'''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


_A										= collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     print(f'''Reading saved model from {model_file}'''				)
     lowercase_       :							int  					=       torch.load(SCREAMING_SNAKE_CASE_	,							map_location=lambda SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				: default_restore_location(SCREAMING_SNAKE_CASE_	,							'cpu'				)				)
     return CheckpointState(**SCREAMING_SNAKE_CASE_				)


class    UpperCAmelCase__	:






   """simple docstring"""
   def __init__(self							,					_a					)     ->  int:
        lowercase_       :							Any  					=       src_file
   def 		_lowerCamelCase			(self					)     ->  Any:
        raise NotImplementedError





   @staticmethod
   def 		_lowerCamelCase			(_a							,					*_a							,					**_a					)     ->  "DPRState":
        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    UpperCAmelCase__	(							_snake_case      ):






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



class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self					)     ->  Union[str, Any]:
        lowercase_       :							Optional[int]  					=       DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'					)[0]					)					)
        print(f'''Loading DPR biencoder from {self.src_file}'''					)
        lowercase_       :							Tuple  					=       load_states_from_checkpoint(self.src_file					)
        lowercase_					,lowercase_       :							Dict  					=       model.question_encoder, 'question_model.'
        # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
        lowercase_       :							Optional[Any]  					=       {'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					):
                  lowercase_       :							int  					=       key[len(_a					) :]
                  if not key.startswith('encode_proj.'					):
                       lowercase_       :							int  					=       'bert_model.' + key
                  lowercase_       :							List[Any]  					=       value
        encoder.load_state_dict(_a					)
        return model



class    UpperCAmelCase__	(							_snake_case      ):






   """simple docstring"""
   def 		_lowerCamelCase			(self					)     ->  Any:
        lowercase_       :							int  					=       DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'					)[0]					)					)
        print(f'''Loading DPR reader from {self.src_file}'''					)
        lowercase_       :							List[str]  					=       load_states_from_checkpoint(self.src_file					)
        # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
        lowercase_       :							Optional[int]  					=       {
            '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_       :							List[Any]  					=       'encoder.bert_model.' + key[len('encoder.'					) :]
             lowercase_       :							List[Any]  					=       value
        model.span_predictor.load_state_dict(_a					)
        return model



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							str  					=       Path(SCREAMING_SNAKE_CASE_				)
     dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_				)

     lowercase_       :							Union[str, Any]  					=       DPRState.from_type(SCREAMING_SNAKE_CASE_	,							src_file=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Tuple  					=       dpr_state.load_dpr_model()
     model.save_pretrained(SCREAMING_SNAKE_CASE_				)
     model.from_pretrained(SCREAMING_SNAKE_CASE_				)  # sanity check


if __name__ == "__main__":
 _A										= 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.')
 _A										= parser.parse_args()

 _A										= Path(args.src)
 _A										= F"""converted-{src_file.name}""" if args.dest is None else args.dest
 _A										= 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 os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_A										= get_logger(__name__)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     if version.parse(SCREAMING_SNAKE_CASE_				) < version.parse(SCREAMING_SNAKE_CASE_				):
          if "dev" in min_version:
               lowercase_       :							Tuple  					=       (
                   'This example requires a source install from HuggingFace diffusers (see '
                   '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
               )
          else:
               lowercase_       :							List[Any]  					=       f'''This example requires a minimum version of {min_version},'''
          error_message += f''' but the version found is {__version__}.\n'''
          raise ImportError(SCREAMING_SNAKE_CASE_				)




