


"""simple docstring"""



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		(		lowercase						:						str		,							lowercase						:						str		,							lowercase						:						str		,							lowercase						:						PreTrainedTokenizer		,							lowercase						:						int		,							lowercase						:						Optional[int] = None		,							)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   Any         =					{}

    if train_file is not None:
        snake_case					:   List[str]         =					[train_file]
    if eval_file is not None:
        snake_case					:   List[Any]         =					[eval_file]
    if test_file is not None:
        snake_case					:   Tuple         =					[test_file]

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

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

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

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

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

    snake_case					:   Any         =					(
        tf.data.Dataset.from_generator(
            lowercase		,							({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					:   Dict         =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

    snake_case					:   Optional[int]         =					(
        tf.data.Dataset.from_generator(
            lowercase		,							({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					:   Dict         =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

    snake_case					:   int         =					(
        tf.data.Dataset.from_generator(
            lowercase		,							({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					:   str         =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

    return train_ds, val_ds, test_ds, labelaid


__snake_case         =					logging.getLogger(__name__)







@dataclass
class _lowerCAmelCase       :
   __UpperCAmelCase				: int													=						field(metadata={'''help''': '''Which column contains the label'''}			)
   __UpperCAmelCase				: str													=						field(default=snake_case_					,							metadata={'''help''': '''The path of the training file'''}			)
   __UpperCAmelCase				: Optional[str]													=						field(default=snake_case_					,							metadata={'''help''': '''The path of the development file'''}			)
   __UpperCAmelCase				: Optional[str]													=						field(default=snake_case_					,							metadata={'''help''': '''The path of the test file'''}			)
   __UpperCAmelCase				: 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.'''
           )
       }					,							)
   __UpperCAmelCase				: bool													=						field(
       default=snake_case_					,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)







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







def 						__lowerCAmelCase		(		)			->		Any:


    """simple docstring"""





    snake_case					:   Any         =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
    snake_case					:   List[Any]         =					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					:   str         =					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					:   Optional[int]         =					get_tfds(
        train_file=data_args.train_file		,							eval_file=data_args.dev_file		,							test_file=data_args.test_file		,							tokenizer=lowercase		,							label_column_id=data_args.label_column_id		,							max_seq_length=data_args.max_seq_length		,							)

    snake_case					:   Dict         =					AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path		,							num_labels=len(lowercase				)		,							labelaid=lowercase		,							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					:   Dict         =					TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path		,							from_pt=bool(".bin" in model_args.model_name_or_path				)		,							config=lowercase		,							cache_dir=model_args.cache_dir		,							)

    def compute_metrics(lowercase						:						EvalPrediction				) -> Dict:
        snake_case					:   Optional[Any]         =					np.argmax(p.predictions		,							axis=1				)

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

    # Initialize our Trainer
    snake_case					:   Optional[Any]         =					TFTrainer(
        model=lowercase		,							args=lowercase		,							train_dataset=lowercase		,							eval_dataset=lowercase		,							compute_metrics=lowercase		,							)

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

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

        with open(lowercase		,							"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(lowercase				)

    return results


if __name__ == "__main__":
  main()









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Union[str, Any]													=						'''xmod'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=1		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=2		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=("en_XX",)		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




       super().__init__(pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		)

       snake_case					:   Optional[int]         =					vocab_size
       snake_case					:   Optional[Any]         =					hidden_size
       snake_case					:   Union[str, Any]         =					num_hidden_layers
       snake_case					:   int         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Union[str, Any]         =					intermediate_size
       snake_case					:   str         =					hidden_dropout_prob
       snake_case					:   List[str]         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Union[str, Any]         =					type_vocab_size
       snake_case					:   Union[str, Any]         =					initializer_range
       snake_case					:   str         =					layer_norm_eps
       snake_case					:   Dict         =					position_embedding_type
       snake_case					:   Any         =					use_cache
       snake_case					:   str         =					classifier_dropout
       snake_case					:   Any         =					pre_norm
       snake_case					:   Union[str, Any]         =					adapter_reduction_factor
       snake_case					:   Optional[int]         =					adapter_layer_norm
       snake_case					:   int         =					adapter_reuse_layer_norm
       snake_case					:   List[Any]         =					ln_before_adapter
       snake_case					:   Tuple         =					list(UpperCamelCase__		)
       snake_case					:   Tuple         =					default_language







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   str         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Tuple         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





