



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_   :					Union[str, Any]			,lowerCAmelCase_   :					int				)      -> Optional[Any]:






						'''simple docstring'''



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



def        __a     (    lowerCAmelCase_   :					int				)      -> Any:






						'''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_   :					Tuple				)      -> List[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 		lowercase						( enum.Enum):






	"""simple docstring"""


	a__      :					int		   =							0
	a__      :					Any		   =							1
	a__      :					List[str]		   =							2
	a__      :					List[str]		   =							3
	a__      :					List[Any]		   =							4
	a__      :					List[Any]		   =							5






class 		lowercase						( enum.Enum):






	"""simple docstring"""


	a__      :					Dict		   =							0
	a__      :					Dict		   =							1
	a__      :					Union[str, Any]		   =							2






@dataclasses.dataclass
class 		lowercase						:






	"""simple docstring"""


	a__      :					Text
	a__      :					_Operator
	a__      :					Any

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



def        __a     (    lowerCAmelCase_   :					int				)      -> Union[str, Any]:






						'''simple docstring'''



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



def        __a     (    lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					Optional[int]				)      -> Tuple:






						'''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_   :					Any			,lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Dict				)      -> Any:






						'''simple docstring'''



						UpperCAmelCase_=   table["""types"""]
						return _TYPE_CONVERTER[types[column]](lowerCAmelCase_				)



def        __a     (    lowerCAmelCase_   :					List[Any]				)      -> Dict:






						'''simple docstring'''



						return isinstance(lowerCAmelCase_			,lowerCAmelCase_				)



def        __a     (    lowerCAmelCase_   :					Union[str, Any]			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					Union[str, Any]				)      -> Any:






						'''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_   :					List[str]			,lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Tuple				)      -> 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_   :					str			,lowerCAmelCase_   :					str				)      -> 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_   :					Any			,lowerCAmelCase_   :					str			,lowerCAmelCase_   :					Optional[Any]				)      -> str:






						'''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_   :					Union[str, Any]			,lowerCAmelCase_   :					Union[str, Any]				)      -> 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









import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
	__A				    =						True
	__A				    =						'''p3dn.24xlarge'''
	import smdistributed.dataparallel.tensorflow as sdp
else:
	__A				    =						False



def        __a     (    lowerCAmelCase_   :					Optional[Any]			,lowerCAmelCase_   :					Union[str, Any]			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					int			,lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Any			,lowerCAmelCase_   :					int=None				)      -> Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=   tqdm(lowerCAmelCase_				)
						for i, batch in enumerate(lowerCAmelCase_				):
												with tf.GradientTape() as tape:
																		UpperCAmelCase_,   UpperCAmelCase_=   batch
																		UpperCAmelCase_=   model(lowerCAmelCase_				)
																		UpperCAmelCase_=   loss(lowerCAmelCase_			,outputs.logits				)

												if SDP_ENABLED:
																		UpperCAmelCase_=   sdp.DistributedGradientTape(lowerCAmelCase_			,sparse_as_dense=lowerCAmelCase_				)

												UpperCAmelCase_=   tape.gradient(lowerCAmelCase_			,model.trainable_variables				)
												opt.apply_gradients(zip(lowerCAmelCase_			,model.trainable_variables				)				)

												pbar.set_description(F"""Loss: {loss_value:.4f}"""				)

												if SDP_ENABLED and i == 0:
																		sdp.broadcast_variables(model.variables			,root_rank=0				)
																		sdp.broadcast_variables(opt.variables()			,root_rank=0				)

												if max_steps and i >= max_steps:
																		break

						UpperCAmelCase_=   {"""loss""": loss_value.numpy()}
						return train_results



def        __a     (    lowerCAmelCase_   :					Optional[int]			,lowerCAmelCase_   :					Dict			,lowerCAmelCase_   :					str				)      -> str:






						'''simple docstring'''



						UpperCAmelCase_,   UpperCAmelCase_=   load_dataset("""imdb"""			,split=["""train""", """test"""]				)

						# Preprocess train dataset
						UpperCAmelCase_=   train_dataset.map(
						    lambda lowerCAmelCase_				: tokenizer(e["""text"""]			,truncation=lowerCAmelCase_			,padding="""max_length"""				)			,batched=lowerCAmelCase_				)
						train_dataset.set_format(type="""tensorflow"""			,columns=["""input_ids""", """attention_mask""", """label"""]				)

						UpperCAmelCase_=   {
						    x: train_dataset[x].to_tensor(default_value=0			,shape=[None, tokenizer.model_max_length]				)
						    for x in ["""input_ids""", """attention_mask"""]
						}
						UpperCAmelCase_=   tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])				)

