




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__				:     Union[str, Any]			       =							'none'
lowerCamelCase__				:     List[str]			       =							'none'

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			str							)      ->				List[str]:
      SCREAMING_SNAKE_CASE_					=    value.split(__UpperCAmelCase							)
      return len(__UpperCAmelCase							) > 1 and any((len(__UpperCAmelCase							) == 3 for x in split)							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Any]							)      ->				Optional[int]:
      if isinstance(__UpperCAmelCase    ,    __UpperCAmelCase							):
            return value
      if isinstance(__UpperCAmelCase    ,    __UpperCAmelCase							):
            return float(__UpperCAmelCase							)
      if not isinstance(__UpperCAmelCase    ,    __UpperCAmelCase							):
            raise ValueError('Argument value is not a string. Can\'t parse it as float'							)
      SCREAMING_SNAKE_CASE_					=    value

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

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]							)      ->				Optional[Any]:
      if answer is None:
            return None
      try:
            SCREAMING_SNAKE_CASE_					=    convert_to_float(__UpperCAmelCase							)
            if isinstance(__UpperCAmelCase    ,    __UpperCAmelCase							) and math.isnan(__UpperCAmelCase							):
                  return None
            return value
      except ValueError:
            return answer.lower()


lowerCamelCase__				:     Union[str, Any]			       =							{
    'text': lambda x: x,
    'real': convert_to_float,
}





class  lowerCamelCase_							(   enum.Enum      ):


 '''simple docstring'''







 lowercase_       					=					0
 lowercase_       					=					1
 lowercase_       					=					2
 lowercase_       					=					3
 lowercase_       					=					4
 lowercase_       					=					5





class  lowerCamelCase_							(   enum.Enum      ):


 '''simple docstring'''







 lowercase_       					=					0
 lowercase_       					=					1
 lowercase_       					=					2





@dataclasses.dataclass
class  lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       					=					42
 lowercase_       					=					42
 lowercase_       					=					42


lowerCamelCase__				:     Any			       =							re.compile(r'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Tuple							)      ->				Optional[Any]:
      return list(_TOKENIZER.findall(x.lower()							)							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[int]    ,    __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			Union[str, Any]							)      ->				Optional[int]:
      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_							(   __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			List[str]							)      ->				int:
      SCREAMING_SNAKE_CASE_					=    table['types']
      return _TYPE_CONVERTER[types[column]](__UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[int]							)      ->				List[Any]:
      return isinstance(__UpperCAmelCase    ,    __UpperCAmelCase							)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			int    ,    __UpperCAmelCase		:			int							)      ->				int:
      for cond in conditions:
            SCREAMING_SNAKE_CASE_					=    row[cond.column]

            SCREAMING_SNAKE_CASE_					=    _parse_value(__UpperCAmelCase    ,    cond.column    ,    cond.cmp_value							)

            if _is_string(__UpperCAmelCase							) and _is_string(__UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_					=    _normalize_for_match(__UpperCAmelCase							)
                  SCREAMING_SNAKE_CASE_					=    _normalize_for_match(__UpperCAmelCase							)

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

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

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int    ,    __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			Dict							)      ->				str:
      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(__UpperCAmelCase							)							)

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

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

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			Optional[int]							)      ->				int:
      SCREAMING_SNAKE_CASE_					=    sql_query['agg']
      if aggregation_op_index >= 3:
            SCREAMING_SNAKE_CASE_					=    _Aggregation(__UpperCAmelCase							)
      else:
            SCREAMING_SNAKE_CASE_					=    _Aggregation.NONE

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

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

      if not indices:
            return [], aggregation_op

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

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

            SCREAMING_SNAKE_CASE_					=    [indices[reduced[1]]]
            return ret, _Aggregation.NONE

      return indices, aggregation_op

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			List[Any]							)      ->				Union[str, Any]:
      if float_answer is not None:
            return [str(__UpperCAmelCase							)]
      return [str(table['real_rows'][r][c]							) for r, c in answer_coordinates]





def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Any]    ,    __UpperCAmelCase		:			Optional[Any]							)      ->				List:
      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    _get_answer_coordinates(__UpperCAmelCase    ,    __UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    _get_float_answer(__UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    _get_answer_text(__UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase							)
      # keep the original data the same with TaPas
      if len(__UpperCAmelCase							) == 0:
            SCREAMING_SNAKE_CASE_					=    [EMPTY_ANSWER]
      return answer_text





import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowerCamelCase__				:     Optional[Any]			       =							get_logger(__name__)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				List[str]:
      if text.startswith(__UpperCAmelCase							):
            return text[len(__UpperCAmelCase							) :]
      return text  # or whatever

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]							)      ->				List[Any]:
      return {remove_prefix(__UpperCAmelCase    ,    'model.'							): v for k, v in sd.items()}

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Dict[str, torch.Tensor]]							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    {}
      for k in state_dicts[0].keys():
            SCREAMING_SNAKE_CASE_					=    [sd[k] for sd in state_dicts]
            SCREAMING_SNAKE_CASE_					=    sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)
            assert isinstance(__UpperCAmelCase    ,    torch.Tensor							)
            SCREAMING_SNAKE_CASE_					=    new_t
      return new_sd

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				None:
      SCREAMING_SNAKE_CASE_					=    AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase							)
      if os.path.isfile(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    [pl_ckpt_path]
      else:
            assert os.path.isdir(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    list(Path(__UpperCAmelCase							).glob('*.ckpt'							)							)
            assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

      if len(__UpperCAmelCase							) > 1:
            logger.info(f"averaging the weights of {ckpt_files}"							)

      SCREAMING_SNAKE_CASE_					=    [sanitize(torch.load(__UpperCAmelCase    ,    map_location='cpu'							)['state_dict']							) for x in ckpt_files]
      SCREAMING_SNAKE_CASE_					=    average_state_dicts(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    hf_model.load_state_dict(__UpperCAmelCase    ,    strict=__UpperCAmelCase							)
      assert not missing, f"missing keys: {missing}"
      hf_model.save_pretrained(__UpperCAmelCase							)
      try:
            SCREAMING_SNAKE_CASE_					=    AutoTokenizer.from_pretrained(__UpperCAmelCase							)
            tok.save_pretrained(__UpperCAmelCase							)
      except Exception:
            pass
            # dont copy tokenizer if cant


if __name__ == "__main__":
   fire.Fire(convert_pl_to_hf)