




import argparse
import os
import re


__A						:							List[Any]							=  """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__A						:							Tuple							=  re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__A						:							Optional[Any]							=  re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = False   ):
			'''simple docstring'''
			with open(SCREAMING_SNAKE_CASE					,	"""r"""					,	encoding="""utf-8"""   ) as f:
						SCREAMING_SNAKE_CASE           =					f.read()

			SCREAMING_SNAKE_CASE           =					content.split("""\n"""   )
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					0
			while line_idx < len(SCREAMING_SNAKE_CASE   ):
						if _re_intro_mapping.search(lines[line_idx]   ) is not None:
									SCREAMING_SNAKE_CASE           =					len(re.search(r"""^(\s*)\S"""					,	lines[line_idx]   ).groups()[0]   ) + 8
									# Start of a new mapping!
									while not lines[line_idx].startswith(""" """ * indent + """("""   ):
												new_lines.append(lines[line_idx]   )
												line_idx += 1

									SCREAMING_SNAKE_CASE           =					[]
									while lines[line_idx].strip() != "]":
												# Blocks either fit in one line or not
												if lines[line_idx].strip() == "(":
															SCREAMING_SNAKE_CASE           =					line_idx
															while not lines[line_idx].startswith(""" """ * indent + """)"""   ):
																		line_idx += 1
															blocks.append("""\n""".join(lines[start_idx : line_idx + 1]   )   )
												else:
															blocks.append(lines[line_idx]   )
												line_idx += 1

									# Sort blocks by their identifiers
									SCREAMING_SNAKE_CASE           =					sorted(SCREAMING_SNAKE_CASE					,	key=lambda SCREAMING_SNAKE_CASE   : _re_identifier.search(SCREAMING_SNAKE_CASE   ).groups()[0]   )
									new_lines += blocks
						else:
									new_lines.append(lines[line_idx]   )
									line_idx += 1

			if overwrite:
						with open(SCREAMING_SNAKE_CASE					,	"""w"""					,	encoding="""utf-8"""   ) as f:
									f.write("""\n""".join(SCREAMING_SNAKE_CASE   )   )
			elif "\n".join(SCREAMING_SNAKE_CASE   ) != content:
						return True
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE = False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[os.path.join(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) for f in os.listdir(SCREAMING_SNAKE_CASE   ) if f.endswith(""".py"""   )]
			SCREAMING_SNAKE_CASE           =					[sort_auto_mapping(SCREAMING_SNAKE_CASE					,	overwrite=SCREAMING_SNAKE_CASE   ) for fname in fnames]

			if not overwrite and any(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					[f for f, d in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) if d]
						raise ValueError(
						    f"""The following files have auto mappings that need sorting: {", ".join(SCREAMING_SNAKE_CASE   )}. Run `make style` to fix"""
						    """ this."""   )


if __name__ == "__main__":
		__A						:							Tuple							=  argparse.ArgumentParser()
		parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
		__A						:							Union[str, Any]							=  parser.parse_args()

		sort_all_auto_mappings(not args.check_only)












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       ,     )






