

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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = None  ,		):
						snake_case_   =	{}

						if train_file is not None:
												snake_case_   =	[train_file]
						if eval_file is not None:
												snake_case_   =	[eval_file]
						if test_file is not None:
												snake_case_   =	[test_file]

						snake_case_   =	datasets.load_dataset('''csv'''  ,		data_files=SCREAMING_SNAKE_CASE__       )
						snake_case_   =	list(ds[list(files.keys()       )[0]].features.keys()       )
						snake_case_   =	features_name.pop(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	list(set(ds[list(files.keys()       )[0]][label_name]       )       )
						snake_case_   =	{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__       )}
						snake_case_   =	tokenizer.model_input_names
						snake_case_   =	{}

						if len(SCREAMING_SNAKE_CASE__       ) == 1:
												for k in files.keys():
																		snake_case_   =	ds[k].map(
																		    lambda SCREAMING_SNAKE_CASE__       : tokenizer.batch_encode_plus(
																		        example[features_name[0]]  ,		truncation=SCREAMING_SNAKE_CASE__  ,		max_length=SCREAMING_SNAKE_CASE__  ,		padding='''max_length'''       )  ,		batched=SCREAMING_SNAKE_CASE__  ,		)
						elif len(SCREAMING_SNAKE_CASE__       ) == 2:
												for k in files.keys():
																		snake_case_   =	ds[k].map(
																		    lambda SCREAMING_SNAKE_CASE__       : tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]])  ,		truncation=SCREAMING_SNAKE_CASE__  ,		max_length=SCREAMING_SNAKE_CASE__  ,		padding='''max_length'''  ,		)  ,		batched=SCREAMING_SNAKE_CASE__  ,		)

						def gen_train():
												for ex in transformed_ds[datasets.Split.TRAIN]:
																		snake_case_   =	{k: v for k, v in ex.items() if k in input_names}
																		snake_case_   =	labelaid[ex[label_name]]
																		yield (d, label)

						def gen_val():
												for ex in transformed_ds[datasets.Split.VALIDATION]:
																		snake_case_   =	{k: v for k, v in ex.items() if k in input_names}
																		snake_case_   =	labelaid[ex[label_name]]
																		yield (d, label)

						def gen_test():
												for ex in transformed_ds[datasets.Split.TEST]:
																		snake_case_   =	{k: v for k, v in ex.items() if k in input_names}
																		snake_case_   =	labelaid[ex[label_name]]
																		yield (d, label)

						snake_case_   =	(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__  ,		({k: tf.intaa for k in input_names}, tf.intaa)  ,		({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,		)
						    if datasets.Split.TRAIN in transformed_ds
						    else None
						)

						if train_ds is not None:
												snake_case_   =	train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

						snake_case_   =	(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__  ,		({k: tf.intaa for k in input_names}, tf.intaa)  ,		({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,		)
						    if datasets.Split.VALIDATION in transformed_ds
						    else None
						)

						if val_ds is not None:
												snake_case_   =	val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

						snake_case_   =	(
						    tf.data.Dataset.from_generator(
						        SCREAMING_SNAKE_CASE__  ,		({k: tf.intaa for k in input_names}, tf.intaa)  ,		({k: tf.TensorShape([None]       ) for k in input_names}, tf.TensorShape([]       ))  ,		)
						    if datasets.Split.TEST in transformed_ds
						    else None
						)

						if test_ds is not None:
												snake_case_   =	test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

						return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase_       	=	logging.getLogger(__name__)





@dataclass
class 	snake_case_   :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				field(metadata={"help": "Which column contains the label"}					)
			SCREAMING_SNAKE_CASE     :   str									=				field(default=__A	,       metadata={"help": "The path of the training file"}					)
			SCREAMING_SNAKE_CASE     :   Optional[str]									=				field(default=__A	,       metadata={"help": "The path of the development file"}					)
			SCREAMING_SNAKE_CASE     :   Optional[str]									=				field(default=__A	,       metadata={"help": "The path of the test file"}					)
			SCREAMING_SNAKE_CASE     :   int									=				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."
			        )
			    }	,       )
			SCREAMING_SNAKE_CASE     :   bool									=				field(
			    default=__A	,       metadata={"help": "Overwrite the cached training and evaluation sets"}					)






@dataclass
class 	snake_case_   :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   str									=				field(
			    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}					)
			SCREAMING_SNAKE_CASE     :   Optional[str]									=				field(
			    default=__A	,       metadata={"help": "Pretrained config name or path if not the same as model_name"}					)
			SCREAMING_SNAKE_CASE     :   Optional[str]									=				field(
			    default=__A	,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}					)
			SCREAMING_SNAKE_CASE     :   bool									=				field(default=__A	,       metadata={"help": "Set this flag to use fast tokenization."}					)
			# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
			# or just modify its tokenizer_config.json.
			SCREAMING_SNAKE_CASE     :   Optional[str]									=				field(
			    default=__A	,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,       )






def   __SCREAMING_SNAKE_CASE					():
						# 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.
						snake_case_   =	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
						snake_case_,    snake_case_,    snake_case_   =	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  ,		)
						logger.info(
						    F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1       )}, '''
						    F'''16-bits training: {training_args.fpaa}'''       )
						logger.info(F'''Training/evaluation parameters {training_args}'''       )

