"""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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Any    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Any    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: List[str]    ,_lowerCamelCase		: Tuple = None    ,) ->   Any:
		_lowerCAmelCase		: str						    =  {}

		if train_file is not None:
				_lowerCAmelCase		: str						    =  [train_file]
		if eval_file is not None:
				_lowerCAmelCase		: List[str]						    =  [eval_file]
		if test_file is not None:
				_lowerCAmelCase		: Optional[Any]						    =  [test_file]

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

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

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

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

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

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

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

		_lowerCAmelCase		: Union[str, Any]						    =  (
		    tf.data.Dataset.from_generator(
		        UpperCAmelCase__    ,({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:
				_lowerCAmelCase		: Optional[Any]						    =  test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]			)			)			)

		return train_ds, val_ds, test_ds, labelaid


_a   :	int 					=     logging.getLogger(__name__)
@dataclass
class 		__A							:
					_UpperCamelCase							:    Tuple	      =	field(metadata={"help": "Which column contains the label"}	)
					_UpperCamelCase							:    Optional[Any]	      =	field(default=_a					,			metadata={"help": "The path of the training file"}	)
					_UpperCamelCase							:    str	      =	field(default=_a					,			metadata={"help": "The path of the development file"}	)
					_UpperCamelCase							:    List[str]	      =	field(default=_a					,			metadata={"help": "The path of the test file"}	)
					_UpperCamelCase							:    str	      =	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							:    str	      =	field(
					    default=_a					,			metadata={"help": "Overwrite the cached training and evaluation sets"}	)







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







def 							SCREAMING_SNAKE_CASE  (    ) ->   Union[str, Any]:
		_lowerCAmelCase		: Optional[int]						    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)			)
		_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  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.

		_lowerCAmelCase		: int						    =  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    ,)

		_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  get_tfds(
		    train_file=data_args.train_file    ,eval_file=data_args.dev_file    ,test_file=data_args.test_file    ,tokenizer=UpperCAmelCase__    ,label_column_id=data_args.label_column_id    ,max_seq_length=data_args.max_seq_length    ,)

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

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

		def compute_metrics(_lowerCamelCase		: List[Any]			) -> Dict:
				_lowerCAmelCase		: Optional[int]						    =  np.argmax(p.predictions    ,axis=1			)

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

		# Initialize our Trainer
		_lowerCAmelCase		: List[str]						    =  TFTrainer(
		    model=UpperCAmelCase__    ,args=UpperCAmelCase__    ,train_dataset=UpperCAmelCase__    ,eval_dataset=UpperCAmelCase__    ,compute_metrics=UpperCAmelCase__    ,)

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

		# Evaluation
		_lowerCAmelCase		: Dict						    =  {}
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""			)
				_lowerCAmelCase		: List[str]						    =  trainer.evaluate()
				_lowerCAmelCase		: str						    =  os.path.join(training_args.output_dir    ,"""eval_results.txt"""			)

				with open(UpperCAmelCase__    ,"""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(UpperCAmelCase__			)

		return results


if __name__ == "__main__":
					main()





"""simple docstring"""




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)




