'''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


_A										= 'none'
_A										= 'none'

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[Any]  					=       value.split(SCREAMING_SNAKE_CASE_				)
     return len(SCREAMING_SNAKE_CASE_				) > 1 and any((len(SCREAMING_SNAKE_CASE_				) == 3 for x in split)				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     if isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          return value
     if isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          return float(SCREAMING_SNAKE_CASE_				)
     if not isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          raise ValueError('Argument value is not a string. Can\'t parse it as float'				)
     lowercase_       :							int  					=       value

     try:
          # Example: 1,000.7
          if "." in sanitized and "," in sanitized:
               return float(sanitized.replace(','	,							''				)				)
          # 1,000
          if "," in sanitized and _split_thousands(','	,							SCREAMING_SNAKE_CASE_				):
               return float(sanitized.replace(','	,							''				)				)
          # 5,5556
          if "," in sanitized and sanitized.count(','				) == 1 and not _split_thousands(','	,							SCREAMING_SNAKE_CASE_				):
               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(SCREAMING_SNAKE_CASE_				)
     except ValueError:
          # Avoid adding the sanitized value in the error message.
          raise ValueError('Unable to convert value to float'				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     if answer is None:
          return None
     try:
          lowercase_       :							Dict  					=       convert_to_float(SCREAMING_SNAKE_CASE_				)
          if isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				) and math.isnan(SCREAMING_SNAKE_CASE_				):
               return None
          return value
     except ValueError:
          return answer.lower()


_A										= {
    'text': lambda x: x,
    'real': convert_to_float,
}


class    UpperCAmelCase__	(							enum.Enum      ):






   """simple docstring"""
   A				:							Any			     =     0
   A				:							Optional[Any]			     =     1
   A				:							List[str]			     =     2
   A				:							List[Any]			     =     3
   A				:							str			     =     4
   A				:							str			     =     5



class    UpperCAmelCase__	(							enum.Enum      ):






   """simple docstring"""
   A				:							List[Any]			     =     0
   A				:							Dict			     =     1
   A				:							Tuple			     =     2



@dataclasses.dataclass
class    UpperCAmelCase__	:






   """simple docstring"""
   A				:							Text
   A				:							_Operator
   A				:							Any



_A										= re.compile(R'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return list(_TOKENIZER.findall(x.lower()				)				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[int]  					=       table['types']
     return _TYPE_CONVERTER[types[column]](SCREAMING_SNAKE_CASE_				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     for cond in conditions:
          lowercase_       :							List[Any]  					=       row[cond.column]

          lowercase_       :							Optional[int]  					=       _parse_value(SCREAMING_SNAKE_CASE_	,							cond.column	,							cond.cmp_value				)

          if _is_string(SCREAMING_SNAKE_CASE_				) and _is_string(SCREAMING_SNAKE_CASE_				):
               lowercase_       :							Tuple  					=       _normalize_for_match(SCREAMING_SNAKE_CASE_				)
               lowercase_       :							List[Any]  					=       _normalize_for_match(SCREAMING_SNAKE_CASE_				)

          if not isinstance(SCREAMING_SNAKE_CASE_	,							type(SCREAMING_SNAKE_CASE_				)				):
               raise ValueError('Type difference {} != {}'.format(type(SCREAMING_SNAKE_CASE_				)	,							type(SCREAMING_SNAKE_CASE_				)				)				)

          if not _compare(cond.operator	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
               return False
     return True

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     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(SCREAMING_SNAKE_CASE_				)				)

