





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 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      List[str]           =						value.split(__a )
	return len(__a ) > 1 and any((len(__a ) == 3 for x in split) )


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	if isinstance(__a ,			__a ):
		return value
	if isinstance(__a ,			__a ):
		return float(__a )
	if not isinstance(__a ,			__a ):
		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(',' ,			__a ):
			return float(sanitized.replace(',' ,			'' ) )
		# 5,5556
		if "," in sanitized and sanitized.count(',' ) == 1 and not _split_thousands(',' ,			__a ):
			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(__a )
	except ValueError:
		# Avoid adding the sanitized value in the error message.
		raise ValueError('Unable to convert value to float' )


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	if answer is None:
		return None
	try:
		snake_case_		:      str           =						convert_to_float(__a )
		if isinstance(__a ,			__a ) and math.isnan(__a ):
			return None
		return value
	except ValueError:
		return answer.lower()


_SCREAMING_SNAKE_CASE   						=		{
    """text""": lambda x: x,
    """real""": convert_to_float,
}
class SCREAMING_SNAKE_CASE_    (       enum.Enum       ):
						__magic_name__:      Union[str, Any]			   =							0
						__magic_name__:      Optional[int]			   =							1
						__magic_name__:      List[str]			   =							2
						__magic_name__:      Union[str, Any]			   =							3
						__magic_name__:      Tuple			   =							4
						__magic_name__:      Optional[Any]			   =							5
class SCREAMING_SNAKE_CASE_    (       enum.Enum       ):
						__magic_name__:      List[str]			   =							0
						__magic_name__:      Any			   =							1
						__magic_name__:      Optional[Any]			   =							2
@dataclasses.dataclass
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      Text
						__magic_name__:      _Operator
						__magic_name__:      Any
_SCREAMING_SNAKE_CASE   						=		re.compile(R"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	return list(_TOKENIZER.findall(x.lower() ) )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	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__    (     __a ,			__a ,			__a ):
	snake_case_		:      List[str]           =						table['types']
	return _TYPE_CONVERTER[types[column]](__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	return isinstance(__a ,			__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	for cond in conditions:
		snake_case_		:      List[Any]           =						row[cond.column]

		snake_case_		:      Dict           =						_parse_value(__a ,			cond.column ,			cond.cmp_value )

		if _is_string(__a ) and _is_string(__a ):
			snake_case_		:      Optional[int]           =						_normalize_for_match(__a )
			snake_case_		:      Tuple           =						_normalize_for_match(__a )

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

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


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	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(__a ) )

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

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


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      Dict           =						sql_query['agg']
	if aggregation_op_index >= 3:
		snake_case_		:      List[Any]           =						_Aggregation(__a )
	else:
		snake_case_		:      int           =						_Aggregation.NONE

	snake_case_		:      Dict           =						sql_query['sel']
	snake_case_		:      Optional[int]           =						[
	    _Condition(__a ,			_Operator(__a ) ,			__a )
	    for column, operator, cmp_value in zip(
	        sql_query['conds']['column_index'] ,			sql_query['conds']['operator_index'] ,			sql_query['conds']['condition'] )
	]

	snake_case_		:      List[str]           =						[]
	for row in range(len(table['rows'] ) ):
		if _respect_conditions(__a ,			table['rows'][row] ,			__a ):
			indices.append((row, target_column) )

	if not indices:
		return [], aggregation_op

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

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

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

	return indices, aggregation_op


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	if float_answer is not None:
		return [str(__a )]
	return [str(table['real_rows'][r][c] ) for r, c in answer_coordinates]


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_    ,snake_case_		:      Dict           =						_get_answer_coordinates(__a ,			__a )
	snake_case_		:      Any           =						_get_float_answer(__a ,			__a ,			__a )
	snake_case_		:      Any           =						_get_answer_text(__a ,			__a ,			__a )
	# keep the original data the same with TaPas
	if len(__a ) == 0:
		snake_case_		:      Optional[Any]           =						[EMPTY_ANSWER]
	return answer_text


'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase       :     Dict			=    logging.get_logger(__name__)

