code: 
  
  
  '''simple docstring'''
  
  import tempfile
  
  import torch
  
  from diffusers import (
      DEISMultistepScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      UniPCMultistepScheduler,
  )
  
  from .test_schedulers import SchedulerCommonTest
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  Any						      =							(DPMSolverMultistepScheduler,)
        lowerCAmelCase__      :  List[str]						      =							(("""num_inference_steps""", 25),)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple     ,		**UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				{
                   '''num_train_timesteps''': 1000,
                   '''beta_start''': 0.00_01,
                   '''beta_end''': 0.02,
                   '''beta_schedule''': '''linear''',
                   '''solver_order''': 2,
                   '''prediction_type''': '''epsilon''',
                   '''thresholding''': False,
                   '''sample_max_value''': 1.0,
                   '''algorithm_type''': '''dpmsolver++''',
                   '''solver_type''': '''midpoint''',
                   '''lower_order_final''': False,
                   '''lambda_min_clipped''': -float('''inf'''				),
                   '''variance_type''': None,
               }
  
               config.update(**UpperCamelCase				)
               return config
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					List[Any]=0     ,		**UpperCamelCase					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				dict(self.forward_default_kwargs				)
               lowercase__       =				kwargs.pop('''num_inference_steps'''     ,		UpperCamelCase				)
               lowercase__       =				self.dummy_sample
               lowercase__       =				0.1 * sample
               lowercase__       =				[residual + 0.2, residual + 0.15, residual + 0.10]
  
               for scheduler_class in self.scheduler_classes:
                      lowercase__       =				self.get_scheduler_config(**UpperCamelCase				)
                      lowercase__       =				scheduler_class(**UpperCamelCase				)
                      scheduler.set_timesteps(UpperCamelCase				)
                      # copy over dummy past residuals
                      lowercase__       =				dummy_past_residuals[: scheduler.config.solver_order]
  
                      with tempfile.TemporaryDirectory() as tmpdirname:
                             scheduler.save_config(UpperCamelCase				)
                             lowercase__       =				scheduler_class.from_pretrained(UpperCamelCase				)
                             new_scheduler.set_timesteps(UpperCamelCase				)
                             # copy over dummy past residuals
                             lowercase__       =				dummy_past_residuals[: new_scheduler.config.solver_order]
  
                      lowercase__    ,lowercase__       =				sample, sample
                      for t in range(UpperCamelCase     ,		time_step + scheduler.config.solver_order + 1				):
                             lowercase__       =				scheduler.step(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		**UpperCamelCase				).prev_sample
                             lowercase__       =				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    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               pass
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Any=0     ,		**UpperCamelCase					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				dict(self.forward_default_kwargs				)
               lowercase__       =				kwargs.pop('''num_inference_steps'''     ,		UpperCamelCase				)
               lowercase__       =				self.dummy_sample
               lowercase__       =				0.1 * sample
               lowercase__       =				[residual + 0.2, residual + 0.15, residual + 0.10]
  
               for scheduler_class in self.scheduler_classes:
                      lowercase__       =				self.get_scheduler_config()
                      lowercase__       =				scheduler_class(**UpperCamelCase				)
                      scheduler.set_timesteps(UpperCamelCase				)
  
                      # copy over dummy past residuals (must be after setting timesteps)
                      lowercase__       =				dummy_past_residuals[: scheduler.config.solver_order]
  
                      with tempfile.TemporaryDirectory() as tmpdirname:
                             scheduler.save_config(UpperCamelCase				)
                             lowercase__       =				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)
                             lowercase__       =				dummy_past_residuals[: new_scheduler.config.solver_order]
  
                      lowercase__       =				scheduler.step(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		**UpperCamelCase				).prev_sample
                      lowercase__       =				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    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					str=None     ,		**UpperCamelCase					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if scheduler is None:
                      lowercase__       =				self.scheduler_classes[0]
                      lowercase__       =				self.get_scheduler_config(**UpperCamelCase				)
                      lowercase__       =				scheduler_class(**UpperCamelCase				)
  
               lowercase__       =				10
               lowercase__       =				self.dummy_model()
               lowercase__       =				self.dummy_sample_deter
               scheduler.set_timesteps(UpperCamelCase				)
  
               for i, t in enumerate(scheduler.timesteps				):
                      lowercase__       =				model(UpperCamelCase     ,		UpperCamelCase				)
                      lowercase__       =				scheduler.step(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				).prev_sample
  
               return sample
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				dict(self.forward_default_kwargs				)
  
               lowercase__       =				kwargs.pop('''num_inference_steps'''     ,		UpperCamelCase				)
  
               for scheduler_class in self.scheduler_classes:
                      lowercase__       =				self.get_scheduler_config()
                      lowercase__       =				scheduler_class(**UpperCamelCase				)
  
                      lowercase__       =				self.dummy_sample
                      lowercase__       =				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'''				):
                             lowercase__       =				num_inference_steps
  
