


'''simple docstring'''

import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowercase__				     =		logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm
   import torch_xla.debug.metrics as met





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def __init__(			self     ,							*__UpperCamelCase     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    int:
          super().__init__(*__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		eval_examples
          _a						=		post_process_function
          _a						=		quant_trainer_args
          _a						=		128  # default number of calibration samples






     def  a_					(			self     ,							__UpperCamelCase=None		)       ->    List[Any]:
          if calib_dataset is None and self.calib_dataset is None:
               raise ValueError("Trainer: calibration requires an calib_dataset."		)
          _a						=		calib_dataset if calib_dataset is not None else self.calib_dataset

          _a						=		self._remove_unused_columns(__UpperCamelCase     ,							description="Calibration"		)

          return DataLoader(
              __UpperCamelCase     ,							batch_size=self.args.eval_batch_size     ,							collate_fn=self.data_collator     ,							drop_last=self.args.dataloader_drop_last     ,							num_workers=self.args.dataloader_num_workers     ,							pin_memory=self.args.dataloader_pin_memory     ,							shuffle=__UpperCamelCase     ,							)






     def  a_					(			self     ,							__UpperCamelCase=None		)       ->    List[str]:
          _a						=		self.train_dataset if calib_dataset is None else calib_dataset
          _a						=		self.get_calib_dataloader(__UpperCamelCase		)

          _a						=		self.model
          quant_trainer.configure_model(__UpperCamelCase     ,							self.quant_trainer_args     ,							calib=__UpperCamelCase		)
          model.eval()
          quant_trainer.enable_calibration(__UpperCamelCase		)

          logger.info("***** Running calibration *****"		)
          logger.info(f"  Num examples = {self.calib_num}"		)
          logger.info(f"  Batch size = {calib_dataloader.batch_size}"		)

          for step, inputs in enumerate(__UpperCamelCase		):
               # Prediction step
               _a			,			_a			,			_a						=		self.prediction_step(__UpperCamelCase     ,							__UpperCamelCase     ,							prediction_loss_only=__UpperCamelCase		)
               if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                    break

          quant_trainer.finish_calibration(__UpperCamelCase     ,							self.quant_trainer_args		)
          _a						=		model






     def  a_					(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase = "eval"		)       ->    str:
          _a						=		self.eval_dataset if eval_dataset is None else eval_dataset
          _a						=		self.get_eval_dataloader(__UpperCamelCase		)
          _a						=		self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          _a						=		self.compute_metrics
          _a						=		None
          _a						=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
               _a						=		eval_loop(
                   __UpperCamelCase     ,							description="Evaluation"     ,							prediction_loss_only=True if compute_metrics is None else None     ,							ignore_keys=__UpperCamelCase     ,							)
          finally:
               _a						=		compute_metrics

          if self.post_process_function is not None and self.compute_metrics is not None:
               _a						=		self.post_process_function(__UpperCamelCase     ,							__UpperCamelCase     ,							output.predictions		)
               _a						=		self.compute_metrics(__UpperCamelCase		)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()		):
                    if not key.startswith(f"{metric_key_prefix}_"		):
                         _a						=		metrics.pop(__UpperCamelCase		)

               self.log(__UpperCamelCase		)
          else:
               _a						=		{}

          if self.args.tpu_metrics_debug or self.args.debug:
               # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
               xm.master_print(met.metrics_report()		)

          _a						=		self.callback_handler.on_evaluate(self.args     ,							self.state     ,							self.control     ,							__UpperCamelCase		)
          return metrics






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=None     ,							__UpperCamelCase = "test"		)       ->    Tuple:
          _a						=		self.get_test_dataloader(__UpperCamelCase		)

          # Temporarily disable metric computation, we will do it in the loop here.
          _a						=		self.compute_metrics
          _a						=		None
          _a						=		self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
               _a						=		eval_loop(
                   __UpperCamelCase     ,							description="Prediction"     ,							prediction_loss_only=True if compute_metrics is None else None     ,							ignore_keys=__UpperCamelCase     ,							)
          finally:
               _a						=		compute_metrics

          if self.post_process_function is None or self.compute_metrics is None:
               return output

