

"""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    ( A_		,	A_		,	A_		,	A_		,	A_		,	A_ = None		,	):
	lowerCAmelCase__      :		str    							=      {}

	if train_file is not None:
		lowerCAmelCase__      :		int    							=      [train_file]
	if eval_file is not None:
		lowerCAmelCase__      :		Union[str, Any]    							=      [eval_file]
	if test_file is not None:
		lowerCAmelCase__      :		int    							=      [test_file]

	lowerCAmelCase__      :		Tuple    							=      datasets.load_dataset('''csv'''		,	data_files=A_					)
	lowerCAmelCase__      :		Union[str, Any]    							=      list(ds[list(files.keys()					)[0]].features.keys()					)
	lowerCAmelCase__      :		int    							=      features_name.pop(A_					)
	lowerCAmelCase__      :		List[Any]    							=      list(set(ds[list(files.keys()					)[0]][label_name]					)					)
	lowerCAmelCase__      :		Tuple    							=      {label: i for i, label in enumerate(A_					)}
	lowerCAmelCase__      :		Dict    							=      tokenizer.model_input_names
	lowerCAmelCase__      :		int    							=      {}

	if len(A_					) == 1:
		for k in files.keys():
			lowerCAmelCase__      :		Dict    							=      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():
			lowerCAmelCase__      :		Any    							=      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]:
			lowerCAmelCase__      :		List[Any]    							=      {k: v for k, v in ex.items() if k in input_names}
			lowerCAmelCase__      :		Optional[Any]    							=      labelaid[ex[label_name]]
			yield (d, label)

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

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

	lowerCAmelCase__      :		Union[str, Any]    							=      (
	    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:
		lowerCAmelCase__      :		Optional[Any]    							=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]					)					)					)

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

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

	return train_ds, val_ds, test_ds, labelaid


__UpperCamelCase    : Optional[Any]				 =						logging.getLogger(__name__)







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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







@dataclass
class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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

def     __SCREAMING_SNAKE_CASE    ( ):
	# 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.
	lowerCAmelCase__      :		Dict    							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)					)
	lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      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__      :		Optional[Any]    							=      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__      :		str    							=      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		,	)

	lowerCAmelCase__      :		Optional[Any]    							=      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():
		lowerCAmelCase__      :		Dict    							=      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_					) -> Dict:
		lowerCAmelCase__      :		List[Any]    							=      np.argmax(p.predictions		,	axis=1					)

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

	# Initialize our Trainer
	lowerCAmelCase__      :		Tuple    							=      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
	lowerCAmelCase__      :		List[Any]    							=      {}
	if training_args.do_eval:
		logger.info('''*** Evaluate ***'''					)
		lowerCAmelCase__      :		str    							=      trainer.evaluate()
		lowerCAmelCase__      :		Union[str, