code: 
  
  '''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							:     List[str]								=			collections.namedtuple(
      'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
  )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> CheckpointState:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         print(f"Reading saved model from {model_file}" )
         lowercase__       =				torch.load(A						,				map_location=lambda A						,				A : default_restore_location(A						,				'''cpu''' ) )
         return CheckpointState(**A )
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def __init__(self					:					Any     ,		UpperCamelCase					:					Path				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				src_file
  
  
  
        def    UpperCamelCase__	(self					:					List[str]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               raise NotImplementedError
  
  
  
  
  
  
        @staticmethod
        def    UpperCamelCase__	(UpperCamelCase					:					str     ,		*UpperCamelCase					:					Dict     ,		**UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if comp_type.startswith('''c'''				):
                      return DPRContextEncoderState(*UpperCamelCase     ,		**UpperCamelCase				)
               if comp_type.startswith('''q'''				):
                      return DPRQuestionEncoderState(*UpperCamelCase     ,		**UpperCamelCase				)
               if comp_type.startswith('''r'''				):
                      return DPRReaderState(*UpperCamelCase     ,		**UpperCamelCase				)
               else:
                      raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''				)
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR biencoder from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               lowercase__    ,lowercase__       =				model.ctx_encoder, '''ctx_model.'''
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{'''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(UpperCamelCase				):
                             lowercase__       =				key[len(UpperCamelCase				) :]
                             if not key.startswith('''encode_proj.'''				):
                                    lowercase__       =				'''bert_model.''' + key
                             lowercase__       =				value
               encoder.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR biencoder from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               lowercase__    ,lowercase__       =				model.question_encoder, '''question_model.'''
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{'''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(UpperCamelCase				):
                             lowercase__       =				key[len(UpperCamelCase				) :]
                             if not key.startswith('''encode_proj.'''				):
                                    lowercase__       =				'''bert_model.''' + key
                             lowercase__       =				value
               encoder.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''				)[0]				)				)
               print(f"Loading DPR reader from {self.src_file}"				)
               lowercase__       =				load_states_from_checkpoint(self.src_file				)
               # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
               lowercase__       =				{
                   '''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__       =				'''encoder.bert_model.''' + key[len('''encoder.'''				) :]
                      lowercase__       =				value
               model.span_predictor.load_state_dict(UpperCamelCase				)
               return model
  
  
  
  
  
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				Path(A )
         dest_dir.mkdir(exist_ok=A )
  
         lowercase__       =				DPRState.from_type(A						,				src_file=A )
         lowercase__       =				dpr_state.load_dpr_model()
         model.save_pretrained(A )
         model.from_pretrained(A )  # sanity check
  
  
  if __name__ == "__main__":
   lowerCamelCase							:     List[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							:     Optional[int]								=			parser.parse_args()
  
   lowerCamelCase							:     List[str]								=			Path(args.src)
   lowerCamelCase							:     List[Any]								=			f"""converted-{src_file.name}""" if args.dest is None else args.dest
   lowerCamelCase							:     Optional[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)
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import os
  from shutil import copyfile
  from typing import TYPE_CHECKING, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  from ...utils.versions import require_version
  
  
  if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation
  
  require_version('tokenizers>=0.13.3')
  
  if is_sentencepiece_available():
   from .tokenization_llama import LlamaTokenizer
  else:
   lowerCamelCase							:     Optional[int]								=			None
  
  lowerCamelCase							:     List[Any]								=			logging.get_logger(__name__)
  lowerCamelCase							:     Dict								=			{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCamelCase		,						lowerCamelCase							:     str								=			'[INST]', '[/INST]'
  lowerCamelCase		,						lowerCamelCase							:     Optional[Any]								=			'<<SYS>>\n', '\n<</SYS>>\n\n'
  
  # fmt: off
  lowerCamelCase							:     Optional[int]								=			'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
        lowerCAmelCase__      :  List[str]						      =							LlamaTokenizer
        lowerCAmelCase__      :  Tuple						      =							"""left"""
        lowerCAmelCase__      :  int						      =							["""input_ids""", """attention_mask"""]
  
  
  
        def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					Tuple=None     ,		UpperCamelCase					:					Optional[int]=None     ,		UpperCamelCase					:					List[str]=False     ,		UpperCamelCase					:					Dict="<unk>"     ,		UpperCamelCase					:					int="<s>"     ,		UpperCamelCase					:					Dict="</s>"     ,		UpperCamelCase					:					Any=True     ,		UpperCamelCase					:					Tuple=False     ,		**UpperCamelCase					:					List[str]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(
                   vocab_file=UpperCamelCase     ,		tokenizer_file=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
               lowercase__       =				add_bos_token
               lowercase__       =				add_eos_token
               self.update_post_processor()
  
               lowercase__       =				vocab_file
               lowercase__       =				False if not self.vocab_file else True
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.bos_token
               lowercase__       =				self.bos_token_id
  
               lowercase__       =				self.eos_token
               lowercase__       =				self.eos_token_id
  
               lowercase__       =				f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
               lowercase__       =				f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"
  
               lowercase__       =				[]
               if self.add_bos_token:
                      special_tokens.append((bos, bos_token_id)				)
               if self.add_eos_token:
                      special_tokens.append((eos, eos_token_id)				)
               lowercase__       =				processors.TemplateProcessing(
                   single=UpperCamelCase     ,		pair=UpperCamelCase     ,		special_tokens=UpperCamelCase				)
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_eos_token
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_bos_token
  
  
  
        @add_eos_token.setter
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        @add_bos_token.setter
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[str] = None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if not self.can_save_slow_tokenizer:
                      raise ValueError(
                          '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                          '''tokenizer.'''				)
  
               if not os.path.isdir(UpperCamelCase				):
                      logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                      return
               lowercase__       =				os.path.join(
                   UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
  
               if os.path.abspath(self.vocab_file				) != os.path.abspath(UpperCamelCase				):
                      copyfile(self.vocab_file     ,		UpperCamelCase				)
  
               return (out_vocab_file,)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					"Conversation"				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				list(conversation.iter_texts()				)
               if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
                   [not is_user for is_user, msg in dialogue[1::2]]				):
                      raise ValueError(
                          '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)
  
               lowercase__       =				[]
               if len(conversation.past_user_inputs				) > 0:
                      if not conversation.past_user_inputs[0].startswith(UpperCamelCase				) or E_SYS not in conversation.past_user_inputs[0]:
                             lowercase__       =				(
                                 B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                             )
               elif not dialogue[0][1].startswith(UpperCamelCase				) or E_SYS not in dialogue[0][1]:
                      lowercase__       =				(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])
  
               dialog_tokens += sum(
                   [
                       [self.bos_token_id]
                       + self.encode(
                           f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "     ,		add_special_tokens=UpperCamelCase				)
                       + [self.eos_token_id]
                       for prompt, answer in zip(dialogue[::2]     ,		dialogue[1::2]				)
                   ]     ,		[]     ,		)
               if not (dialogue[-1][0]):
                      raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"				)
               dialog_tokens += [self.bos_token_id] + self.encode(
                   f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"     ,		add_special_tokens=UpperCamelCase				)
               return dialog_tokens
  
  
  
  
  