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

						snake_case_   =	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  ,		)

						snake_case_,    snake_case_,    snake_case_,    snake_case_   =	get_tfds(
						    train_file=data_args.train_file  ,		eval_file=data_args.dev_file  ,		test_file=data_args.test_file  ,		tokenizer=SCREAMING_SNAKE_CASE__  ,		label_column_id=data_args.label_column_id  ,		max_seq_length=data_args.max_seq_length  ,		)

						snake_case_   =	AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,		num_labels=len(SCREAMING_SNAKE_CASE__       )  ,		labelaid=SCREAMING_SNAKE_CASE__  ,		idalabel={id: label for label, id in labelaid.items()}  ,		finetuning_task='''text-classification'''  ,		cache_dir=model_args.cache_dir  ,		)

						with training_args.strategy.scope():
												snake_case_   =	TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path  ,		from_pt=bool('''.bin''' in model_args.model_name_or_path       )  ,		config=SCREAMING_SNAKE_CASE__  ,		cache_dir=model_args.cache_dir  ,		)

						def compute_metrics(SCREAMING_SNAKE_CASE__       ) -> Dict:
												snake_case_   =	np.argmax(p.predictions  ,		axis=1       )

												return {"acc": (preds == p.label_ids).mean()}

						# Initialize our Trainer
						snake_case_   =	TFTrainer(
						    model=SCREAMING_SNAKE_CASE__  ,		args=SCREAMING_SNAKE_CASE__  ,		train_dataset=SCREAMING_SNAKE_CASE__  ,		eval_dataset=SCREAMING_SNAKE_CASE__  ,		compute_metrics=SCREAMING_SNAKE_CASE__  ,		)

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir       )

						# Evaluation
						snake_case_   =	{}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''       )
												snake_case_   =	trainer.evaluate()
												snake_case_   =	os.path.join(training_args.output_dir  ,		'''eval_results.txt'''       )

												with open(SCREAMING_SNAKE_CASE__  ,		'''w'''       ) as writer:
																		logger.info('''***** Eval results *****'''       )

																		for key, value in result.items():
																								logger.info(F'''  {key} = {value}'''       )
																								writer.write(F'''{key} = {value}\n'''       )

																		results.update(SCREAMING_SNAKE_CASE__       )

						return results


if __name__ == "__main__":
					main()


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def   __SCREAMING_SNAKE_CASE					(*SCREAMING_SNAKE_CASE__       ):
						if not isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	list(SCREAMING_SNAKE_CASE__       )
						for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	None
						gc.collect()
						if is_xpu_available():
												torch.xpu.empty_cache()
						elif is_npu_available():
												torch.npu.empty_cache()
						else:
												torch.cuda.empty_cache()
						return objects


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[
						    '''CUDA out of memory.''',  # CUDA OOM
						    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
						    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
						]
						if isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ) and len(exception.args       ) == 1:
												return any(err in exception.args[0] for err in _statements       )
						return False


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = None  ,		SCREAMING_SNAKE_CASE__ = 128       ):
						if function is None:
												return functools.partial(SCREAMING_SNAKE_CASE__  ,		starting_batch_size=SCREAMING_SNAKE_CASE__       )

						snake_case_   =	starting_batch_size

						def decorator(*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       ):
												nonlocal batch_size
												gc.collect()
												if is_xpu_available():
																		torch.xpu.empty_cache()
												elif is_npu_available():
																		torch.npu.empty_cache()
												else:
																		torch.cuda.empty_cache()
												snake_case_   =	list(inspect.signature(SCREAMING_SNAKE_CASE__       ).parameters.keys()       )
												# Guard against user error
												if len(SCREAMING_SNAKE_CASE__       ) < (len(SCREAMING_SNAKE_CASE__       ) + 1):
																		snake_case_   =	''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]  ,		args[1:]       )]       )
																		raise TypeError(
																		    F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
																		    F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''       )
												while True:
																		if batch_size == 0:
																								raise RuntimeError('''No executable batch size found, reached zero.'''       )
																		try:
																								return function(SCREAMING_SNAKE_CASE__  ,		*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )
																		except Exception as e:
																								if should_reduce_batch_size(SCREAMING_SNAKE_CASE__       ):
																														gc.collect()
																														if is_xpu_available():
																																				torch.xpu.empty_cache()
																														elif is_npu_available():
																																				torch.npu.empty_cache()
																														else:
																																				torch.cuda.empty_cache()
																														batch_size //= 2
																								else:
																														raise

						return decorator