






import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_a   			=						logging.get_logger(__name__)

if is_tf_available():
		import tensorflow as tf





@dataclass
class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   "tf"
	UpperCamelCase__			=   field(
	    default=snake_case__  , metadata={"help": "Name of TPU"}  , )

	UpperCamelCase__			=   field(
	    default=snake_case__  , metadata={"help": "Zone of TPU"}  , )

	UpperCamelCase__			=   field(
	    default=snake_case__  , metadata={"help": "Name of Cloud TPU-enabled project"}  , )

	UpperCamelCase__			=   field(
	    default=1.0  , metadata={"help": "Power for the Polynomial decay LR scheduler."}  , )

	UpperCamelCase__			=   field(default=snake_case__  , metadata={"help": "Whether to activate the XLA compilation or not"})

	@cached_property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							requires_backends(self ,					['tf']					)
							logger.info('Tensorflow: setting up strategy'					)

							_UpperCAmelCase             =    tf.config.list_physical_devices('GPU'					)

							# Set to float16 at first
							if self.fpaa:
													tf.keras.mixed_precision.set_global_policy('mixed_float16'					)

							if self.no_cuda:
													_UpperCAmelCase             =    tf.distribute.OneDeviceStrategy(device='/cpu:0'					)
							else:
													try:
																			if self.tpu_name:
																									_UpperCAmelCase             =    tf.distribute.cluster_resolver.TPUClusterResolver(
																									    self.tpu_name ,					zone=self.tpu_zone ,					project=self.gcp_project					)
																			else:
																									_UpperCAmelCase             =    tf.distribute.cluster_resolver.TPUClusterResolver()
													except ValueError:
																			if self.tpu_name:
																									raise RuntimeError(F"""Couldn't connect to TPU {self.tpu_name}!"""					)
																			else:
																									_UpperCAmelCase             =    None

													if tpu:
																			# Set to bfloat16 in case of TPU
																			if self.fpaa:
																									tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'					)

																			tf.config.experimental_connect_to_cluster(UpperCAmelCase					)
																			tf.tpu.experimental.initialize_tpu_system(UpperCAmelCase					)

																			_UpperCAmelCase             =    tf.distribute.TPUStrategy(UpperCAmelCase					)

													elif len(UpperCAmelCase					) == 0:
																			_UpperCAmelCase             =    tf.distribute.OneDeviceStrategy(device='/cpu:0'					)
													elif len(UpperCAmelCase					) == 1:
																			_UpperCAmelCase             =    tf.distribute.OneDeviceStrategy(device='/gpu:0'					)
													elif len(UpperCAmelCase					) > 1:
																			# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																			_UpperCAmelCase             =    tf.distribute.MirroredStrategy()
													else:
																			raise ValueError('Cannot find the proper strategy, please check your environment properties.'					)

							return strategy

	@property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							requires_backends(self ,					['tf']					)
							return self._setup_strategy

	@property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							requires_backends(self ,					['tf']					)
							return self._setup_strategy.num_replicas_in_sync

	@property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							return False  # TF Logging is handled by Keras not the Trainer

	@property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							if self.per_gpu_train_batch_size:
													logger.warning(
													    'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
													    'version. Using `--per_device_train_batch_size` is preferred.'					)
							_UpperCAmelCase             =    self.per_gpu_train_batch_size or self.per_device_train_batch_size
							return per_device_batch_size * self.n_replicas

	@property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							if self.per_gpu_eval_batch_size:
													logger.warning(
													    'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
													    'version. Using `--per_device_eval_batch_size` is preferred.'					)
							_UpperCAmelCase             =    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
							return per_device_batch_size * self.n_replicas

	@property
	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							requires_backends(self ,					['tf']					)
							warnings.warn(
							    'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.' ,					UpperCAmelCase ,					)
							return self._setup_strategy.num_replicas_in_sync








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   			=						'''none'''
_a   			=						'''none'''


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Any:





						"""simple docstring"""



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


def __A					(   __lowerCAmelCase			)->						List[str]:





						"""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'			)
						_UpperCAmelCase             =    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			)->						Any:





						"""simple docstring"""



						if answer is None:
												return None
						try:
												_UpperCAmelCase             =    convert_to_float(__lowerCAmelCase			)
												if isinstance(__lowerCAmelCase				,	__lowerCAmelCase			) and math.isnan(__lowerCAmelCase			):
																		return None
												return value
						except ValueError:
												return answer.lower()


_a   			=						{
    '''text''': lambda x: x,
    '''real''': convert_to_float,
}





class __lowerCamelCase				(   enum.Enum):





	"""simple docstring"""






	UpperCamelCase__			=   0
	UpperCamelCase__			=   1
	UpperCamelCase__			=   2
	UpperCamelCase__			=   3
	UpperCamelCase__			=   4
	UpperCamelCase__			=   5





class __lowerCamelCase				(   enum.Enum):





	"""simple docstring"""






	UpperCamelCase__			=   0
	UpperCamelCase__			=   1
	UpperCamelCase__			=   2





@dataclasses.dataclass
class __lowerCamelCase				:





	"""simple docstring"""






	UpperCamelCase__			=   42
	UpperCamelCase__			=   42
	UpperCamelCase__			=   42



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


def __A					(   __lowerCAmelCase			)->						Optional[Any]:





						"""simple docstring"""



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


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Any:





						"""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			)->						str:





						"""simple docstring"""



						_UpperCAmelCase             =    table['types']
						return _TYPE_CONVERTER[types[column]](__lowerCAmelCase			)


def __A					(   __lowerCAmelCase			)->						str:





						"""simple docstring"""



						return isinstance(__lowerCAmelCase				,	__lowerCAmelCase			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						str:





						"""simple docstring"""



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

												_UpperCAmelCase             =    _parse_value(__lowerCAmelCase				,	cond.column				,	cond.cmp_value			)

												if _is_string(__lowerCAmelCase			) and _is_string(__lowerCAmelCase			):
																		_UpperCAmelCase             =    _normalize_for_match(__lowerCAmelCase			)
																		_UpperCAmelCase             =    _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			)->						str:





						"""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.
						_UpperCAmelCase             =    [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			)->						Any:





						"""simple docstring"""



						_UpperCAmelCase             =    sql_query['agg']
						if aggregation_op_index >= 3:
												_UpperCAmelCase             =    _Aggregation(__lowerCAmelCase			)
						else:
												_UpperCAmelCase             =    _Aggregation.NONE

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

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

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

						return indices, aggregation_op


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Dict:





						"""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			)->						List:





						"""simple docstring"""



						_UpperCAmelCase      ,				_UpperCAmelCase             =    _get_answer_coordinates(__lowerCAmelCase				,	__lowerCAmelCase			)
						_UpperCAmelCase             =    _get_float_answer(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)
						_UpperCAmelCase             =    _get_answer_text(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)
						# keep the original data the same with TaPas
						if len(__lowerCAmelCase			) == 0:
												_UpperCAmelCase             =    [EMPTY_ANSWER]
						return answer_text
