



'''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			:   Optional[int]			=      """none"""
A			:   int			=      """none"""
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							List[str]						  =value.split(lowerCamelCase_							)
   return len(lowerCamelCase_							) > 1 and any((len(lowerCamelCase_							) == 3 for x in split)							)
def 		_a   (  lowerCamelCase_							):
   if isinstance(lowerCamelCase_					,						lowerCamelCase_							):
      return value
   if isinstance(lowerCamelCase_					,						lowerCamelCase_							):
      return float(lowerCamelCase_							)
   if not isinstance(lowerCamelCase_					,						lowerCamelCase_							):
      raise ValueError('''Argument value is not a string. Can\'t parse it as float'''							)
   snake_case   :							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(''','''					,						lowerCamelCase_							):
         return float(sanitized.replace(''','''					,						''''''							)							)
      # 5,5556
      if "," in sanitized and sanitized.count(''','''							) == 1 and not _split_thousands(''','''					,						lowerCamelCase_							):
         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(lowerCamelCase_							)
   except ValueError:
      # Avoid adding the sanitized value in the error message.
      raise ValueError('''Unable to convert value to float'''							)
def 		_a   (  lowerCamelCase_							):
   if answer is None:
      return None
   try:
      snake_case   :							Dict						  =convert_to_float(lowerCamelCase_							)
      if isinstance(lowerCamelCase_					,						lowerCamelCase_							) and math.isnan(lowerCamelCase_							):
         return None
      return value
   except ValueError:
      return answer.lower()


A			:   Any			=      {
    """text""": lambda x: x,
    """real""": convert_to_float,
}





class 							lowerCAmelCase_		(	enum.Enum						):
    __UpperCAmelCase							=	0
    __UpperCAmelCase							=	1
    __UpperCAmelCase							=	2
    __UpperCAmelCase							=	3
    __UpperCAmelCase							=	4
    __UpperCAmelCase							=	5





class 							lowerCAmelCase_		(	enum.Enum						):
    __UpperCAmelCase							=	0
    __UpperCAmelCase							=	1
    __UpperCAmelCase							=	2





@dataclasses.dataclass
class 							lowerCAmelCase_		:
    __UpperCAmelCase							=	42
    __UpperCAmelCase							=	42
    __UpperCAmelCase							=	42




A			:   Optional[int]			=      re.compile(r"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)
def 		_a   (  lowerCamelCase_							):
   return list(_TOKENIZER.findall(x.lower()							)							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   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 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							Optional[int]						  =table['''types''']
   return _TYPE_CONVERTER[types[column]](lowerCamelCase_							)
def 		_a   (  lowerCamelCase_							):
   return isinstance(lowerCamelCase_					,						lowerCamelCase_							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   for cond in conditions:
      snake_case   :							Any						  =row[cond.column]

      snake_case   :							List[str]						  =_parse_value(lowerCamelCase_					,						cond.column					,						cond.cmp_value							)

      if _is_string(lowerCamelCase_							) and _is_string(lowerCamelCase_							):
         snake_case   :							Union[str, Any]						  =_normalize_for_match(lowerCamelCase_							)
         snake_case   :							Tuple						  =_normalize_for_match(lowerCamelCase_							)

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

      if not _compare(cond.operator					,						lowerCamelCase_					,						lowerCamelCase_							):
         return False
   return True
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   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(lowerCamelCase_							)							)

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

   if aggregation_op == _Aggregation.SUM:
      return float(sum(lowerCamelCase_							)							)
   elif aggregation_op == _Aggregation.AVERAGE:
      return sum(lowerCamelCase_							) / len(lowerCamelCase_							)
   else:
      raise ValueError(F'''Unknown aggregation: {aggregation_op}'''							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							Any						  =sql_query['''agg''']
   if aggregation_op_index >= 3:
      snake_case   :							Dict						  =_Aggregation(lowerCamelCase_							)
   else:
      snake_case   :							int						  =_Aggregation.NONE

   snake_case   :							Dict						  =sql_query['''sel''']
   snake_case   :							Any						  =[
       _Condition(lowerCamelCase_					,						_Operator(lowerCamelCase_							)					,						lowerCamelCase_							)
       for column, operator, cmp_value in zip(
           sql_query['''conds''']['''column_index''']					,						sql_query['''conds''']['''operator_index''']					,						sql_query['''conds''']['''condition''']							)
   ]

   snake_case   :							Union[str, Any]						  =[]
   for row in range(len(table['''rows''']							)							):
      if _respect_conditions(lowerCamelCase_					,						table['''rows'''][row]					,						lowerCamelCase_							):
         indices.append((row, target_column)							)

   if not indices:
      return [], aggregation_op

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

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

      snake_case   :							Dict						  =[indices[reduced[1]]]
      return ret, _Aggregation.NONE

   return indices, aggregation_op
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   if float_answer is not None:
      return [str(lowerCamelCase_							)]
   return [str(table['''real_rows'''][r][c]							) for r, c in answer_coordinates]


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










'''simple docstring'''





import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      import torch

      from transformers import GPTaLMHeadModel





@require_torch
@require_sentencepiece
@require_tokenizers
class 							lowerCAmelCase_		(	unittest.TestCase						):





    @slow
    @unittest.skip('''Model is not available.'''     )
    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							Any						  ='''nvidia/megatron-gpt2-345m/'''
       if "MYDIR" in os.environ:
          snake_case   :							Any						  =os.path.join(os.environ['''MYDIR'''],    _snake_case     )
       snake_case   :							Optional[Any]						  =GPTaLMHeadModel.from_pretrained(_snake_case     )
       model.to(_snake_case     )
       model.half()

       snake_case   :							int						  =torch.tensor(
           [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]],    device=_snake_case,    dtype=torch.long,    )

       with torch.no_grad():
          snake_case   :							Dict						  =model(_snake_case     ).logits

       snake_case   :							Dict						  =torch.Size((1, 9, 50_257)     )
       self.assertEqual(output.shape,    _snake_case     )

       snake_case   :							str						  =torch.tensor(
           [
               4.9414,
               -0.2920,
               -1.2148,
               -4.0273,
               -0.5161,
               -5.2109,
               -1.2412,
               -1.8301,
               -1.7734,
               -4.7148,
               -0.2317,
               -1.0811,
               -2.1777,
               0.4141,
               -3.7969,
               -4.0586,
               -2.5332,
               -3.3809,
               4.3867,
           ],    device=_snake_case,    dtype=torch.half,    )

       for i in range(19     ):
          snake_case  ,    snake_case   :							Tuple						  =8 * i // 17, 2_792 * i  # along the diagonal
          snake_case  ,    snake_case   :							List[str]						  =output[0, r, c], expected_diag[i]
          snake_case   :							Optional[int]						  =f'''row={r} col={c} computed={computed} expected={expected}'''
          self.assertAlmostEqual(_snake_case,    _snake_case,    delta=1E-4,    msg=_snake_case     )