						# Preprocess test dataset
						UpperCAmelCase_=   test_dataset.map(
						    lambda lowerCAmelCase_				: tokenizer(e["""text"""]			,truncation=lowerCAmelCase_			,padding="""max_length"""				)			,batched=lowerCAmelCase_				)
						test_dataset.set_format(type="""tensorflow"""			,columns=["""input_ids""", """attention_mask""", """label"""]				)

						UpperCAmelCase_=   {
						    x: test_dataset[x].to_tensor(default_value=0			,shape=[None, tokenizer.model_max_length]				)
						    for x in ["""input_ids""", """attention_mask"""]
						}
						UpperCAmelCase_=   tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])				)

						if SDP_ENABLED:
												UpperCAmelCase_=   tf_train_dataset.shard(sdp.size()			,sdp.rank()				)
												UpperCAmelCase_=   tf_test_dataset.shard(sdp.size()			,sdp.rank()				)
						UpperCAmelCase_=   tf_train_dataset.batch(lowerCAmelCase_			,drop_remainder=lowerCAmelCase_				)
						UpperCAmelCase_=   tf_test_dataset.batch(lowerCAmelCase_			,drop_remainder=lowerCAmelCase_				)

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
	__A				    =						argparse.ArgumentParser()

	# Hyperparameters sent by the client are passed as command-line arguments to the script.
	parser.add_argument('''--epochs''', type=int, default=3)
	parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
	parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
	parser.add_argument('''--model_name_or_path''', type=str)
	parser.add_argument('''--learning_rate''', type=str, default=5E-5)
	parser.add_argument('''--do_train''', type=bool, default=True)
	parser.add_argument('''--do_eval''', type=bool, default=True)
	parser.add_argument('''--output_dir''', type=str)
	parser.add_argument('''--max_steps''', type=int, default=None)

	# Data, model, and output directories
	parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
	parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
	parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

	__A    ,			__A				    =						parser.parse_known_args()

	# Set up logging
	__A				    =						logging.getLogger(__name__)

	logging.basicConfig(
	    level=logging.getLevelName('''INFO'''),
	    handlers=[logging.StreamHandler(sys.stdout)],
	    format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
	)

	if SDP_ENABLED:
		sdp.init()

		__A				    =						tf.config.experimental.list_physical_devices('''GPU''')
		for gpu in gpus:
			tf.config.experimental.set_memory_growth(gpu, True)
		if gpus:
			tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
	__A				    =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	__A				    =						AutoTokenizer.from_pretrained(args.model_name_or_path)

	# get datasets
	__A    ,			__A				    =						get_datasets(
	    tokenizer=tokenizer,
	    train_batch_size=args.per_device_train_batch_size,
	    eval_batch_size=args.per_device_eval_batch_size,
	)

	# fine optimizer and loss
	__A				    =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
	__A				    =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
	__A				    =						[tf.keras.metrics.SparseCategoricalAccuracy()]
	model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

	# Training
	if args.do_train:
		# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
		__A				    =						time.time()
		__A				    =						fit(
		    model,
		    loss,
		    optimizer,
		    tf_train_dataset,
		    args.epochs,
		    args.per_device_train_batch_size,
		    max_steps=args.max_steps,
		)
		__A				    =						time.time() - start_train_time
		logger.info('''*** Train ***''')
		logger.info(f'train_runtime = {end_train_time}')

		__A				    =						os.path.join(args.output_dir, '''train_results.txt''')

		if not SDP_ENABLED or sdp.rank() == 0:
			with open(output_eval_file, '''w''') as writer:
				logger.info('''***** Train results *****''')
				logger.info(train_results)
				for key, value in train_results.items():
					logger.info(f'  {key} = {value}')
					writer.write(f'{key} = {value}\n')

    # Evaluation
	if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
		__A				    =						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
		logger.info('''*** Evaluate ***''')

		__A				    =						os.path.join(args.output_dir, '''eval_results.txt''')

		with open(output_eval_file, '''w''') as writer:
			logger.info('''***** Eval results *****''')
			logger.info(result)
			for key, value in result.items():
				logger.info(f'  {key} = {value}')
				writer.write(f'{key} = {value}\n')

    # Save result
	if SDP_ENABLED:
		if sdp.rank() == 0:
			model.save_pretrained(args.output_dir)
			tokenizer.save_pretrained(args.output_dir)
	else:
		model.save_pretrained(args.output_dir)
		tokenizer.save_pretrained(args.output_dir)




