


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  UpperCamelCase					(	__magic_name__     :							List[Any]    ,    __magic_name__     :							Union[str, Any]    ,    __magic_name__     :							Tuple    ,    __magic_name__     :							int    ,    __magic_name__     :							Union[str, Any]    ,    __magic_name__     :							List[Any] = None    ,    )   ->  int:
		"""simple docstring"""




		lowercase__     			=		{}

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

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

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

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

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

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

		lowercase__     			=		(
		    tf.data.Dataset.from_generator(
		        _lowerCAmelCase    ,    ({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:
				lowercase__     			=		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]     )     )     )

		lowercase__     			=		(
		    tf.data.Dataset.from_generator(
		        _lowerCAmelCase    ,    ({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:
				lowercase__     			=		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]     )     )     )

		lowercase__     			=		(
		    tf.data.Dataset.from_generator(
		        _lowerCAmelCase    ,    ({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:
				lowercase__     			=		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]     )     )     )

		return train_ds, val_ds, test_ds, labelaid


A       :		Tuple    =   logging.getLogger(__name__)



@dataclass
class 							A    :
					'''simple docstring'''






					A__			= field(metadata={'''help''': '''Which column contains the label'''}							)
					A__			= field(default=__snake_case ,  metadata={'''help''': '''The path of the training file'''}							)
					A__			= field(default=__snake_case ,  metadata={'''help''': '''The path of the development file'''}							)
					A__			= field(default=__snake_case ,  metadata={'''help''': '''The path of the test file'''}							)
					A__			= 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.'''
					        )
					    } ,  )
					A__			= field(
					    default=__snake_case ,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}							)






@dataclass
class 							A    :
					'''simple docstring'''






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






def  UpperCamelCase					(	)   ->  Tuple:
		"""simple docstring"""




		lowercase__     			=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
		lowercase__     			=		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.

		lowercase__     			=		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    ,    )

		lowercase__     			=		get_tfds(
		    train_file=data_args.train_file    ,    eval_file=data_args.dev_file    ,    test_file=data_args.test_file    ,    tokenizer=_lowerCAmelCase    ,    label_column_id=data_args.label_column_id    ,    max_seq_length=data_args.max_seq_length    ,    )

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

		with training_args.strategy.scope():
				lowercase__     			=		TFAutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path    ,    from_pt=bool(""".bin""" in model_args.model_name_or_path     )    ,    config=_lowerCAmelCase    ,    cache_dir=model_args.cache_dir    ,    )

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

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

		# Initialize our Trainer
		lowercase__     			=		TFTrainer(
		    model=_lowerCAmelCase    ,    args=_lowerCAmelCase    ,    train_dataset=_lowerCAmelCase    ,    eval_dataset=_lowerCAmelCase    ,    compute_metrics=_lowerCAmelCase    ,    )

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

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

				with open(_lowerCAmelCase    ,    """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(_lowerCAmelCase     )

		return results


if __name__ == "__main__":
	main()








from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase       :     Optional[int]   =     {"""configuration_mmbt""": ["""MMBTConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[Any]   =     ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""]


if TYPE_CHECKING:
    from .configuration_mmbt import MMBTConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
    import sys

    __lowerCamelCase       :     int   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