                      # copy over dummy past residuals (must be done after set_timesteps)
                      lowercase__       =				[residual + 0.2, residual + 0.15, residual + 0.10]
                      lowercase__       =				dummy_past_residuals[: scheduler.config.solver_order]
  
                      lowercase__       =				scheduler.timesteps[5]
                      lowercase__       =				scheduler.timesteps[6]
  
                      lowercase__       =				scheduler.step(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		**UpperCamelCase				).prev_sample
                      lowercase__       =				scheduler.step(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase     ,		**UpperCamelCase				).prev_sample
  
                      self.assertEqual(output_a.shape     ,		sample.shape				)
                      self.assertEqual(output_a.shape     ,		output_a.shape				)
  
  
  
        def    UpperCamelCase__	(self					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for timesteps in [25, 50, 100, 999, 1000]:
                      self.check_over_configs(num_train_timesteps=UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.check_over_configs(thresholding=UpperCamelCase				)
               for order in [1, 2, 3]:
                      for solver_type in ["midpoint", "heun"]:
                             for threshold in [0.5, 1.0, 2.0]:
                                    for prediction_type in ["epsilon", "sample"]:
                                           self.check_over_configs(
                                               thresholding=UpperCamelCase     ,		prediction_type=UpperCamelCase     ,		sample_max_value=UpperCamelCase     ,		algorithm_type='''dpmsolver++'''     ,		solver_order=UpperCamelCase     ,		solver_type=UpperCamelCase     ,		)
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(prediction_type=UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                      for solver_type in ["midpoint", "heun"]:
                             for order in [1, 2, 3]:
                                    for prediction_type in ["epsilon", "sample"]:
                                           if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                                  if order == 3:
                                                         continue
                                           else:
                                                  self.check_over_configs(
                                                      solver_order=UpperCamelCase     ,		solver_type=UpperCamelCase     ,		prediction_type=UpperCamelCase     ,		algorithm_type=UpperCamelCase     ,		)
                                           lowercase__       =				self.full_loop(
                                               solver_order=UpperCamelCase     ,		solver_type=UpperCamelCase     ,		prediction_type=UpperCamelCase     ,		algorithm_type=UpperCamelCase     ,		)
                                           assert not torch.isnan(UpperCamelCase				).any(), "Samples have nan numbers"
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.check_over_configs(lower_order_final=UpperCamelCase				)
               self.check_over_configs(lower_order_final=UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.check_over_configs(lambda_min_clipped=-float('''inf'''				)				)
               self.check_over_configs(lambda_min_clipped=-5.1				)
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               self.check_over_configs(variance_type=UpperCamelCase				)
               self.check_over_configs(variance_type='''learned_range'''				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                      self.check_over_forward(num_inference_steps=UpperCamelCase     ,		time_step=0				)
  
  
  
        def    UpperCamelCase__	(self					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.full_loop()
               lowercase__       =				torch.mean(torch.abs(UpperCamelCase				)				)
  
               assert abs(result_mean.item() - 0.33_01				) < 1E-3
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.full_loop(thresholding=UpperCamelCase     ,		dynamic_thresholding_ratio=0.87     ,		sample_max_value=0.5				)
               lowercase__       =				torch.mean(torch.abs(UpperCamelCase				)				)
  
               assert abs(result_mean.item() - 1.13_64				) < 1E-3
  
  
  
        def    UpperCamelCase__	(self					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.full_loop(prediction_type='''v_prediction'''				)
               lowercase__       =				torch.mean(torch.abs(UpperCamelCase				)				)
  
               assert abs(result_mean.item() - 0.22_51				) < 1E-3
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.full_loop(prediction_type='''v_prediction'''     ,		use_karras_sigmas=UpperCamelCase				)
               lowercase__       =				torch.mean(torch.abs(UpperCamelCase				)				)
  
               assert abs(result_mean.item() - 0.20_96				) < 1E-3
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPMSolverMultistepScheduler(**self.get_scheduler_config()				)
               lowercase__       =				self.full_loop(scheduler=UpperCamelCase				)
               lowercase__       =				torch.mean(torch.abs(UpperCamelCase				)				)
  
               assert abs(result_mean.item() - 0.33_01				) < 1E-3
  
               lowercase__       =				DPMSolverSinglestepScheduler.from_config(scheduler.config				)
               lowercase__       =				UniPCMultistepScheduler.from_config(scheduler.config				)
               lowercase__       =				DEISMultistepScheduler.from_config(scheduler.config				)
               lowercase__       =				DPMSolverMultistepScheduler.from_config(scheduler.config				)
  
               lowercase__       =				self.full_loop(scheduler=UpperCamelCase				)
               lowercase__       =				torch.mean(torch.abs(UpperCamelCase				)				)
  