     # If we have just one answer, if float returns it or try a conversion.
     lowercase_       :							List[str]  					=       [table['rows'][i][j] for (i, j) in answer_coordinates]
     if len(SCREAMING_SNAKE_CASE_				) == 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(SCREAMING_SNAKE_CASE_	,							(int, float)				) for v in values)				):
          return None

     if aggregation_op == _Aggregation.SUM:
          return float(sum(SCREAMING_SNAKE_CASE_				)				)
     elif aggregation_op == _Aggregation.AVERAGE:
          return sum(SCREAMING_SNAKE_CASE_				) / len(SCREAMING_SNAKE_CASE_				)
     else:
          raise ValueError(f'''Unknown aggregation: {aggregation_op}'''				)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							str  					=       sql_query['agg']
     if aggregation_op_index >= 3:
          lowercase_       :							Optional[int]  					=       _Aggregation(SCREAMING_SNAKE_CASE_				)
     else:
          lowercase_       :							Union[str, Any]  					=       _Aggregation.NONE

     lowercase_       :							str  					=       sql_query['sel']
     lowercase_       :							Any  					=       [
         _Condition(SCREAMING_SNAKE_CASE_	,							_Operator(SCREAMING_SNAKE_CASE_				)	,							SCREAMING_SNAKE_CASE_				)
         for column, operator, cmp_value in zip(
             sql_query['conds']['column_index']	,							sql_query['conds']['operator_index']	,							sql_query['conds']['condition']				)
     ]

     lowercase_       :							Tuple  					=       []
     for row in range(len(table['rows']				)				):
          if _respect_conditions(SCREAMING_SNAKE_CASE_	,							table['rows'][row]	,							SCREAMING_SNAKE_CASE_				):
               indices.append((row, target_column)				)

     if not indices:
          return [], aggregation_op

     if len(SCREAMING_SNAKE_CASE_				) == 1:
          return indices, aggregation_op

     # Parsing of MIN/MAX.
     if aggregation_op_index in (1, 2):
          lowercase_       :							Any  					=       {2: min, 1: max}
          lowercase_       :							Optional[Any]  					=       [(table['rows'][i][j], index) for index, (i, j) in enumerate(SCREAMING_SNAKE_CASE_				)]
          lowercase_       :							int  					=       functools.reduce(operators[sql_query['agg']]	,							SCREAMING_SNAKE_CASE_				)

          lowercase_       :							str  					=       [indices[reduced[1]]]
          return ret, _Aggregation.NONE

     return indices, aggregation_op

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     if float_answer is not None:
          return [str(SCREAMING_SNAKE_CASE_				)]
     return [str(table['real_rows'][r][c]				) for r, c in answer_coordinates]






def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_					,lowercase_       :							Union[str, Any]  					=       _get_answer_coordinates(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[Any]  					=       _get_float_answer(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Optional[int]  					=       _get_answer_text(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)
     # keep the original data the same with TaPas
     if len(SCREAMING_SNAKE_CASE_				) == 0:
          lowercase_       :							Optional[Any]  					=       [EMPTY_ANSWER]
     return answer_text





'''simple docstring'''




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_A										= logging.get_logger(__name__)

_A										= ['UtteranceLevel']

@torch.no_grad()
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							List[str]  					=       torch.load(SCREAMING_SNAKE_CASE_	,							map_location='cpu'				)
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''				)

     lowercase_       :							Dict  					=       checkpoint['Downstream']

     lowercase_       :							List[Any]  					=       HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[Any]  					=       HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_	,							config=SCREAMING_SNAKE_CASE_				)
     lowercase_       :							List[str]  					=       WavaVecaFeatureExtractor.from_pretrained(
         SCREAMING_SNAKE_CASE_	,							return_attention_mask=SCREAMING_SNAKE_CASE_	,							do_normalize=SCREAMING_SNAKE_CASE_				)

     if hf_congfig.use_weighted_layer_sum:
          lowercase_       :							List[str]  					=       checkpoint['Featurizer']['weights']

     lowercase_       :							Tuple  					=       downstream_dict['projector.weight']
     lowercase_       :							List[Any]  					=       downstream_dict['projector.bias']
     lowercase_       :							List[Any]  					=       downstream_dict['model.post_net.linear.weight']
     lowercase_       :							str  					=       downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_				)
     hf_model.save_pretrained(SCREAMING_SNAKE_CASE_				)


if __name__ == "__main__":
 _A										= argparse.ArgumentParser()
 parser.add_argument(
     '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
 )
 parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
 parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
 _A										= parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




