




import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 						_snake_case					( _snake_case							: str							)   -> List[Any]:






      '''simple docstring'''


      if "model" in orig_key:
            _A        =       orig_key.replace('model.'    ,  ''							)
      if "norm1" in orig_key:
            _A        =       orig_key.replace('norm1'    ,  'attention.output.LayerNorm'							)
      if "norm2" in orig_key:
            _A        =       orig_key.replace('norm2'    ,  'output.LayerNorm'							)
      if "norm" in orig_key:
            _A        =       orig_key.replace('norm'    ,  'LayerNorm'							)
      if "transformer" in orig_key:
            _A        =       orig_key.split('.'							)[0].split('_'							)[-1]
            _A        =       orig_key.replace(F'''transformer_{layer_num}'''    ,  F'''encoder.layer.{layer_num}'''							)
      if "mha.attn" in orig_key:
            _A        =       orig_key.replace('mha.attn'    ,  'attention.self'							)
      if "mha" in orig_key:
            _A        =       orig_key.replace('mha'    ,  'attention'							)
      if "W_q" in orig_key:
            _A        =       orig_key.replace('W_q'    ,  'self.query'							)
      if "W_k" in orig_key:
            _A        =       orig_key.replace('W_k'    ,  'self.key'							)
      if "W_v" in orig_key:
            _A        =       orig_key.replace('W_v'    ,  'self.value'							)
      if "ff1" in orig_key:
            _A        =       orig_key.replace('ff1'    ,  'intermediate.dense'							)
      if "ff2" in orig_key:
            _A        =       orig_key.replace('ff2'    ,  'output.dense'							)
      if "ff" in orig_key:
            _A        =       orig_key.replace('ff'    ,  'output.dense'							)
      if "mlm_class" in orig_key:
            _A        =       orig_key.replace('mlm.mlm_class'    ,  'cls.predictions.decoder'							)
      if "mlm" in orig_key:
            _A        =       orig_key.replace('mlm'    ,  'cls.predictions.transform'							)
      if "cls" not in orig_key:
            _A        =       "yoso." + orig_key

      return orig_key






def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: Tuple							)   -> List[str]:






      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            _A        =       orig_state_dict.pop(__lowerCamelCase							)

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  _A        =       val

      _A        =       orig_state_dict["cls.predictions.decoder.bias"]
      _A        =       torch.arange(__lowerCamelCase							).expand((1, -1)							) + 2

      return orig_state_dict






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: Union[str, Any]    ,  _snake_case							: List[str]							)   -> Optional[int]:






      '''simple docstring'''


      _A        =       torch.load(__lowerCamelCase    ,  map_location='cpu'							)["model_state_dict"]
      _A        =       YosoConfig.from_json_file(__lowerCamelCase							)
      _A        =       YosoForMaskedLM(__lowerCamelCase							)

      _A        =       convert_checkpoint_helper(config.max_position_embeddings    ,  __lowerCamelCase							)

      print(model.load_state_dict(__lowerCamelCase							)							)
      model.eval()
      model.save_pretrained(__lowerCamelCase							)

      print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
       )
       parser.add_argument(
           '''--config_file''',
           default=None,
           type=str,
           required=True,
           help='''The json file for YOSO model config.''',
       )
       parser.add_argument(
           '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       a         =      parser.parse_args()
       convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)







"""simple docstring"""

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     (IPNDMScheduler,)
 UpperCAmelCase   :					Optional[Any]				  =     (('''num_inference_steps''', 50),)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							**_UpperCAmelCase	:	List[Any]		):
       _A        =       {'num_train_timesteps': 1_000}
       config.update(**_UpperCAmelCase		)
       return config

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Optional[int]=0   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)
             # copy over dummy past residuals
             _A        =       dummy_past_residuals[:]

             if time_step is None:
                   _A        =       scheduler.timesteps[len(scheduler.timesteps		) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   new_scheduler.set_timesteps(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def       lowerCAmelCase_							(			self	:	str		):
       pass

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Any=0   ,							**_UpperCAmelCase	:	Any		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)

             # copy over dummy past residuals (must be after setting timesteps)
             _A        =       dummy_past_residuals[:]

             if time_step is None:
                   _A        =       scheduler.timesteps[len(scheduler.timesteps		) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_UpperCAmelCase		)

                   # copy over dummy past residual (must be after setting timesteps)
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	Optional[int]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A        =       10
       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase		)

       for i, t in enumerate(scheduler.timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       for i, t in enumerate(scheduler.timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       return sample

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)

       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)

             _A        =       self.dummy_sample
             _A        =       0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   scheduler.set_timesteps(_UpperCAmelCase		)
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   _A        =       num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             _A        =       dummy_past_residuals[:]

             _A        =       scheduler.timesteps[5]
             _A        =       scheduler.timesteps[6]

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

             self.assertEqual(output_a.shape   ,							sample.shape		)
             self.assertEqual(output_a.shape   ,							output_a.shape		)

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

             self.assertEqual(output_a.shape   ,							sample.shape		)
             self.assertEqual(output_a.shape   ,							output_a.shape		)

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

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       for t, num_inference_steps in zip([1, 5, 10]   ,							[10, 50, 100]		):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.full_loop()
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_mean.item() - 2_540_529		) < 10

