


"""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_     :     Any    		=						collections.namedtuple(
    """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
)
def _A		(		lowercase	):







    """simple docstring"""
    print(f'''Reading saved model from {model_file}'''	)
    a					=torch.load(lowercase ,					map_location=lambda lowercase ,					lowercase	: default_restore_location(lowercase ,					'''cpu'''	)	)
    return CheckpointState(**lowercase	)






class    __A       :






 """simple docstring"""




 def __init__(	self				,					__A		)      ->  Tuple:
     a					=src_file


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, Any]:
     raise NotImplementedError





 @staticmethod
 def    SCREAMING_SNAKE_CASE							(	__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    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
     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(__A		):
             a					=key[len(__A		) :]
             if not key.startswith('''encode_proj.'''		):
                 a					='''bert_model.''' + key
             a					=value
     encoder.load_state_dict(__A		)
     return model






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 def    SCREAMING_SNAKE_CASE							(	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(__A		):
             a					=key[len(__A		) :]
             if not key.startswith('''encode_proj.'''		):
                 a					='''bert_model.''' + key
             a					=value
     encoder.load_state_dict(__A		)
     return model






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
     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(__A		)
     return model

def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=Path(lowercase	)
    dest_dir.mkdir(exist_ok=lowercase	)

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


if __name__ == "__main__":
       lowerCamelCase_     :     Optional[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_     :     List[Any]    		=						parser.parse_args()

       lowerCamelCase_     :     List[Any]    		=						Path(args.src)
       lowerCamelCase_     :     Union[str, Any]    		=						F'converted-{src_file.name}' if args.dest is None else args.dest
       lowerCamelCase_     :     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)









"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)

lowerCamelCase_     :     Tuple    		=						{
    """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
}






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"mobilevitv2"


 def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
     super().__init__(**__A		)

     a					=num_channels
     a					=image_size
     a					=patch_size
     a					=expand_ratio
     a					=hidden_act
     a					=conv_kernel_size
     a					=output_stride
     a					=initializer_range
     a					=layer_norm_eps
     a					=n_attn_blocks
     a					=base_attn_unit_dims
     a					=width_multiplier
     a					=ffn_multiplier
     a					=ffn_dropout
     a					=attn_dropout
     a					=classifier_dropout_prob

     # decode head attributes for semantic segmentation
     a					=aspp_out_channels
     a					=atrous_rates
     a					=aspp_dropout_prob
     a					=semantic_loss_ignore_index






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						version.parse("1.11"    )


 @property
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
     return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)


 @property
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
     if self.task == "image-classification":
         return OrderedDict([('''logits''', {0: '''batch'''})]		)
     else:
         return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]		)





 @property
 def    SCREAMING_SNAKE_CASE							(	self		)      ->  float:
     return 1E-4