





'''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


_SCREAMING_SNAKE_CASE			=							"none"
_SCREAMING_SNAKE_CASE			=							"none"


def 		__a(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Dict      ):



    '''simple docstring'''





    _lowerCAmelCase		     = value.split(SCREAMING_SNAKE_CASE_      )
    return len(SCREAMING_SNAKE_CASE_      ) > 1 and any((len(SCREAMING_SNAKE_CASE_      ) == 3 for x in split)      )


def 		__a(SCREAMING_SNAKE_CASE_					:   Any      ):



    '''simple docstring'''





    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"      )
    _lowerCAmelCase		     = 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 		__a(SCREAMING_SNAKE_CASE_					:   Any      ):



    '''simple docstring'''





    if answer is None:
        return None
    try:
        _lowerCAmelCase		     = 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()


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

class 				lowerCAmelCase_						(						enum.Enum						):
 __lowerCamelCase  :							int    					=   0
 __lowerCamelCase  :							Optional[Any]    					=   1
 __lowerCamelCase  :							Dict    					=   2
 __lowerCamelCase  :							Tuple    					=   3
 __lowerCamelCase  :							List[str]    					=   4
 __lowerCamelCase  :							Tuple    					=   5







class 				lowerCAmelCase_						(						enum.Enum						):
 __lowerCamelCase  :							Optional[Any]    					=   0
 __lowerCamelCase  :							Optional[int]    					=   1
 __lowerCamelCase  :							str    					=   2







@dataclasses.dataclass
class 				lowerCAmelCase_						:
 __lowerCamelCase  :							Text
 __lowerCamelCase  :							_Operator
 __lowerCamelCase  :							Any







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


def 		__a(SCREAMING_SNAKE_CASE_					:   Union[str, Any]      ):



    '''simple docstring'''





    return list(_TOKENIZER.findall(x.lower()      )      )


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[int]      ):



    '''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 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   Optional[int]						,    SCREAMING_SNAKE_CASE_					:   Any      ):



    '''simple docstring'''





    _lowerCAmelCase		     = table["types"]
    return _TYPE_CONVERTER[types[column]](SCREAMING_SNAKE_CASE_      )


def 		__a(SCREAMING_SNAKE_CASE_					:   Union[str, Any]      ):



    '''simple docstring'''





    return isinstance(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )


def 		__a(SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Tuple      ):



    '''simple docstring'''





    for cond in conditions:
        _lowerCAmelCase		     = row[cond.column]

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

        if _is_string(SCREAMING_SNAKE_CASE_      ) and _is_string(SCREAMING_SNAKE_CASE_      ):
            _lowerCAmelCase		     = _normalize_for_match(SCREAMING_SNAKE_CASE_      )
            _lowerCAmelCase		     = _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 		__a(SCREAMING_SNAKE_CASE_					:   Optional[int]						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[int]      ):



    '''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(SCREAMING_SNAKE_CASE_      )      )

    # If we have just one answer, if float returns it or try a conversion.
    _lowerCAmelCase		     = [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 		__a(SCREAMING_SNAKE_CASE_					:   Optional[Any]						,    SCREAMING_SNAKE_CASE_					:   Any      ):



    '''simple docstring'''





    _lowerCAmelCase		     = sql_query["agg"]
    if aggregation_op_index >= 3:
        _lowerCAmelCase		     = _Aggregation(SCREAMING_SNAKE_CASE_      )
    else:
        _lowerCAmelCase		     = _Aggregation.NONE

    _lowerCAmelCase		     = sql_query["sel"]
    _lowerCAmelCase		     = [
        _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"]      )
    ]

    _lowerCAmelCase		     = []
    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):
        _lowerCAmelCase		     = {2: min, 1: max}
        _lowerCAmelCase		     = [(table["rows"][i][j], index) for index, (i, j) in enumerate(SCREAMING_SNAKE_CASE_      )]
        _lowerCAmelCase		     = functools.reduce(operators[sql_query["agg"]]						,    SCREAMING_SNAKE_CASE_      )