          _a						=		self.post_process_function(__UpperCamelCase     ,							__UpperCamelCase     ,							output.predictions     ,							"predict"		)
          _a						=		self.compute_metrics(__UpperCamelCase		)

          # Prefix all keys with metric_key_prefix + '_'
          for key in list(metrics.keys()		):
               if not key.startswith(f"{metric_key_prefix}_"		):
                    _a						=		metrics.pop(__UpperCamelCase		)

          return PredictionOutput(predictions=predictions.predictions     ,							label_ids=predictions.label_ids     ,							metrics=__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase="./"		)       ->    str:
          _a						=		self.eval_dataset
          _a						=		self.get_eval_dataloader(__UpperCamelCase		)

          _a						=		next(iter(__UpperCamelCase		)		)

          # saving device - to make it consistent
          _a						=		torch.device("cuda" if torch.cuda.is_available() else "cpu"		)

          # convert to tuple
          _a						=		tuple(v.to(__UpperCamelCase		) for k, v in batch.items()		)

          logger.info("Converting model to be onnx compatible"		)
          from pytorch_quantization.nn import TensorQuantizer

          _a						=		True

          _a						=		self.model.to(__UpperCamelCase		)

          model.eval()
          model.float()

          _a						=		model.module if hasattr(__UpperCamelCase     ,							"module"		) else model
          quant_trainer.configure_model(__UpperCamelCase     ,							self.quant_trainer_args		)

          _a						=		os.path.join(__UpperCamelCase     ,							"model.onnx"		)
          logger.info(f"exporting model to {output_model_file}"		)

          _a						=		{0: "batch_size", 1: "seq_len"}

          torch.onnx.export(
              __UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							export_params=__UpperCamelCase     ,							opset_version=13     ,							do_constant_folding=__UpperCamelCase     ,							input_names=["input_ids", "attention_mask", "token_type_ids"]     ,							output_names=["output_start_logits", "output_end_logits"]     ,							dynamic_axes={
                  "input_ids": axes,
                  "attention_mask": axes,
                  "token_type_ids": axes,
                  "output_start_logits": axes,
                  "output_end_logits": axes,
              }     ,							verbose=__UpperCamelCase     ,							)
          logger.info("onnx export finished"		)





'''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 __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: str      ,  __lowerCamelCase		: PreTrainedTokenizer      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: Optional[int] = None      ,  )      ->     Tuple:







     '''simple docstring'''






     _a						=		{}

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

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

     if len(__lowerCamelCase				) == 1:
          for k in files.keys():
               _a						=		ds[k].map(
                   lambda __lowerCamelCase				: 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():
               _a						=		ds[k].map(
                   lambda __lowerCamelCase				: 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]:
               _a						=		{k: v for k, v in ex.items() if k in input_names}
               _a						=		labelaid[ex[label_name]]
               yield (d, label)

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

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

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

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

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

     return train_ds, val_ds, test_ds, labelaid


lowercase__				     =		logging.getLogger(__name__)





@dataclass
class 						__SCREAMING_SNAKE_CASE					:
     UpperCAmelCase 					=				field(metadata={'''help''': '''Which column contains the label'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the training file'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the development file'''} )
     UpperCAmelCase 					=				field(default=lowerCamelCase__ ,			metadata={'''help''': '''The path of the test file'''} )
     UpperCAmelCase 					=				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 					=				field(
         default=lowerCamelCase__ ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







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







def __UpperCamelCase   (			)      ->     str:







     '''simple docstring'''






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

     _a						=		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      ,  )

     _a			,			_a			,			_a			,			_a						=		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      ,  )

     _a						=		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():
          _a						=		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(__lowerCamelCase		: EvalPrediction				) -> Dict:
          _a						=		np.argmax(p.predictions      ,  axis=1				)

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

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

