






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__     :      Any             =			{
    'configuration_gpt2': ['GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPT2Config', 'GPT2OnnxConfig'],
    'tokenization_gpt2': ['GPT2Tokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      List[Any]             =			['GPT2TokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPT2DoubleHeadsModel',
							    'GPT2ForQuestionAnswering',
							    'GPT2ForSequenceClassification',
							    'GPT2ForTokenClassification',
							    'GPT2LMHeadModel',
							    'GPT2Model',
							    'GPT2PreTrainedModel',
							    'load_tf_weights_in_gpt2',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Union[str, Any]             =			[
							    'TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFGPT2DoubleHeadsModel',
							    'TFGPT2ForSequenceClassification',
							    'TFGPT2LMHeadModel',
							    'TFGPT2MainLayer',
							    'TFGPT2Model',
							    'TFGPT2PreTrainedModel',
							]

try:
							if not is_keras_nlp_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      Optional[int]             =			['TFGPT2Tokenizer']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__     :      int             =			['FlaxGPT2LMHeadModel', 'FlaxGPT2Model', 'FlaxGPT2PreTrainedModel']

if TYPE_CHECKING:
							from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
							from .tokenization_gpta import GPTaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpta_fast import GPTaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpta import (
														    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTaDoubleHeadsModel,
														    GPTaForQuestionAnswering,
														    GPTaForSequenceClassification,
														    GPTaForTokenClassification,
														    GPTaLMHeadModel,
														    GPTaModel,
														    GPTaPreTrainedModel,
														    load_tf_weights_in_gpta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gpta import (
														    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFGPTaDoubleHeadsModel,
														    TFGPTaForSequenceClassification,
														    TFGPTaLMHeadModel,
														    TFGPTaMainLayer,
														    TFGPTaModel,
														    TFGPTaPreTrainedModel,
														)

							try:
														if not is_keras_nlp_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_gpta_tf import TFGPTaTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
							import sys

							A__     :      Union[str, Any]             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












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__     :      Dict             =			'none'
A__     :      Optional[Any]             =			'none'






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      value.split(lowerCamelCase_    )
				return len(lowerCamelCase_    ) > 1 and any((len(lowerCamelCase_    ) == 3 for x in split)    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				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'''    )
				lowercase__       			=      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_    ):






				'''simple docstring'''
				if answer is None:
								return None
				try:
								lowercase__       			=      convert_to_float(lowerCamelCase_    )
								if isinstance(lowerCamelCase_		,       lowerCamelCase_    ) and math.isnan(lowerCamelCase_    ):
												return None
								return value
				except ValueError:
								return answer.lower()


A__     :      Tuple             =			{
    'text': lambda x: x,
    'real': convert_to_float,
}
class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2
				lowercase__    							=	3
				lowercase__    							=	4
				lowercase__    							=	5



class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2



@dataclasses.dataclass
class 				_UpperCAmelCase :

				"""simple docstring"""




				lowercase__    							=	42
				lowercase__    							=	42
				lowercase__    							=	42



A__     :      str             =			re.compile(r'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return list(_TOKENIZER.findall(x.lower()    )    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''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      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      table['''types''']
				return _TYPE_CONVERTER[types[column]](lowerCamelCase_    )






def   a      (						lowerCamelCase_    ):






				'''simple docstring'''
				return isinstance(lowerCamelCase_		,       lowerCamelCase_    )






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				for cond in conditions:
								lowercase__       			=      row[cond.column]

								lowercase__       			=      _parse_value(lowerCamelCase_		,       cond.column		,       cond.cmp_value    )

								if _is_string(lowerCamelCase_    ) and _is_string(lowerCamelCase_    ):
												lowercase__       			=      _normalize_for_match(lowerCamelCase_    )
												lowercase__       			=      _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_    ):






				'''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(lowerCamelCase_    )    )

				# If we have just one answer, if float returns it or try a conversion.
				lowercase__       			=      [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_    ):






				'''simple docstring'''
				lowercase__       			=      sql_query['''agg''']
				if aggregation_op_index >= 3:
								lowercase__       			=      _Aggregation(lowerCamelCase_    )
				else:
								lowercase__       			=      _Aggregation.NONE

				lowercase__       			=      sql_query['''sel''']
				lowercase__       			=      [
				    _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''']    )
				]

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

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

				return indices, aggregation_op






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				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_    ):






				'''simple docstring'''
				lowercase__			,						lowercase__       			=      _get_answer_coordinates(lowerCamelCase_		,       lowerCamelCase_    )
				lowercase__       			=      _get_float_answer(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    )
				lowercase__       			=      _get_answer_text(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    )
				# keep the original data the same with TaPas
				if len(lowerCamelCase_    ) == 0:
								lowercase__       			=      [EMPTY_ANSWER]
				return answer_text




