'''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


__snake_case  : Union[str, Any]		       =    collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)





def 	_UpperCAmelCase       (				_UpperCamelCase       :      str  )      -> CheckpointState:
    print(F'''Reading saved model from {model_file}'''  )
    A_      =  torch.load(_UpperCamelCase,  map_location=lambda _UpperCamelCase,  _UpperCamelCase  : default_restore_location(_UpperCamelCase,  '''cpu'''  )  )
    return CheckpointState(**_UpperCamelCase  )




class       __UpperCAmelCase							:





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE					)						->	List[str]:
           A_      =  src_file

       def    __A			( self					)						->	str:
           raise NotImplementedError

       @staticmethod
       def    __A			( _SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	"DPRState":
           if comp_type.startswith('''c'''					):
               return DPRContextEncoderState(*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           if comp_type.startswith('''q'''					):
               return DPRQuestionEncoderState(*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           if comp_type.startswith('''r'''					):
               return DPRReaderState(*_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           else:
               raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''					)




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def    __A			( self					)						->	Optional[Any]:
           A_      =  DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''					)[0]					)					)
           print(F'''Loading DPR biencoder from {self.src_file}'''					)
           A_      =  load_states_from_checkpoint(self.src_file					)
           A_  ,A_      =  model.ctx_encoder, '''ctx_model.'''
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           A_      =  {'''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(_SCREAMING_SNAKE_CASE					):
                   A_      =  key[len(_SCREAMING_SNAKE_CASE					) :]
                   if not key.startswith('''encode_proj.'''					):
                       A_      =  '''bert_model.''' + key
                   A_      =  value
           encoder.load_state_dict(_SCREAMING_SNAKE_CASE					)
           return model




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def    __A			( self					)						->	Any:
           A_      =  DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''					)[0]					)					)
           print(F'''Loading DPR biencoder from {self.src_file}'''					)
           A_      =  load_states_from_checkpoint(self.src_file					)
           A_  ,A_      =  model.question_encoder, '''question_model.'''
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           A_      =  {'''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(_SCREAMING_SNAKE_CASE					):
                   A_      =  key[len(_SCREAMING_SNAKE_CASE					) :]
                   if not key.startswith('''encode_proj.'''					):
                       A_      =  '''bert_model.''' + key
                   A_      =  value
           encoder.load_state_dict(_SCREAMING_SNAKE_CASE					)
           return model




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def    __A			( self					)						->	Optional[Any]:
           A_      =  DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''					)[0]					)					)
           print(F'''Loading DPR reader from {self.src_file}'''					)
           A_      =  load_states_from_checkpoint(self.src_file					)
           # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
           A_      =  {
               '''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'''					):
                   A_      =  '''encoder.bert_model.''' + key[len('''encoder.'''					) :]
               A_      =  value
           model.span_predictor.load_state_dict(_SCREAMING_SNAKE_CASE					)
           return model





def 	_UpperCAmelCase       (				_UpperCamelCase       :      str,  _UpperCamelCase       :      Path,  _UpperCamelCase       :      Path  )      -> List[Any]:
    A_      =  Path(_UpperCamelCase  )
    dest_dir.mkdir(exist_ok=_UpperCamelCase  )

    A_      =  DPRState.from_type(_UpperCamelCase,  src_file=_UpperCamelCase  )
    A_      =  dpr_state.load_dpr_model()
    model.save_pretrained(_UpperCamelCase  )
    model.from_pretrained(_UpperCamelCase  )  # sanity check


if __name__ == "__main__":
  __snake_case  : str		       =    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.')
  __snake_case  : Union[str, Any]		       =    parser.parse_args()

  __snake_case  : Tuple		       =    Path(args.src)
  __snake_case  : Tuple		       =    F"""converted-{src_file.name}""" if args.dest is None else args.dest
  __snake_case  : Optional[Any]		       =    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)







'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   str  					=							(ScoreSdeVeScheduler,)
       __lowercase   :   List[str]  					=							()

       @property
       def    __A			( self					)						->	List[str]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  torch.rand((batch_size, num_channels, height, width)					)

           return sample

       @property
       def    __A			( self					)						->	Optional[Any]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  batch_size * num_channels * height * width
           A_      =  torch.arange(_SCREAMING_SNAKE_CASE					)
           A_      =  sample.reshape(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  sample / num_elems
           A_      =  sample.permute(3       ,	0       ,	1       ,	2					)

           return sample

       def    __A			( self					)						->	Union[str, Any]:
           def model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					):
               return sample * t / (t + 1)

           return model

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	Tuple:
           A_      =  {
               '''num_train_timesteps''': 2000,
               '''snr''': 0.15,
               '''sigma_min''': 0.01,
               '''sigma_max''': 1348,
               '''sampling_eps''': 1E-5,
           }

           config.update(**_SCREAMING_SNAKE_CASE					)
           return config

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

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

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

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

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)
           kwargs.update(_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

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

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

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

       def    __A			( self					)						->	Any:
           for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Dict:
           for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,	[1, 100, 1000]					):
               self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE       ,	sigma_max=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	int:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  self.scheduler_classes[0]
           A_      =  self.get_scheduler_config()
           A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

           A_      =  3

           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter

           scheduler.set_sigmas(_SCREAMING_SNAKE_CASE					)
           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
           A_      =  torch.manual_seed(0					)

           for i, t in enumerate(scheduler.timesteps					):
               A_      =  scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                       A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               with torch.no_grad():
                   A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
               A_  ,A_      =  output.prev_sample, output.prev_sample_mean

           A_      =  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
           A_      =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

           assert np.isclose(result_sum.item()       ,	14_372_758_528.0					)
           assert np.isclose(result_mean.item()       ,	18_714_530.0					)

       def    __A			( self					)						->	List[Any]:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  kwargs.pop('''num_inference_steps'''       ,	_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               A_      =  self.dummy_sample
               A_      =  0.1 * sample

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

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	0       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	1       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

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






