



"""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				:     Dict								=  collections.namedtuple(
    """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
)


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  print(F'''Reading saved model from {model_file}'''    )
  _SCREAMING_SNAKE_CASE        =			torch.load(UpperCamelCase__       ,							map_location=lambda UpperCamelCase__       ,							UpperCamelCase__    : default_restore_location(UpperCamelCase__       ,							'''cpu'''    )    )
  return CheckpointState(**UpperCamelCase__    )




class    __snake_case:




    def __init__(   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			src_file




    def   A   (   self			):





      '''simple docstring'''



      raise NotImplementedError




    @staticmethod
    def   A   (   A_    ,							*A_    ,							**A_			):





      '''simple docstring'''



      if comp_type.startswith('''c'''			):
        return DPRContextEncoderState(*A_    ,							**A_			)
      if comp_type.startswith('''q'''			):
        return DPRQuestionEncoderState(*A_    ,							**A_			)
      if comp_type.startswith('''r'''			):
        return DPRReaderState(*A_    ,							**A_			)
      else:
        raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''			)




class    __snake_case(					__A       ):




    def   A   (   self			):





      '''simple docstring'''



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




class    __snake_case(					__A       ):




    def   A   (   self			):





      '''simple docstring'''



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




class    __snake_case(					__A       ):




    def   A   (   self			):





      '''simple docstring'''



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



def 							A__ (     UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__    ):


  '''simple docstring'''


  _SCREAMING_SNAKE_CASE        =			Path(UpperCamelCase__    )
  dest_dir.mkdir(exist_ok=UpperCamelCase__    )

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


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

    lowerCamelCase				:     Optional[int]								=  Path(args.src)
    lowerCamelCase				:     Tuple								=  F'''converted-{src_file.name}''' if args.dest is None else args.dest
    lowerCamelCase				:     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 unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class    __snake_case(					unittest.TestCase       ):




    @property
    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			UNetaDModel(
          block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=3    ,							out_channels=3    ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,							)
      return model




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''cpu'''
      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler()

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''    ,							return_dict=A_			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array(
          [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.dummy_uncond_unet
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler(prediction_type='''sample'''			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							num_inference_steps=2    ,							output_type='''numpy'''			)[0]

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]
      _SCREAMING_SNAKE_CASE        =			image_eps[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			1e-2 if torch_device != '''mps''' else 3e-2
      assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class    __snake_case(					unittest.TestCase       ):




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			'''google/ddpm-cifar10-32'''

      _SCREAMING_SNAKE_CASE        =			UNetaDModel.from_pretrained(A_			)
      _SCREAMING_SNAKE_CASE        =			DDPMScheduler.from_pretrained(A_			)

      _SCREAMING_SNAKE_CASE        =			DDPMPipeline(unet=A_    ,							scheduler=A_			)
      ddpm.to(A_			)
      ddpm.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			ddpm(generator=A_    ,							output_type='''numpy'''			).images

      _SCREAMING_SNAKE_CASE        =			image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _SCREAMING_SNAKE_CASE        =			np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
