

'''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)








'''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




