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


UpperCAmelCase							=       "none"
UpperCAmelCase							=       "none"






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: str     ):
      snake_case_     :			Tuple       =       value.split(lowerCAmelCase_     )
      return len(lowerCAmelCase_     ) > 1 and any((len(lowerCAmelCase_     ) == 3 for x in split)     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int     ):


      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_     :			Tuple       =       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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: str     ):


      if answer is None:
            return None
      try:
            snake_case_     :			Optional[Any]       =       convert_to_float(lowerCAmelCase_     )
            if isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     ) and math.isnan(lowerCAmelCase_     ):
                  return None
            return value
      except ValueError:
            return answer.lower()


UpperCAmelCase							=       {
    "text": lambda x: x,
    "real": convert_to_float,
}



class 					snake_case__       (				enum.Enum							):
   _SCREAMING_SNAKE_CASE	: Any						    =      0
   _SCREAMING_SNAKE_CASE	: str						    =      1
   _SCREAMING_SNAKE_CASE	: int						    =      2
   _SCREAMING_SNAKE_CASE	: List[str]						    =      3
   _SCREAMING_SNAKE_CASE	: List[str]						    =      4
   _SCREAMING_SNAKE_CASE	: Tuple						    =      5



class 					snake_case__       (				enum.Enum							):
   _SCREAMING_SNAKE_CASE	: int						    =      0
   _SCREAMING_SNAKE_CASE	: Optional[int]						    =      1
   _SCREAMING_SNAKE_CASE	: Optional[Any]						    =      2



@dataclasses.dataclass
class 					snake_case__       :
   _SCREAMING_SNAKE_CASE	: Text
   _SCREAMING_SNAKE_CASE	: _Operator
   _SCREAMING_SNAKE_CASE	: Any


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






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






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: int ,	lowerCAmelCase_: 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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: Dict ,	lowerCAmelCase_: Any     ):
      snake_case_     :			Union[str, Any]       =       table["types"]
      return _TYPE_CONVERTER[types[column]](lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any]     ):
      return isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: Tuple ,	lowerCAmelCase_: Optional[int]     ):


      for cond in conditions:
            snake_case_     :			int       =       row[cond.column]

            snake_case_     :			Optional[Any]       =       _parse_value(lowerCAmelCase_ ,	cond.column ,	cond.cmp_value     )

            if _is_string(lowerCAmelCase_     ) and _is_string(lowerCAmelCase_     ):
                  snake_case_     :			List[str]       =       _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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Tuple     ):


      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_     :			Optional[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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Tuple ,	lowerCAmelCase_: Tuple     ):
      snake_case_     :			str       =       sql_query["agg"]
      if aggregation_op_index >= 3:
            snake_case_     :			int       =       _Aggregation(lowerCAmelCase_     )
      else:
            snake_case_     :			Tuple       =       _Aggregation.NONE

      snake_case_     :			Optional[int]       =       sql_query["sel"]
      snake_case_     :			int       =       [
          _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_     :			Dict       =       []
      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_     :			Optional[int]       =       {2: min, 1: max}
            snake_case_     :			Any       =       [(table["rows"][i][j], index) for index, (i, j) in enumerate(lowerCAmelCase_     )]
            snake_case_     :			Optional[int]       =       functools.reduce(operators[sql_query["agg"]] ,	lowerCAmelCase_     )

            snake_case_     :			int       =       [indices[reduced[1]]]
            return ret, _Aggregation.NONE

      return indices, aggregation_op






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: Tuple     ):


      if float_answer is not None:
            return [str(lowerCAmelCase_     )]
      return [str(table["real_rows"][r][c]     ) for r, c in answer_coordinates]






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




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 					snake_case__       (				unittest.TestCase							):



   @slow
   @unittest.skip("Model is not available."   )
   def 					UpperCAmelCase__       (				self   :   Dict   )							->     Optional[Any]:




         '''simple docstring'''




         snake_case_     :			str       =       "nvidia/megatron-gpt2-345m/"
         if "MYDIR" in os.environ:
               snake_case_     :			int       =       os.path.join(os.environ["MYDIR"]			,	A__   )
         snake_case_     :			str       =       GPTaLMHeadModel.from_pretrained(A__   )
         model.to(A__   )
         model.half()

         snake_case_     :			Optional[int]       =       torch.tensor(
             [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]			,	device=A__			,	dtype=torch.long			,	)

         with torch.no_grad():
               snake_case_     :			Union[str, Any]       =       model(A__   ).logits

         snake_case_     :			List[str]       =       torch.Size((1, 9, 5_02_57)   )
         self.assertEqual(output.shape			,	A__   )

         snake_case_     :			Dict       =       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=A__			,	dtype=torch.half			,	)

         for i in range(19   ):
               snake_case_  ,snake_case_     :			Dict       =       8 * i // 17, 27_92 * i  # along the diagonal
               snake_case_  ,snake_case_     :			Any       =       output[0, r, c], expected_diag[i]
               snake_case_     :			Dict       =       f"row={r} col={c} computed={computed} expected={expected}"
               self.assertAlmostEqual(A__			,	A__			,	delta=1E-4			,	msg=A__   )



