'''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					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   PreTrainedTokenizer    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Optional[int] = None    ,					):


	"""simple docstring"""
	__UpperCAmelCase							=			{}

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

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

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

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

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

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

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

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

	return train_ds, val_ds, test_ds, labelaid


__lowerCAmelCase	:       List[Any]						       =	logging.getLogger(__name__)






@dataclass
class 						A   :
							a_        =      field(metadata={'''help''': '''Which column contains the label'''}   )
							a_        =      field(default=UpperCAmelCase       ,	metadata={'''help''': '''The path of the training file'''}   )
							a_        =      field(default=UpperCAmelCase       ,	metadata={'''help''': '''The path of the development file'''}   )
							a_        =      field(default=UpperCAmelCase       ,	metadata={'''help''': '''The path of the test file'''}   )
							a_        =      field(
							    default=1_2_8       ,	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=UpperCAmelCase       ,	metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )






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





def       lowerCAmelCase					(		):


	"""simple docstring"""
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.
	__UpperCAmelCase							=			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
	__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			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.

	__UpperCAmelCase							=			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    ,					)

	__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			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    ,					)

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

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

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


def       lowerCAmelCase					(		UpperCamelCase__				:   int       ):


	"""simple docstring"""

	if not isinstance(UpperCamelCase__    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			f"""Input value of [number={number}] must be an integer"""
		raise TypeError(UpperCamelCase__       )

	if number < 1:
		__UpperCAmelCase							=			f"""Input value of [number={number}] must be > 0"""
		raise ValueError(UpperCamelCase__       )

	__UpperCAmelCase							=			1

	for i in range(1    ,					UpperCamelCase__       ):
		current_number *= 4 * i - 2
		current_number //= i + 1

	return current_number


if __name__ == "__main__":
					import doctest

					doctest.testmod()
