'''simple docstring'''


def       lowerCAmelCase					(		UpperCamelCase__				:   int    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	if index == r:
		for j in range(UpperCamelCase__       ):
			print(data[j]    ,					end=''' '''       )
		print(''' '''       )
		return
	#  When no more elements are there to put in data[]
	if i >= n:
		return
	# current is included, put next at next location
	__UpperCAmelCase							=			arr[i]
	combination_util(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					index + 1    ,					UpperCamelCase__    ,					i + 1       )
	# current is excluded, replace it with
	# next (Note that i+1 is passed, but
	# index is not changed)
	combination_util(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					i + 1       )
	# The main function that prints all combinations
	# of size r in arr[] of size n. This function
	# mainly uses combinationUtil()





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	# A temporary array to store all combination one by one
	__UpperCAmelCase							=			[0] * r
	# Print all combination using temporary array 'data[]'
	combination_util(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					0    ,					UpperCamelCase__    ,					0       )


if __name__ == "__main__":
					# Driver code to check the function above
					__lowerCAmelCase	:       List[str]						       =	[10, 20, 30, 40, 50]
					print_combination(arr, len(arr), 3)
					# This code is contributed by Ambuj sahu

'''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()
