


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'''

import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

A:   List[str]						=					logging.get_logger(__name__)

A:   List[Any]						=					{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

A:   Tuple						=					{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

A:   Optional[int]						=					{
    "microsoft/deberta-base": 5_1_2,
    "microsoft/deberta-large": 5_1_2,
    "microsoft/deberta-xlarge": 5_1_2,
    "microsoft/deberta-base-mnli": 5_1_2,
    "microsoft/deberta-large-mnli": 5_1_2,
    "microsoft/deberta-xlarge-mnli": 5_1_2,
}

A:   Any						=					{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : Any   	=      (
						    list(range(ord('!'				)    ,      ord('~'				) + 1				)				) + list(range(ord('¡'				)    ,      ord('¬'				) + 1				)				) + list(range(ord('®'				)    ,      ord('ÿ'				) + 1				)				)
						)
						lowercase_   : Union[str, Any]   	=      bs[:]
						lowercase_   : Dict   	=      0
						for b in range(2**8				):
												if b not in bs:
																		bs.append(a				)
																		cs.append(2**8 + n				)
																		n += 1
						lowercase_   : List[str]   	=      [chr(a				) for n in cs]
						return dict(zip(a    ,      a				)				)






def 						_UpperCAmelCase     (	a		:						Optional[int]				)      ->      Union[str, Any]:





						"""simple docstring"""
						lowercase_   : str   	=      set()
						lowercase_   : Optional[int]   	=      word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)				)
												lowercase_   : str   	=      char
						return pairs






class 				__magic_name__  (						UpperCAmelCase_					):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_       :   List[str]   =	VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_       :   Tuple   =	PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_       :   Dict   =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_       :   Dict   =	['input_ids', 'attention_mask', 'token_type_ids']
	def __init__(				self	,   _lowercase	,   _lowercase	,   _lowercase="replace"	,   _lowercase="[CLS]"	,   _lowercase="[SEP]"	,   _lowercase="[SEP]"	,   _lowercase="[CLS]"	,   _lowercase="[UNK]"	,   _lowercase="[PAD]"	,   _lowercase="[MASK]"	,   _lowercase=False	,   _lowercase=False	,   **_lowercase	,   )    ->	Union[str, Any]:
							lowercase_   : List[str]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else bos_token
							lowercase_   : str   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else eos_token
							lowercase_   : Tuple   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else sep_token
							lowercase_   : List[Any]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else cls_token
							lowercase_   : Any   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else unk_token
							lowercase_   : Any   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_   : Union[str, Any]   	=      AddedToken(_lowercase	,   lstrip=_lowercase	,   rstrip=_lowercase							) if isinstance(_lowercase	,   _lowercase							) else mask_token

							super().__init__(
							    errors=_lowercase	,   bos_token=_lowercase	,   eos_token=_lowercase	,   unk_token=_lowercase	,   sep_token=_lowercase	,   cls_token=_lowercase	,   pad_token=_lowercase	,   mask_token=_lowercase	,   add_prefix_space=_lowercase	,   add_bos_token=_lowercase	,   **_lowercase	,   )
							lowercase_   : str   	=      add_bos_token

							with open(_lowercase	,   encoding='utf-8'							) as vocab_handle:
													lowercase_   : Union[str, Any]   	=      json.load(_lowercase							)
							lowercase_   : List[Any]   	=      {v: k for k, v in self.encoder.items()}
							lowercase_   : int   	=      errors  # how to handle errors in decoding
							lowercase_   : str   	=      bytes_to_unicode()
							lowercase_   : List[Any]   	=      {v: k for k, v in self.byte_encoder.items()}
							with open(_lowercase	,   encoding='utf-8'							) as merges_handle:
													lowercase_   : Tuple   	=      merges_handle.read().split('\n'							)[1:-1]
							lowercase_   : List[str]   	=      [tuple(merge.split()							) for merge in bpe_merges]
							lowercase_   : Dict   	=      dict(zip(_lowercase	,   range(len(_lowercase							)							)							)							)
							lowercase_   : Any   	=      {}
							lowercase_   : str   	=      add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							lowercase_   : Tuple   	=      re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'							)
	@property
	# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							return len(self.encoder							)
	def 							lowerCamelCase__		(				self							)    ->	Optional[Any]:
							return dict(self.encoder	,   **self.added_tokens_encoder							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							if token in self.cache:
													return self.cache[token]
							lowercase_   : Any   	=      tuple(_lowercase							)
							lowercase_   : Union[str, Any]   	=      get_pairs(_lowercase							)