UpperCamelCase       :     Tuple			=    {
    """flaubert/flaubert_small_cased""": """https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json""",
    """flaubert/flaubert_base_uncased""": """https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json""",
    """flaubert/flaubert_base_cased""": """https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json""",
    """flaubert/flaubert_large_cased""": """https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json""",
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    int				=			"flaubert"
    A     :    List[str]				=			{
        "hidden_size": "emb_dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
        "n_words": "vocab_size",  # For backward compatibility
    }





    def __init__(					self     :						str							,							UpperCAmelCase_     :						Optional[int]=False							,							UpperCAmelCase_     :						List[Any]=0.0							,							UpperCAmelCase_     :						List[Any]=3_0_1_4_5							,							UpperCAmelCase_     :						Union[str, Any]=2_0_4_8							,							UpperCAmelCase_     :						Tuple=1_2							,							UpperCAmelCase_     :						str=1_6							,							UpperCAmelCase_     :						Tuple=0.1							,							UpperCAmelCase_     :						str=0.1							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Union[str, Any]=False							,							UpperCAmelCase_     :						Any=False							,							UpperCAmelCase_     :						str=False							,							UpperCAmelCase_     :						Tuple=1							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						Tuple=5_1_2							,							UpperCAmelCase_     :						List[str]=2_0_4_8**-0.5							,							UpperCAmelCase_     :						List[Any]=1e-12							,							UpperCAmelCase_     :						Tuple=0.02							,							UpperCAmelCase_     :						Any=0							,							UpperCAmelCase_     :						List[Any]=1							,							UpperCAmelCase_     :						str=2							,							UpperCAmelCase_     :						Optional[Any]=3							,							UpperCAmelCase_     :						List[str]=5							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						List[Any]="first"							,							UpperCAmelCase_     :						Any=True							,							UpperCAmelCase_     :						Union[str, Any]=None							,							UpperCAmelCase_     :						Union[str, Any]=True							,							UpperCAmelCase_     :						Any=0.1							,							UpperCAmelCase_     :						List[Any]=5							,							UpperCAmelCase_     :						List[Any]=5							,							UpperCAmelCase_     :						Union[str, Any]=0							,							UpperCAmelCase_     :						Optional[Any]=0							,							UpperCAmelCase_     :						List[Any]=2							,							UpperCAmelCase_     :						List[Any]=0							,							**UpperCAmelCase_     :						List[Any]							,							):


         """simple docstring"""







         a					:							Any							= pre_norm
         a					:							str							= layerdrop
         a					:							int							= vocab_size
         a					:							str							= emb_dim
         a					:							Any							= n_layers
         a					:							Dict							= n_heads
         a					:							Union[str, Any]							= dropout
         a					:							List[Any]							= attention_dropout
         a					:							List[str]							= gelu_activation
         a					:							int							= sinusoidal_embeddings
         a					:							Union[str, Any]							= causal
         a					:							str							= asm
         a					:							Tuple							= n_langs
         a					:							Optional[Any]							= use_lang_emb
         a					:							str							= layer_norm_eps
         a					:							Optional[Any]							= bos_index
         a					:							Union[str, Any]							= eos_index
         a					:							str							= pad_index
         a					:							List[str]							= unk_index
         a					:							Dict							= mask_index
         a					:							Optional[Any]							= is_encoder
         a					:							str							= max_position_embeddings
         a					:							Optional[Any]							= embed_init_std
         a					:							str							= init_std
         a					:							Union[str, Any]							= summary_type
         a					:							Tuple							= summary_use_proj
         a					:							Tuple							= summary_activation
         a					:							str							= summary_proj_to_labels
         a					:							List[str]							= summary_first_dropout
         a					:							Any							= start_n_top
         a					:							Optional[Any]							= end_n_top
         a					:							List[Any]							= mask_token_id
         a					:							Optional[Any]							= lang_id

         if "n_words" in kwargs:
              a					:							int							= kwargs['n_words']

         super().__init__(pad_token_id=UpperCAmelCase_							,							bos_token_id=UpperCAmelCase_							,							**UpperCAmelCase_)






class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    @property
    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         if self.task == "multiple-choice":
              a					:							int							= {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              a					:							Tuple							= {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ])

