code: 
  
  
  '''simple docstring'''
  
  import dataclasses
  import enum
  import functools
  import math
  import re
  
  # The following script is adapted from the script of TaPas.
  # Original: https://github.com/google-research/tapas/master/wikisql_utils.py
  from typing import Any, List, Text
  
  
  lowerCamelCase							:     Any								=			'none'
  lowerCamelCase							:     List[str]								=			'none'
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				value.split(A )
         return len(A ) > 1 and any((len(A ) == 3 for x in split) )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> Dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if isinstance(A						,				A ):
                return value
         if isinstance(A						,				A ):
                return float(A )
         if not isinstance(A						,				A ):
                raise ValueError('''Argument value is not a string. Can\'t parse it as float''' )
         lowercase__       =				value
  
         try:
                # Example: 1,000.7
                if "." in sanitized and "," in sanitized:
                       return float(sanitized.replace(''','''						,				'''''' ) )
                # 1,000
                if "," in sanitized and _split_thousands(''','''						,				A ):
                       return float(sanitized.replace(''','''						,				'''''' ) )
                # 5,5556
                if "," in sanitized and sanitized.count(''',''' ) == 1 and not _split_thousands(''','''						,				A ):
                       return float(sanitized.replace(''','''						,				'''.''' ) )
                # 0.0.0.1
                if sanitized.count('''.''' ) > 1:
                       return float(sanitized.replace('''.'''						,				'''''' ) )
                # 0,0,0,1
                if sanitized.count(''',''' ) > 1:
                       return float(sanitized.replace(''','''						,				'''''' ) )
                return float(A )
         except ValueError:
                # Avoid adding the sanitized value in the error message.
                raise ValueError('''Unable to convert value to float''' )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> Union[str, Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if answer is None:
                return None
         try:
                lowercase__       =				convert_to_float(A )
                if isinstance(A						,				A ) and math.isnan(A ):
                       return None
                return value
         except ValueError:
                return answer.lower()
  
  
  lowerCamelCase							:     List[Any]								=			{
      'text': lambda x: x,
      'real': convert_to_float,
  }
  class 						__lowerCAmelCase		(enum.Enum  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							0
        lowerCAmelCase__      :  Dict						      =							1
        lowerCAmelCase__      :  Dict						      =							2
        lowerCAmelCase__      :  str						      =							3
        lowerCAmelCase__      :  Optional[Any]						      =							4
        lowerCAmelCase__      :  List[str]						      =							5
  
  
  
  
  
  
  class 						__lowerCAmelCase		(enum.Enum  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  Dict						      =							0
        lowerCAmelCase__      :  Union[str, Any]						      =							1
        lowerCAmelCase__      :  Tuple						      =							2
  
  
  
  
  
  
  @dataclasses.dataclass
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  Text
        lowerCAmelCase__      :  _Operator
        lowerCAmelCase__      :  Any
  
  
  
  
  
  
  lowerCamelCase							:     Union[str, Any]								=			re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)
  
  def _SCREAMING_SNAKE_CASE   (A ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return list(_TOKENIZER.findall(x.lower() ) )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if operator == _Operator.EQUALS:
                return src == tgt
         elif operator == _Operator.GREATER:
                return src > tgt
         elif operator == _Operator.LESSER:
                return src < tgt
         raise ValueError(f"Unknown operator: {operator}" )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Dict:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				table['''types''']
         return _TYPE_CONVERTER[types[column]](A )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         return isinstance(A						,				A )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Any:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         for cond in conditions:
                lowercase__       =				row[cond.column]
  
                lowercase__       =				_parse_value(A						,				cond.column						,				cond.cmp_value )
  
                if _is_string(A ) and _is_string(A ):
                       lowercase__       =				_normalize_for_match(A )
                       lowercase__       =				_normalize_for_match(A )
  
                if not isinstance(A						,				type(A ) ):
                       raise ValueError('''Type difference {} != {}'''.format(type(A )						,				type(A ) ) )
  
                if not _compare(cond.operator						,				A						,				A ):
                       return False
         return True
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if not answer_coordinates:
                if aggregation_op == _Aggregation.COUNT:
                       return 0.0
                else:
                       return EMPTY_ANSWER_AGG
  
      # Count can support non numeric answers.
         if aggregation_op == _Aggregation.COUNT:
                return float(len(A ) )
  
         # If we have just one answer, if float returns it or try a conversion.
         lowercase__       =				[table['''rows'''][i][j] for (i, j) in answer_coordinates]
         if len(A ) == 1:
                try:
                       return convert_to_float(values[0] )
                except ValueError as e:
                       if aggregation_op != _Aggregation.NONE:
                              raise e
  
         if aggregation_op == _Aggregation.NONE:
                return None
  
         # Other aggregation only support numeric values. Bail out if we have strings.
         if not all((isinstance(A						,				(int, float) ) for v in values) ):
                return None
  
         if aggregation_op == _Aggregation.SUM:
                return float(sum(A ) )
         elif aggregation_op == _Aggregation.AVERAGE:
                return sum(A ) / len(A )
         else:
                raise ValueError(f"Unknown aggregation: {aggregation_op}" )
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				sql_query['''agg''']
         if aggregation_op_index >= 3:
                lowercase__       =				_Aggregation(A )
         else:
                lowercase__       =				_Aggregation.NONE
  
         lowercase__       =				sql_query['''sel''']
         lowercase__       =				[
             _Condition(A						,				_Operator(A )						,				A )
             for column, operator, cmp_value in zip(
                 sql_query['''conds''']['''column_index''']						,				sql_query['''conds''']['''operator_index''']						,				sql_query['''conds''']['''condition'''] )
         ]
  
         lowercase__       =				[]
         for row in range(len(table['''rows'''] ) ):
                if _respect_conditions(A						,				table['''rows'''][row]						,				A ):
                       indices.append((row, target_column) )
  
         if not indices:
                return [], aggregation_op
  
         if len(A ) == 1:
                return indices, aggregation_op
  
         # Parsing of MIN/MAX.
         if aggregation_op_index in (1, 2):
                lowercase__       =				{2: min, 1: max}
                lowercase__       =				[(table['''rows'''][i][j], index) for index, (i, j) in enumerate(A )]
                lowercase__       =				functools.reduce(operators[sql_query['''agg''']]						,				A )
  
                lowercase__       =				[indices[reduced[1]]]
                return ret, _Aggregation.NONE
  
         return indices, aggregation_op
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if float_answer is not None:
                return [str(A )]
         return [str(table['''real_rows'''][r][c] ) for r, c in answer_coordinates]
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> List:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__    ,lowercase__       =				_get_answer_coordinates(A						,				A )
         lowercase__       =				_get_float_answer(A						,				A						,				A )
         lowercase__       =				_get_answer_text(A						,				A						,				A )
         # keep the original data the same with TaPas
         if len(A ) == 0:
                lowercase__       =				[EMPTY_ANSWER]
         return answer_text
  
  
  
  
  
style_context: 
  
  
  '''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)
  
  
  
  
  