

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__      ( __A    :str	,__A    :str	,__A    :str	,__A    :PreTrainedTokenizer	,__A    :int	,__A    :Optional[int] = None	,):


       """simple docstring"""




       __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=__A		)
       __snake_case       	=							list(ds[list(files.keys()		)[0]].features.keys()		)
       __snake_case       	=							features_name.pop(__A		)
       __snake_case       	=							list(set(ds[list(files.keys()		)[0]][label_name]		)		)
       __snake_case       	=							{label: i for i, label in enumerate(__A		)}
       __snake_case       	=							tokenizer.model_input_names
       __snake_case       	=							{}

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

       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(
               __A	,({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(
               __A	,({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(
               __A	,({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


UpperCamelCase__		 =     logging.getLogger(__name__)

@dataclass
class     __snake_case    :
       """simple docstring"""







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






@dataclass
class     __snake_case    :
       """simple docstring"""







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







def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __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=__A	,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(__A		)	,labelaid=__A	,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=__A	,cache_dir=model_args.cache_dir	,)

       def compute_metrics(__A    :EvalPrediction		) -> Dict:
              __snake_case       	=							np.argmax(p.predictions	,axis=1		)

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

       # Initialize our Trainer
       __snake_case       	=							TFTrainer(
           model=__A	,args=__A	,train_dataset=__A	,eval_dataset=__A	,compute_metrics=__A	,)

       # 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(__A	,"""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(__A		)

       return results


if __name__ == "__main__":
    main()




import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCamelCase__		 =     get_logger(__name__)

class     __snake_case    (				enum.Enum  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							"all_checks"
       __SCREAMING_SNAKE_CASE				    =							"basic_checks"
       __SCREAMING_SNAKE_CASE				    =							"no_checks"






class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""














def 						lowerCamelCase__      ( __A    :Optional[dict]	,__A    :dict	,__A    :Any=None		):


       """simple docstring"""




       if expected_checksums is None:
              logger.info("""Unable to verify checksums."""		)
              return
       if len(set(__A		) - set(__A		)		) > 0:
              raise ExpectedMoreDownloadedFiles(str(set(__A		) - set(__A		)		)		)
       if len(set(__A		) - set(__A		)		) > 0:
              raise UnexpectedDownloadedFile(str(set(__A		) - set(__A		)		)		)
       __snake_case       	=							[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
       __snake_case       	=							""" for """ + verification_name if verification_name is not None else """"""
       if len(__A		) > 0:
              raise NonMatchingChecksumError(
                  F'Checksums didn\'t match{for_verification_name}:\n'
                  F'{bad_urls}\n'
                  """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""		)
       logger.info("""All the checksums matched successfully""" + for_verification_name		)

class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""














def 						lowerCamelCase__      ( __A    :Optional[dict]	,__A    :dict		):


       """simple docstring"""




       if expected_splits is None:
              logger.info("""Unable to verify splits sizes."""		)
              return
       if len(set(__A		) - set(__A		)		) > 0:
              raise ExpectedMoreSplits(str(set(__A		) - set(__A		)		)		)
       if len(set(__A		) - set(__A		)		) > 0:
              raise UnexpectedSplits(str(set(__A		) - set(__A		)		)		)
       __snake_case       	=							[
           {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
           for name in expected_splits
           if expected_splits[name].num_examples != recorded_splits[name].num_examples
       ]
       if len(__A		) > 0:
              raise NonMatchingSplitsSizesError(str(__A		)		)
       logger.info("""All the splits matched successfully."""		)







def 						lowerCamelCase__      ( __A    :str	,__A    :bool = True		):


       """simple docstring"""




       if record_checksum:
              __snake_case       	=							shaaaa()
              with open(__A	,"""rb"""		) as f:
                     for chunk in iter(lambda: f.read(1 << 2_0		)	,b""""""		):
                            m.update(__A		)
                     __snake_case       	=							m.hexdigest()
       else:
              __snake_case       	=							None
       return {"num_bytes": os.path.getsize(__A		), "checksum": checksum}







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       if dataset_size and config.IN_MEMORY_MAX_SIZE:
              return dataset_size < config.IN_MEMORY_MAX_SIZE
       else:
              return False

