


"""simple docstring"""


import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
		import sqlitea

		import sqlalchemy


a_											=			datasets.utils.logging.get_logger(__name__)
@dataclass
class snake_case					(     datasets.BuilderConfig):
		__UpperCamelCase							=	None
		__UpperCamelCase							=	None
		__UpperCamelCase							=	None
		__UpperCamelCase							=	True
		__UpperCamelCase							=	None
		__UpperCamelCase							=	None
		__UpperCamelCase							=	None
		__UpperCamelCase							=	1_0000
		__UpperCamelCase							=	None







		def        a_       (    self					:				Optional[Any]						)				->  Dict:





						'''simple docstring'''







						if self.sql is None:
										raise ValueError("sql must be specified"						)
						if self.con is None:
										raise ValueError("con must be specified"						)







		def        a_       (    self					:				Union[str, Any]			,      a__					:				dict			,      a__					:				Optional[datasets.Features] = None			,      )				->  str:





						'''simple docstring'''







						_A         =	config_kwargs.copy()
						# We need to stringify the Selectable object to make its hash deterministic

						# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
						_A         =	config_kwargs["sql"]
						if not isinstance(a__			,      a__						):
										if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
														import sqlalchemy

														if isinstance(a__			,      sqlalchemy.sql.Selectable						):
																		_A         =	sqlalchemy.create_engine(config_kwargs["con"].split("://"						)[0] + "://"						)
																		_A         =	str(sql.compile(dialect=engine.dialect						)						)
																		_A         =	sql_str
														else:
																		raise TypeError(
																		    F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(a__						)}: {sql}"""						)
										else:
														raise TypeError(
														    F"""Supported types for 'sql' are string and sqlalchemy.sql.Selectable but got {type(a__						)}: {sql}"""						)
						_A         =	config_kwargs["con"]
						if not isinstance(a__			,      a__						):
										_A         =	id(a__						)
										logger.info(
										    F"""SQL connection 'con' of type {type(a__						)} couldn't be hashed properly. To enable hashing, specify 'con' as URI string instead."""						)

						return super().create_config_id(a__			,      custom_features=a__						)







		@property
		def        a_       (    self					:				Optional[int]						)				->  Optional[Any]:





						'''simple docstring'''







						_A         =	{
						    "index_col": self.index_col,
						    "columns": self.columns,
						    "params": self.params,
						    "coerce_float": self.coerce_float,
						    "parse_dates": self.parse_dates,
						}
						return pd_read_sql_kwargs





class snake_case					(     datasets.ArrowBasedBuilder):
		__UpperCamelCase							=	SqlConfig







		def        a_       (    self					:				List[str]						)				->  int:





						'''simple docstring'''







						return datasets.DatasetInfo(features=self.config.features						)







		def        a_       (    self					:				str			,      a__					:				int						)				->  List[str]:





						'''simple docstring'''







						return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,      gen_kwargs={}						)]







		def        a_       (    self					:				Optional[int]			,      a__					:				pa.Table						)				->  pa.Table:





						'''simple docstring'''







						if self.config.features is not None:
										_A         =	self.config.features.arrow_schema
										if all(not require_storage_cast(a__						) for feature in self.config.features.values()						):
														# cheaper cast
														_A         =	pa.Table.from_arrays([pa_table[field.name] for field in schema]			,      schema=a__						)
										else:
														# more expensive cast; allows str <-> int/float or str to Audio for example
														_A         =	table_cast(a__			,      a__						)
						return pa_table







		def        a_       (    self					:				List[Any]						)				->  str:





						'''simple docstring'''







						_A         =	self.config.chunksize
						_A         =	pd.read_sql(
						    self.config.sql			,      self.config.con			,      chunksize=a__			,      **self.config.pd_read_sql_kwargs						)
						_A         =	[sql_reader] if chunksize is None else sql_reader
						for chunk_idx, df in enumerate(a__						):
										_A         =	pa.Table.from_pandas(a__						)
										yield chunk_idx, self._cast_table(a__						)



"""simple docstring"""


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a_											=			logging.getLogger(__name__)
@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Pretrained config name or path if not the same as model_name'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   , )





@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys())})
		__UpperCamelCase							=	field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
		__UpperCamelCase							=	field(
		    default=128   , metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }   , )
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Overwrite the cached training and evaluation sets'})






def    a__       ( __lowercase      )		->		Dict[str, torch.Tensor]:
				_A         =	default_data_collator(__lowercase      )
				_A         =	batch.pop("pairID"   ,	__lowercase      )
				return batch






def    a__       ( )		->		Optional[Any]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				_A         =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
				_A     ,						_A     ,						_A         =	parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir      )
				    and os.listdir(training_args.output_dir      )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    " --overwrite_output_dir to overcome."      )

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,	datefmt="%m/%d/%Y %H:%M:%S"   ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,	)
				logger.warning(
				    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"   ,	training_args.local_rank   ,	training_args.device   ,	training_args.n_gpu   ,	bool(training_args.local_rank != -1      )   ,	training_args.fpaa   ,	)
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank      ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("Training/evaluation parameters %s"   ,	__lowercase      )

				# Set seed
				set_seed(training_args.seed      )

				try:
								_A         =	hans_tasks_num_labels[data_args.task_name]
				except KeyError:
								raise ValueError("Task not found: %s" % (data_args.task_name)      )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				_A         =	AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,	num_labels=__lowercase   ,	finetuning_task=data_args.task_name   ,	cache_dir=model_args.cache_dir   ,	)
				_A         =	AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,	cache_dir=model_args.cache_dir   ,	)
				_A         =	AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path   ,	from_tf=bool(".ckpt" in model_args.model_name_or_path      )   ,	config=__lowercase   ,	cache_dir=model_args.cache_dir   ,	)

				# Get datasets
				_A         =	(
				    HansDataset(
				        data_dir=data_args.data_dir   ,	tokenizer=__lowercase   ,	task=data_args.task_name   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	)
				    if training_args.do_train
				    else None
				)
				_A         =	(
				    HansDataset(
				        data_dir=data_args.data_dir   ,	tokenizer=__lowercase   ,	task=data_args.task_name   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	evaluate=__lowercase   ,	)
				    if training_args.do_eval
				    else None
				)

				# Initialize our Trainer
				_A         =	Trainer(
				    model=__lowercase   ,	args=__lowercase   ,	train_dataset=__lowercase   ,	eval_dataset=__lowercase   ,	data_collator=__lowercase   ,	)

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***"      )

								_A         =	trainer.predict(__lowercase      )
								_A         =	output.predictions
								_A         =	np.argmax(__lowercase   ,	axis=1      )

								_A         =	[ex.pairID for ex in eval_dataset]
								_A         =	os.path.join(training_args.output_dir   ,	"hans_predictions.txt"      )
								_A         =	eval_dataset.get_labels()
								if trainer.is_world_master():
												with open(__lowercase   ,	"w"      ) as writer:
																writer.write("pairID,gold_label\n"      )
																for pid, pred in zip(__lowercase   ,	__lowercase      ):
																				writer.write("ex" + str(__lowercase      ) + "," + label_list[int(__lowercase      )] + "\n"      )

								trainer._log(output.metrics      )






def    a__       ( __lowercase      )		->		Dict:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()