"""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


_UpperCamelCase         =		"none"
_UpperCamelCase         =		"none"

def    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							A__					:    Optional[Any]														= 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"""       )
							A__					:    List[Any]														= 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:
														A__					:    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 	__UpperCAmelCase						(enum.Enum    ):







			'''simple docstring'''
			_UpperCamelCase     :	Union[str, Any]				    =							0
			_UpperCamelCase     :	List[Any]				    =							1
			_UpperCamelCase     :	Optional[int]				    =							2
			_UpperCamelCase     :	Optional[Any]				    =							3
			_UpperCamelCase     :	Dict				    =							4
			_UpperCamelCase     :	Any				    =							5





class 	__UpperCAmelCase						(enum.Enum    ):







			'''simple docstring'''
			_UpperCamelCase     :	Union[str, Any]				    =							0
			_UpperCamelCase     :	List[str]				    =							1
			_UpperCamelCase     :	List[str]				    =							2





@dataclasses.dataclass
class 	__UpperCAmelCase						:







			'''simple docstring'''
			_UpperCamelCase     :	Text
			_UpperCamelCase     :	_Operator
			_UpperCamelCase     :	Any





_UpperCamelCase         =		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       ):
							A__					:    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:
														A__					:    List[Any]														= row[cond.column]

														A__					:    List[Any]														= _parse_value(lowerCAmelCase ,     cond.column ,     cond.cmp_value       )

														if _is_string(lowerCAmelCase       ) and _is_string(lowerCAmelCase       ):
																					A__					:    Dict														= _normalize_for_match(lowerCAmelCase       )
																					A__					:    str														= _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.
							A__					:    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    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							A__					:    Any														= sql_query["""agg"""]
							if aggregation_op_index >= 3:
														A__					:    Any														= _Aggregation(lowerCAmelCase       )
							else:
														A__					:    List[str]														= _Aggregation.NONE

							A__					:    Union[str, Any]														= sql_query["""sel"""]
							A__					:    List[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"""]       )
							]

							A__					:    Optional[int]														= []
							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):
														A__					:    int														= {2: min, 1: max}
														A__					:    List[Any]														= [(table["""rows"""][i][j], index) for index, (i, j) in enumerate(lowerCAmelCase       )]
														A__					:    Tuple														= functools.reduce(operators[sql_query["""agg"""]] ,     lowerCAmelCase       )

														A__					:    Tuple														= [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       ):
							A__							,       A__					:    Tuple														= _get_answer_coordinates(lowerCAmelCase ,     lowerCAmelCase       )
							A__					:    Dict														= _get_float_answer(lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase       )
							A__					:    Optional[Any]														= _get_answer_text(lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase       )
							# keep the original data the same with TaPas
							if len(lowerCAmelCase       ) == 0:
														A__					:    Optional[Any]														= [EMPTY_ANSWER]
							return answer_text






"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase         =		logging.get_logger(__name__)

_UpperCamelCase         =		{
    "facebook/opt-125m": "https://huggingface.co/facebook/opt-125m/blob/main/config.json",
    "facebook/opt-350m": "https://huggingface.co/facebook/opt-350m/blob/main/config.json",
    "facebook/opt-1.3b": "https://huggingface.co/facebook/opt-1.3b/blob/main/config.json",
    "facebook/opt-2.7b": "https://huggingface.co/facebook/opt-2.7b/blob/main/config.json",
    "facebook/opt-6.7b": "https://huggingface.co/facebook/opt-6.7b/blob/main/config.json",
    "facebook/opt-13b": "https://huggingface.co/facebook/opt-13b/blob/main/config.json",
}
class 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Dict				    =							'opt'
			_UpperCamelCase     :	Optional[Any]				    =							['past_key_values']





			def __init__( self      ,			snake_case_=50_272      ,			snake_case_=768      ,			snake_case_=12      ,			snake_case_=3_072      ,			snake_case_=2_048      ,			snake_case_=True      ,			snake_case_=False      ,			snake_case_=None      ,			snake_case_=0.1      ,			snake_case_=0.0      ,			snake_case_=12      ,			snake_case_="relu"      ,			snake_case_=0.0      ,			snake_case_=0.02      ,			snake_case_=True      ,			snake_case_=1      ,			snake_case_=2      ,			snake_case_=2      ,			snake_case_=True      ,			snake_case_=True      ,			**snake_case_      ,			):







										'''simple docstring'''


										super().__init__(
										    pad_token_id=snake_case_      ,			bos_token_id=snake_case_      ,			eos_token_id=snake_case_      ,			**snake_case_      ,			)
										A__					:    int														= vocab_size
										A__					:    List[str]														= max_position_embeddings
										A__					:    str														= num_attention_heads
										A__					:    Dict														= word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
										A__					:    Optional[int]														= ffn_dim
										A__					:    Optional[int]														= hidden_size
										A__					:    int														= num_hidden_layers
										A__					:    str														= dropout
										A__					:    Tuple														= attention_dropout
										A__					:    Optional[Any]														= activation_function
										A__					:    Any														= init_std
										A__					:    Optional[int]														= layerdrop
										A__					:    Optional[int]														= use_cache
										A__					:    int														= do_layer_norm_before
										# We keep these variables at `True` for backward compatibility.
										A__					:    str														= enable_bias
										A__					:    List[str]														= layer_norm_elementwise_affine

										# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
										# with checkpoints that have been fine-tuned before transformers v4.20.1
										# see https://github.com/facebookresearch/metaseq/pull/164
										A__					:    str														= _remove_final_layer_norm





