






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


UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						{
    "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 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		List[str]					,   __UpperCAmelCase :		Tuple=None					,   **__UpperCAmelCase :		List[str]    )       ->List[Any]:




       """simple docstring"""


       logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''    )
       a						    = model
       a						    = kwargs.get('''model_save_dir'''					,   __UpperCAmelCase    )
       a						    = kwargs.get('''latest_model_name'''					,   __UpperCAmelCase    )







      def __call__( self :		List[str]					,   **__UpperCAmelCase :		int    )       ->Union[str, Any]:




       """simple docstring"""


       a						    = {k: np.array(__UpperCAmelCase    ) for k, v in kwargs.items()}
       return self.model.run(__UpperCAmelCase					,   __UpperCAmelCase    )







      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		Union[str, Path]					,   __UpperCAmelCase :		Optional[Any]=None					,   __UpperCAmelCase :		str=None    )       ->Tuple:




       """simple docstring"""


       if provider is None:
        logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''    )
        a						    = '''CPUExecutionProvider'''

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







      def  __lowerCAmelCase   ( self :		List[str]					,   __UpperCAmelCase :		Union[str, Path]					,   __UpperCAmelCase :		Optional[str] = None					,   **__UpperCAmelCase :		List[str]    )       ->List[Any]:




       """simple docstring"""


       a						    = file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

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







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		Union[str, os.PathLike]					,   **__UpperCAmelCase :		Optional[Any]					,   )       ->List[str]:




       """simple docstring"""


       if os.path.isfile(__UpperCAmelCase    ):
        logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""    )
        return

       os.makedirs(__UpperCAmelCase					,   exist_ok=__UpperCAmelCase    )

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







      @classmethod
      def  __lowerCAmelCase   ( cls :		List[Any]					,   __UpperCAmelCase :		Union[str, Path]					,   __UpperCAmelCase :		Optional[Union[bool, str, None]] = None					,   __UpperCAmelCase :		Optional[Union[str, None]] = None					,   __UpperCAmelCase :		bool = False					,   __UpperCAmelCase :		Optional[str] = None					,   __UpperCAmelCase :		Optional[str] = None					,   __UpperCAmelCase :		Optional[str] = None					,   __UpperCAmelCase :		Optional["ort.SessionOptions"] = None					,   **__UpperCAmelCase :		Union[str, Any]					,   )       ->List[Any]:




       """simple docstring"""


       a						    = file_name if file_name is not None else ONNX_WEIGHTS_NAME
       # load model from local directory
       if os.path.isdir(__UpperCAmelCase    ):
        a						    = OnnxRuntimeModel.load_model(
            os.path.join(__UpperCAmelCase					,   __UpperCAmelCase    )					,   provider=__UpperCAmelCase					,   sess_options=__UpperCAmelCase    )
        a						    = Path(__UpperCAmelCase    )
       # load model from hub
       else:
        # download model
        a						    = hf_hub_download(
            repo_id=__UpperCAmelCase					,   filename=__UpperCAmelCase					,   use_auth_token=__UpperCAmelCase					,   revision=__UpperCAmelCase					,   cache_dir=__UpperCAmelCase					,   force_download=__UpperCAmelCase					,   )
        a						    = Path(__UpperCAmelCase    ).parent
        a						    = Path(__UpperCAmelCase    ).name
        a						    = OnnxRuntimeModel.load_model(__UpperCAmelCase					,   provider=__UpperCAmelCase					,   sess_options=__UpperCAmelCase    )
       return cls(model=__UpperCAmelCase					,   **__UpperCAmelCase    )







      @classmethod
      def  __lowerCAmelCase   ( cls :		str					,   __UpperCAmelCase :		Union[str, Path]					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		Optional[str] = None					,   __UpperCAmelCase :		Optional[str] = None					,   **__UpperCAmelCase :		List[str]					,   )       ->Any:




       """simple docstring"""


       a						    = None
       if len(str(__UpperCAmelCase    ).split('''@'''    )    ) == 2:
        a    ,	a						    = model_id.split('''@'''    )

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










from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, torch.Tensor]:
 a						    = []
 a						    = []
 a						    = []

 for rt in rc.restypes:
  a						    = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
  restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]					)
  a						    = {name: i for i, name in enumerate(a					)}
  restype_atomaa_to_atomaa_list.append(
      [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]					)

  restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]					)

 # Add dummy mapping for restype 'UNK'
 restype_atomaa_to_atomaa_list.append([0] * 14					)
 restype_atomaa_to_atomaa_list.append([0] * 37					)
 restype_atomaa_mask_list.append([0.0] * 14					)

 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.intaa      ,				device=protein['''aatype'''].device      ,				)
 a						    = torch.tensor(
     a      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device      ,				)
 a						    = protein['''aatype'''].to(torch.long					)

 # create the mapping for (residx, atom14) --> atom37, i.e. an array
 # with shape (num_res, 14) containing the atom37 indices for this protein
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = restype_atomaa_mask[protein_aatype]

 a						    = residx_atomaa_mask
 a						    = residx_atomaa_to_atomaa.long()

 # create the gather indices for mapping back
 a						    = restype_atomaa_to_atomaa[protein_aatype]
 a						    = residx_atomaa_to_atomaa.long()

 # create the corresponding mask
 a						    = torch.zeros([21, 37]      ,				dtype=torch.floataa      ,				device=protein['''aatype'''].device					)
 for restype, restype_letter in enumerate(rc.restypes					):
  a						    = rc.restype_atoa[restype_letter]
  a						    = rc.residue_atoms[restype_name]
  for atom_name in atom_names:
   a						    = rc.atom_order[atom_name]
   a						    = 1

 a						    = restype_atomaa_mask[protein_aatype]
 a						    = residx_atomaa_mask

 return protein

def 		_a       ( a					:Dict[str, torch.Tensor]					)				->     Dict[str, np.ndarray]:
 a						    = tree_map(lambda a					: torch.tensor(a      ,				device=batch['''aatype'''].device					)      ,				a      ,				np.ndarray					)
 a						    = tensor_tree_map(lambda a					: np.array(a					)      ,				make_atomaa_masks(a					)					)
 return out


