




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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = None					,	):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{}

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

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

			if len(SCREAMING_SNAKE_CASE   ) == 1:
						for k in files.keys():
									SCREAMING_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():
									SCREAMING_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]:
									SCREAMING_SNAKE_CASE           =					{k: v for k, v in ex.items() if k in input_names}
									SCREAMING_SNAKE_CASE           =					labelaid[ex[label_name]]
									yield (d, label)

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

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

			SCREAMING_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:
						SCREAMING_SNAKE_CASE           =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]   )   )   )

			SCREAMING_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:
						SCREAMING_SNAKE_CASE           =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

			SCREAMING_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:
						SCREAMING_SNAKE_CASE           =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]   )   )   )

			return train_ds, val_ds, test_ds, labelaid


__A						:							Any							=  logging.getLogger(__name__)




@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							a__						  =     field(metadata={'''help''': '''Which column contains the label'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''The path of the training file'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''The path of the development file'''}       )
							a__						  =     field(default=A		,      metadata={'''help''': '''The path of the test file'''}       )
							a__						  =     field(
							    default=1_2_8		,      metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }		,      )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )





@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							a__						  =     field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
							a__						  =     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.
							a__						  =     field(
							    default=A		,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,      )





def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			# 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.
			SCREAMING_SNAKE_CASE           =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_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.

			SCREAMING_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					,	)

			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_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					,	)

			SCREAMING_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():
						SCREAMING_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:
						SCREAMING_SNAKE_CASE           =					np.argmax(p.predictions					,	axis=1   )

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

			# Initialize our Trainer
			SCREAMING_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
			SCREAMING_SNAKE_CASE           =					{}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""   )
						SCREAMING_SNAKE_CASE           =					trainer.evaluate()
						SCREAMING_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()












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							Any							=  logging.get_logger(__name__)

__A						:							int							=  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''megatron-bert'''





							def __init__(					self       :	str       ,     a       :	Union[str, Any]=29_056       ,     a       :	Optional[int]=1_024       ,     a       :	str=24       ,     a       :	str=16       ,     a       :	str=4_096       ,     a       :	Any="gelu"       ,     a       :	Any=0.1       ,     a       :	Optional[int]=0.1       ,     a       :	Optional[int]=512       ,     a       :	Optional[Any]=2       ,     a       :	List[str]=0.02       ,     a       :	List[Any]=1E-1_2       ,     a       :	Any=0       ,     a       :	List[Any]="absolute"       ,     a       :	List[str]=True       ,     **a       :	Optional[Any]       ,     )			->					List[str]:
										super().__init__(pad_token_id=a       ,     **a			)

										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					max_position_embeddings
										SCREAMING_SNAKE_CASE           =					type_vocab_size
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					position_embedding_type
										SCREAMING_SNAKE_CASE           =					use_cache






