


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



def 			_a      (    __UpperCamelCase			):
     print(F'''Reading saved model from {model_file}'''			)
     a_     :  str				      =     torch.load(__UpperCamelCase	,	map_location=lambda __UpperCamelCase	,	__UpperCamelCase			: default_restore_location(__UpperCamelCase	,	"""cpu"""			)			)
     return CheckpointState(**__UpperCamelCase			)


class 	a__						:

       def __init__( self   :  int   ,				lowerCamelCase_   :  Path     ):
            a_     :  List[Any]				      =     src_file

       def     UpperCAmelCase( self   :  List[str]     ):
            raise NotImplementedError

       @staticmethod
       def     UpperCAmelCase( lowerCamelCase_   :  str   ,				*lowerCamelCase_   :  Optional[Any]   ,				**lowerCamelCase_   :  Any     ):
            if comp_type.startswith("""c"""     ):
                 return DPRContextEncoderState(*lowerCamelCase_   ,				**lowerCamelCase_     )
            if comp_type.startswith("""q"""     ):
                 return DPRQuestionEncoderState(*lowerCamelCase_   ,				**lowerCamelCase_     )
            if comp_type.startswith("""r"""     ):
                 return DPRReaderState(*lowerCamelCase_   ,				**lowerCamelCase_     )
            else:
                 raise ValueError("""Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."""     )


class 	a__						(    lowerCAmelCase_     ):

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


class 	a__						(    lowerCAmelCase_     ):

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


class 	a__						(    lowerCAmelCase_     ):

       def     UpperCAmelCase( self   :  Union[str, Any]     ):
            a_     :  str				      =     DPRReader(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""     )[0]     )     )
            print(F'''Loading DPR reader from {self.src_file}'''     )
            a_     :  Tuple				      =     load_states_from_checkpoint(self.src_file     )
            # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
            a_     :  Any				      =     {
                """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_     :  str				      =     """encoder.bert_model.""" + key[len("""encoder."""     ) :]
                 a_     :  int				      =     value
            model.span_predictor.load_state_dict(lowerCamelCase_     )
            return model



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Optional[Any]				      =     Path(__UpperCamelCase			)
     dest_dir.mkdir(exist_ok=__UpperCamelCase			)

     a_     :  str				      =     DPRState.from_type(__UpperCamelCase	,	src_file=__UpperCamelCase			)
     a_     :  Optional[Any]				      =     dpr_state.load_dpr_model()
     model.save_pretrained(__UpperCamelCase			)
     model.from_pretrained(__UpperCamelCase			)  # sanity check


if __name__ == "__main__":
       __lowerCamelCase   	=  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   	=  parser.parse_args()

       __lowerCamelCase   	=  Path(args.src)
       __lowerCamelCase   	=  f'''converted-{src_file.name}''' if args.dest is None else args.dest
       __lowerCamelCase   	=  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 typing import Mapping

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


a__			:						Dict							=			logging.get_logger(__name__)

a__			:						Any							=			{
    """google/long-t5-local-base""": """https://huggingface.co/google/long-t5-local-base/blob/main/config.json""",
    """google/long-t5-local-large""": """https://huggingface.co/google/long-t5-local-large/blob/main/config.json""",
    """google/long-t5-tglobal-base""": """https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json""",
    """google/long-t5-tglobal-large""": """https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json""",
}


class       _snake_case  (    UpperCamelCase							):
						__snake_case			      =  'longt5'
						__snake_case			      =  ['past_key_values']
						__snake_case			      =  {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(self  ,				UpperCAmelCase_=3_2_1_2_8  ,				UpperCAmelCase_=5_1_2  ,				UpperCAmelCase_=6_4  ,				UpperCAmelCase_=2_0_4_8  ,				UpperCAmelCase_=6  ,				UpperCAmelCase_=None  ,				UpperCAmelCase_=8  ,				UpperCAmelCase_=1_2_7  ,				UpperCAmelCase_=1_6  ,				UpperCAmelCase_=3_2  ,				UpperCAmelCase_=1_2_8  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=1e-6  ,				UpperCAmelCase_=1.0  ,				UpperCAmelCase_="relu"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_="local"  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=0  ,				UpperCAmelCase_=1  ,				**UpperCAmelCase_  ,				)      ->    int:
											__lowercase        =      vocab_size
											__lowercase        =      d_model
											__lowercase        =      d_kv
											__lowercase        =      d_ff
											__lowercase        =      num_layers
											# default = symmetry
											__lowercase        =      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											__lowercase        =      num_heads
											__lowercase        =      local_radius
											__lowercase        =      global_block_size
											__lowercase        =      relative_attention_num_buckets
											__lowercase        =      relative_attention_max_distance
											__lowercase        =      dropout_rate
											__lowercase        =      layer_norm_epsilon
											__lowercase        =      initializer_factor
											__lowercase        =      feed_forward_proj
											__lowercase        =      encoder_attention_type
											__lowercase        =      use_cache

											__lowercase        =      self.feed_forward_proj.split('''-'''				)
											__lowercase        =      act_info[-1]
											__lowercase        =      act_info[0] == '''gated'''

											if len(UpperCAmelCase_				) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_				) > 2:
																raise ValueError(
																    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
																    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																    '''\'gated-gelu\' or \'relu\''''				)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																__lowercase        =      '''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase_  ,				eos_token_id=UpperCAmelCase_  ,				is_encoder_decoder=UpperCAmelCase_  ,				**UpperCAmelCase_  ,				)



class       _snake_case  (    UpperCamelCase							):







						@property
						def 							_UpperCamelCase     (self				)      ->    Mapping[str, Mapping[int, str]]:
											__lowercase        =      {
											    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
											    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
											}
											if self.use_past:
																__lowercase        =      '''past_encoder_sequence + sequence'''
																__lowercase        =      {0: '''batch'''}
																__lowercase        =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}
																__lowercase        =      {0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
																self.fill_with_past_key_values_(UpperCAmelCase_  ,				direction='''inputs'''				)

											return common_inputs







						@property
						def 							_UpperCamelCase     (self				)      ->    int:
											return 1_3


