




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							Union[str, Any]							=  logging.get_logger(__name__)

__A						:							List[Any]							=  {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''donut-swin'''

							a__						  =     {
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}





							def __init__(					self       :	str       ,     a       :	Any=224       ,     a       :	Any=4       ,     a       :	Union[str, Any]=3       ,     a       :	Union[str, Any]=96       ,     a       :	List[str]=[2, 2, 6, 2]       ,     a       :	Dict=[3, 6, 12, 24]       ,     a       :	Optional[Any]=7       ,     a       :	Dict=4.0       ,     a       :	str=True       ,     a       :	int=0.0       ,     a       :	Optional[Any]=0.0       ,     a       :	List[Any]=0.1       ,     a       :	List[Any]="gelu"       ,     a       :	Any=False       ,     a       :	Optional[Any]=0.02       ,     a       :	Union[str, Any]=1E-5       ,     **a       :	str       ,     )			->					Dict:
										super().__init__(**a			)

										SCREAMING_SNAKE_CASE           =					image_size
										SCREAMING_SNAKE_CASE           =					patch_size
										SCREAMING_SNAKE_CASE           =					num_channels
										SCREAMING_SNAKE_CASE           =					embed_dim
										SCREAMING_SNAKE_CASE           =					depths
										SCREAMING_SNAKE_CASE           =					len(a			)
										SCREAMING_SNAKE_CASE           =					num_heads
										SCREAMING_SNAKE_CASE           =					window_size
										SCREAMING_SNAKE_CASE           =					mlp_ratio
										SCREAMING_SNAKE_CASE           =					qkv_bias
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					drop_path_rate
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					use_absolute_embeddings
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					initializer_range
										# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										SCREAMING_SNAKE_CASE           =					int(embed_dim * 2 ** (len(a			) - 1)			)












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       ,     )






