.array(__lowercase						) for k, v in kwargs.items()}
							return self.model.run(__lowercase,					__lowercase						)




				@staticmethod
				def 							A__      (						__lowercase					:						Union[str, Path],					__lowercase					:						Tuple=None,					__lowercase					:						str=None						):
							if provider is None:
										logger.info("No onnxruntime provider specified, using CPUExecutionProvider"						)
										lowercase__					  =				"CPUExecutionProvider"

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




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[str, Path],					__lowercase					:						Optional[str] = None,					**__lowercase					:						int						):
							lowercase__					  =				file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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




				def 							A__      (						self					:						List[Any],					__lowercase					:						Union[str, os.PathLike],					**__lowercase					:						Dict,					):
							if os.path.isfile(__lowercase						):
										logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''						)
										return

							os.makedirs(__lowercase,					exist_ok=__lowercase						)

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




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




				@classmethod
				def 							A__      (						cls					:						Dict,					__lowercase					:						Union[str, Path],					__lowercase					:						bool = True,					__lowercase					:						Optional[str] = None,					__lowercase					:						Optional[str] = None,					**__lowercase					:						Union[str, Any],					):
							lowercase__					  =				None
							if len(str(__lowercase						).split("@"						)						) == 2:
										lowercase__  ,       lowercase__					  =				model_id.split("@"						)

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









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			if upper_limit < 0:
						raise ValueError("Limit for the Catalan sequence must be ≥ 0"  )

			lowercase__					  =				[0] * (upper_limit + 1)

			# Base case: C(0) = C(1) = 1
			lowercase__					  =				1
			if upper_limit > 0:
						lowercase__					  =				1

			# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
			for i in range(2						,  upper_limit + 1  ):
						for j in range(SCREAMING_SNAKE_CASE_  ):
									catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

			return catalan_list


if __name__ == "__main__":
							print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
							print("""\n*** Enter -1 at any time to quit ***""")
							print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
							try:
														while True:
																					lowercase_								=     int(input().strip())
																					if N < 0:
																												print("""\n********* Goodbye!! ************""")
																												break
																					else:
																												print(F'The Catalan numbers from 0 through {N} are:')
																												print(catalan_numbers(N))
																												print("""Try another upper limit for the sequence: """, end="""""")
							except (NameError, ValueError):
														print("""\n********* Invalid input, goodbye! ************\n""")

							import doctest

							doctest.testmod()




