




"""simple docstring"""

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      (KDPMaDiscreteScheduler,)
 UpperCAmelCase       :	Any     =      10

 def      lowerCAmelCase_				(					self			:			Dict       ,				**_UpperCAmelCase			:			Optional[Any]						):
       _A     =  {
           'num_train_timesteps': 1_100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**_UpperCAmelCase						)
       return config

 def      lowerCAmelCase_				(					self			:			Any						):
       for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]       ,				[0.0002, 0.002, 0.02]						):
             self.check_over_configs(beta_start=_UpperCAmelCase       ,				beta_end=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config(prediction_type='v_prediction'						)
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _A     =  sample.to(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4E-0_7						) < 1E-2
             assert abs(result_mean.item() - 6.1_1_1_2E-1_0						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7						) < 1E-2
             assert abs(result_mean.item() - 0.0002						) < 1E-3

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter * scheduler.init_noise_sigma
       _A     =  sample.to(_UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3



 def      lowerCAmelCase_				(					self			:			Any						):
       if torch_device == "mps":
             return
       _A     =  self.scheduler_classes[0]
       _A     =  self.get_scheduler_config()
       _A     =  scheduler_class(**_UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps       ,				device=_UpperCAmelCase						)

       _A     =  self.dummy_model()
       _A     =  self.dummy_sample_deter.to(_UpperCAmelCase						) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
             _A     =  scheduler.scale_model_input(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  model(_UpperCAmelCase       ,				_UpperCAmelCase						)

             _A     =  scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)
             _A     =  output.prev_sample

       _A     =  torch.sum(torch.abs(_UpperCAmelCase						)						)
       _A     =  torch.mean(torch.abs(_UpperCAmelCase						)						)

       if str(_UpperCAmelCase						).startswith('cpu'						):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3
       else:
             # CUDA
             assert abs(result_sum.item() - 20.4125						) < 1E-2
             assert abs(result_mean.item() - 0.0266						) < 1E-3







"""simple docstring"""

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							      =     logging.get_logger(__name__)

a							      =     {
    '''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			:			Optional[Any]       ,				_UpperCAmelCase			:			Dict=None       ,				**_UpperCAmelCase			:			Optional[Any]						):
       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			:			Dict       ,				**_UpperCAmelCase			:			List[Any]						):
       _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			:			List[str]=None       ,				_UpperCAmelCase			:			List[Any]=None						):
       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			:			int       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			List[Any]						):
       _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			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, os.PathLike]       ,				**_UpperCAmelCase			:			List[str]       ,				):
       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			:			Tuple       ,				_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]       ,				):
       _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			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _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       ,				)

