



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


lowerCAmelCase__					  =  logging.get_logger(__name__)

lowerCAmelCase__					  =  {
    '''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 :     List[str]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=None				,       **SCREAMING_SNAKE_CASE :     int					):
									logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."					)
									lowercase__					:   List[Any]													=					model
									lowercase__					:   Dict													=					kwargs.get("model_save_dir"				,       SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					kwargs.get("latest_model_name"				,       SCREAMING_SNAKE_CASE					)







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







					@staticmethod
					def 			snake_case					(							SCREAMING_SNAKE_CASE :     Union[str, Path]				,       SCREAMING_SNAKE_CASE :     Any=None				,       SCREAMING_SNAKE_CASE :     List[Any]=None					):
									if provider is None:
													logger.info("No onnxruntime provider specified, using CPUExecutionProvider"					)
													lowercase__					:   List[Any]													=					"CPUExecutionProvider"

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







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

									lowercase__					:   Union[str, Any]													=					self.model_save_dir.joinpath(self.latest_model_name					)
									lowercase__					:   Optional[Any]													=					Path(SCREAMING_SNAKE_CASE					).joinpath(SCREAMING_SNAKE_CASE					)
									try:
													shutil.copyfile(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									except shutil.SameFileError:
													pass

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







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

									os.makedirs(SCREAMING_SNAKE_CASE				,       exist_ok=SCREAMING_SNAKE_CASE					)

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







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







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

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''NllbTokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''NllbTokenizerFast''']


if TYPE_CHECKING:
				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb import NllbTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb_fast import NllbTokenizerFast

else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