               assert abs(result_mean.item() - 0.33_01				) < 1E-3
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.scheduler_classes[0]
               lowercase__       =				self.get_scheduler_config(thresholding=UpperCamelCase     ,		dynamic_thresholding_ratio=0				)
               lowercase__       =				scheduler_class(**UpperCamelCase				)
  
               lowercase__       =				10
               lowercase__       =				self.dummy_model()
               lowercase__       =				self.dummy_sample_deter.half()
               scheduler.set_timesteps(UpperCamelCase				)
  
               for i, t in enumerate(scheduler.timesteps				):
                      lowercase__       =				model(UpperCamelCase     ,		UpperCamelCase				)
                      lowercase__       =				scheduler.step(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				).prev_sample
  
               assert sample.dtype == torch.floataa
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Tuple     ,		**UpperCamelCase					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for scheduler_class in self.scheduler_classes:
                      lowercase__       =				self.get_scheduler_config(**UpperCamelCase				)
                      lowercase__       =				scheduler_class(**UpperCamelCase				)
  
                      scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
                      assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import argparse
  import collections
  from pathlib import Path
  
  import torch
  from torch.serialization import default_restore_location
  
  from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
  
  
  lowerCamelCase							:     List[str]								=			collections.namedtuple(
      'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
  )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> CheckpointState:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         print(f"Reading saved model from {model_file}" )
         lowercase__       =				torch.load(A						,				map_location=lambda A						,				A : default_restore_location(A						,				'''cpu''' ) )
         return CheckpointState(**A )
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					Any     ,		UpperCamelCase					:					Path				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				src_file
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               raise NotImplementedError
  
  
  
  
  
  
        @staticmethod
        def    UpperCamelCase__	(UpperCamelCase					:					str     ,		*UpperCamelCase					:					Dict     ,		**UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if comp_type.startswith('''c'''				):
                      return DPRContextEncoderState(*UpperCamelCase     ,		**UpperCamelCase				)
               if comp_type.startswith('''q'''				):
                      return DPRQuestionEncoderState(*UpperCamelCase     ,		**UpperCamelCase				)
               if comp_type.startswith('''r'''				):
                      return DPRReaderState(*UpperCamelCase     ,		**UpperCamelCase				)
               else:
                      raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''				)
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR biencoder from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               lowercase__    ,lowercase__       =				model.ctx_encoder, '''ctx_model.'''
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{'''bert_model.embeddings.position_ids''': model.ctx_encoder.bert_model.embeddings.position_ids}
               for key, value in saved_state.model_dict.items():
                      if key.startswith(UpperCamelCase				):
                             lowercase__       =				key[len(UpperCamelCase				) :]
                             if not key.startswith('''encode_proj.'''				):
                                    lowercase__       =				'''bert_model.''' + key
                             lowercase__       =				value
               encoder.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR biencoder from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               lowercase__    ,lowercase__       =				model.question_encoder, '''question_model.'''
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{'''bert_model.embeddings.position_ids''': model.question_encoder.bert_model.embeddings.position_ids}
               for key, value in saved_state.model_dict.items():
                      if key.startswith(UpperCamelCase				):
                             lowercase__       =				key[len(UpperCamelCase				) :]
                             if not key.startswith('''encode_proj.'''				):
                                    lowercase__       =				'''bert_model.''' + key
                             lowercase__       =				value
               encoder.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR reader from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{
                   '''encoder.bert_model.embeddings.position_ids''': model.span_predictor.encoder.bert_model.embeddings.position_ids
               }
               for key, value in saved_state.model_dict.items():
                      if key.startswith('''encoder.'''				) and not key.startswith('''encoder.encode_proj'''				):
                             lowercase__       =				'''encoder.bert_model.''' + key[len('''encoder.'''				) :]
                      lowercase__       =				value
               model.span_predictor.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				Path(A )
         dest_dir.mkdir(exist_ok=A )
  
         lowercase__       =				DPRState.from_type(A						,				src_file=A )
         lowercase__       =				dpr_state.load_dpr_model()
         model.save_pretrained(A )
         model.from_pretrained(A )  # sanity check
  
  
  if __name__ == "__main__":
   lowerCamelCase							:     List[Any]								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
   )
   parser.add_argument(
       '--src',
       type=str,
       help=(
           'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
           ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
           ' \'retriever\' checkpoints.'
       ),
   )
   parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
   lowerCamelCase							:     Optional[int]								=			parser.parse_args()
  
   lowerCamelCase							:     List[str]								=			Path(args.src)
   lowerCamelCase							:     List[Any]								=			f"""converted-{src_file.name}""" if args.dest is None else args.dest
   lowerCamelCase							:     Optional[int]								=			Path(dest_dir)
   assert src_file.exists()
   assert (
       args.type is not None
   ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
   convert(args.type, src_file, dest_dir)
  
  
  
  
  