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

    return indices, aggregation_op


def 		__a(SCREAMING_SNAKE_CASE_					:   Dict						,    SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    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 		__a(SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    _lowerCAmelCase     ,     _lowerCAmelCase		     = _get_answer_coordinates(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = _get_float_answer(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )
    _lowerCAmelCase		     = _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:
        _lowerCAmelCase		     = [EMPTY_ANSWER]
    return answer_text









'''simple docstring'''


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						__magic_name__						):
 __lowerCamelCase  :							str    					=   "mt5"
 __lowerCamelCase  :							Dict    					=   ["past_key_values"]




 def __init__(		self     ,       _lowerCAmelCase=250112     ,       _lowerCAmelCase=512     ,       _lowerCAmelCase=64     ,       _lowerCAmelCase=1024     ,       _lowerCAmelCase=8     ,       _lowerCAmelCase=None     ,       _lowerCAmelCase=6     ,       _lowerCAmelCase=32     ,       _lowerCAmelCase=128     ,       _lowerCAmelCase=0.1     ,       _lowerCAmelCase=1E-6     ,       _lowerCAmelCase=1.0     ,       _lowerCAmelCase="gated-gelu"     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase=True     ,       _lowerCAmelCase="T5Tokenizer"     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase=0     ,       _lowerCAmelCase=1     ,       _lowerCAmelCase=0     ,       **_lowerCAmelCase     ,       )      ->							Optional[Any]:
     super().__init__(
         is_encoder_decoder=_lowerCAmelCase     ,       tokenizer_class=_lowerCAmelCase     ,       tie_word_embeddings=_lowerCAmelCase     ,       pad_token_id=_lowerCAmelCase     ,       eos_token_id=_lowerCAmelCase     ,       decoder_start_token_id=_lowerCAmelCase     ,       **_lowerCAmelCase     ,       )
     _lowerCAmelCase		     = vocab_size
     _lowerCAmelCase		     = d_model
     _lowerCAmelCase		     = d_kv
     _lowerCAmelCase		     = d_ff
     _lowerCAmelCase		     = num_layers
     _lowerCAmelCase		     = (
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     _lowerCAmelCase		     = num_heads
     _lowerCAmelCase		     = relative_attention_num_buckets
     _lowerCAmelCase		     = relative_attention_max_distance
     _lowerCAmelCase		     = dropout_rate
     _lowerCAmelCase		     = layer_norm_epsilon
     _lowerCAmelCase		     = initializer_factor
     _lowerCAmelCase		     = feed_forward_proj
     _lowerCAmelCase		     = use_cache

     _lowerCAmelCase		     = self.feed_forward_proj.split("-"						)
     _lowerCAmelCase		     = act_info[-1]
     _lowerCAmelCase		     = act_info[0] == "gated"

     if len(_lowerCAmelCase						) > 1 and act_info[0] != "gated" or len(_lowerCAmelCase						) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
             "'gated-gelu' or 'relu'"						)

     # for backwards compatibility
     if feed_forward_proj == "gated-gelu":
         _lowerCAmelCase		     = "gelu_new"




 @property
 def 							_snake_case				(		self						)      ->							Any:
     return self.d_model




 @property
 def 							_snake_case				(		self						)      ->							Optional[Any]:
     return self.num_heads





 @property
 def 							_snake_case				(		self						)      ->							List[str]:
     return self.num_layers







class 				lowerCAmelCase_						(						__magic_name__						):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 							_snake_case				(		self						)      ->							Mapping[str, Mapping[int, str]]:
     _lowerCAmelCase		     = {
         "input_ids": {0: "batch", 1: "encoder_sequence"},
         "attention_mask": {0: "batch", 1: "encoder_sequence"},
     }
     if self.use_past:
         _lowerCAmelCase		     = "past_encoder_sequence + sequence"
         _lowerCAmelCase		     = {0: "batch"}
         _lowerCAmelCase		     = {0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}
         _lowerCAmelCase		     = {0: "batch", 1: "decoder_sequence"}

     if self.use_past:
         self.fill_with_past_key_values_(_lowerCAmelCase     ,       direction="inputs"						)

     return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 							_snake_case				(		self						)      ->							int:
     return 13





 @property
 def 							_snake_case				(		self						)      ->							float:
     return 5E-4