							if not pairs:
													return token

							while True:
													lowercase_   : int   	=      min(_lowercase	,   key=lambda _lowercase							: self.bpe_ranks.get(_lowercase	,   float('inf'							)							)							)
													if bigram not in self.bpe_ranks:
																			break
													lowercase_	,   lowercase_   : Any   	=      bigram
													lowercase_   : Dict   	=      []
													lowercase_   : Tuple   	=      0
													while i < len(_lowercase							):
																			try:
																									lowercase_   : str   	=      word.index(_lowercase	,   _lowercase							)
																			except ValueError:
																									new_word.extend(word[i:]							)
																									break
																			else:
																									new_word.extend(word[i:j]							)
																									lowercase_   : str   	=      j

																			if word[i] == first and i < len(_lowercase							) - 1 and word[i + 1] == second:
																									new_word.append(first + second							)
																									i += 2
																			else:
																									new_word.append(word[i]							)
																									i += 1
													lowercase_   : Optional[int]   	=      tuple(_lowercase							)
													lowercase_   : Tuple   	=      new_word
													if len(_lowercase							) == 1:
																			break
													else:
																			lowercase_   : Optional[Any]   	=      get_pairs(_lowercase							)
							lowercase_   : Tuple   	=      ' '.join(_lowercase							)
							lowercase_   : int   	=      word
							return word
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	List[int]:
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_   : Union[str, Any]   	=      [self.cls_token_id]
							lowercase_   : Optional[Any]   	=      [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None	,   _lowercase = False							)    ->	List[int]:
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowercase	,   token_ids_a=_lowercase	,   already_has_special_tokens=_lowercase							)

							if token_ids_a is None:
													return [1] + ([0] * len(_lowercase							)) + [1]
							return [1] + ([0] * len(_lowercase							)) + [1] + ([0] * len(_lowercase							)) + [1]
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	List[int]:
							lowercase_   : Optional[Any]   	=      [self.sep_token_id]
							lowercase_   : Optional[int]   	=      [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep							) * [0]
							return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[Any]:
							lowercase_   : Optional[int]   	=      []
							for token in re.findall(self.pat	,   _lowercase							):
													lowercase_   : List[str]   	=      ''.join(
													    self.byte_encoder[b] for b in token.encode('utf-8'							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowercase							).split(' '							)							)
							return bpe_tokens
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[str]:
							return self.encoder.get(_lowercase	,   self.encoder.get(self.unk_token							)							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							return self.decoder.get(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	int:
							lowercase_   : List[str]   	=      ''.join(_lowercase							)
							lowercase_   : List[str]   	=      bytearray([self.byte_decoder[c] for c in text]							).decode('utf-8'	,   errors=self.errors							)
							return text
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	Tuple[str]:
							if not os.path.isdir(_lowercase							):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
													return
							lowercase_   : Any   	=      os.path.join(
							    _lowercase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
							lowercase_   : Optional[Any]   	=      os.path.join(
							    _lowercase	,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']							)

							with open(_lowercase	,   'w'	,   encoding='utf-8'							) as f:
													f.write(json.dumps(self.encoder	,   indent=2	,   sort_keys=_lowercase	,   ensure_ascii=_lowercase							) + '\n'							)

							lowercase_   : List[str]   	=      0
							with open(_lowercase	,   'w'	,   encoding='utf-8'							) as writer:
													writer.write('#version: 0.2\n'							)
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,   key=lambda _lowercase							: kv[1]							):
																			if index != token_index:
																									logger.warning(
																									    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																									    ' Please check that the tokenizer is not corrupted!'							)
																									lowercase_   : Any   	=      token_index
																			writer.write(' '.join(_lowercase							) + '\n'							)
																			index += 1

							return vocab_file, merge_file
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase=False	,   **_lowercase							)    ->	Union[str, Any]:
							lowercase_   : List[Any]   	=      kwargs.pop('add_prefix_space'	,   self.add_prefix_space							)
							if (is_split_into_words or add_prefix_space) and (len(_lowercase							) > 0 and not text[0].isspace()):
													lowercase_   : Dict   	=      ' ' + text
							return (text, kwargs)







	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	List[int]:
							lowercase_   : Optional[int]   	=      []
							for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(_lowercase	,   add_special_tokens=_lowercase							) + [self.eos_token_id]							)
							if len(_lowercase							) > self.model_max_length:
													lowercase_   : Optional[int]   	=      input_ids[-self.model_max_length :]
							return input_ids
