


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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = None				, )    ->				Union[str, Any]:
							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


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    logging.getLogger(__name__)





@dataclass
class      __A     :





							'''simple docstring'''

							__lowercase:		int							      =    field(metadata={"""help""": """Which column contains the label"""})
							__lowercase:		str							      =    field(default=snake_case__						,		metadata={"""help""": """The path of the training file"""})
							__lowercase:		Optional[str]							      =    field(default=snake_case__						,		metadata={"""help""": """The path of the development file"""})
							__lowercase:		Optional[str]							      =    field(default=snake_case__						,		metadata={"""help""": """The path of the test file"""})
							__lowercase:		int							      =    field(
							    default=1_28						,		metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }						,		)
							__lowercase:		bool							      =    field(
							    default=snake_case__						,		metadata={"""help""": """Overwrite the cached training and evaluation sets"""})





@dataclass
class      __A     :





							'''simple docstring'''

							__lowercase:		str							      =    field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
							__lowercase:		Optional[str]							      =    field(
							    default=snake_case__						,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
							__lowercase:		Optional[str]							      =    field(
							    default=snake_case__						,		metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
							__lowercase:		bool							      =    field(default=snake_case__						,		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.
							__lowercase:		Optional[str]							      =    field(
							    default=snake_case__						,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,		)







def 						_a				(      )    ->				Union[str, 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.
							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()







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Tuple		=    [
				    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegatronBertForCausalLM',
				    'MegatronBertForMaskedLM',
				    'MegatronBertForMultipleChoice',
				    'MegatronBertForNextSentencePrediction',
				    'MegatronBertForPreTraining',
				    'MegatronBertForQuestionAnswering',
				    'MegatronBertForSequenceClassification',
				    'MegatronBertForTokenClassification',
				    'MegatronBertModel',
				    'MegatronBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_megatron_bert import (
								    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegatronBertForCausalLM,
								    MegatronBertForMaskedLM,
								    MegatronBertForMultipleChoice,
								    MegatronBertForNextSentencePrediction,
								    MegatronBertForPreTraining,
								    MegatronBertForQuestionAnswering,
								    MegatronBertForSequenceClassification,
								    MegatronBertForTokenClassification,
								    MegatronBertModel,
								    MegatronBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE							:    int		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



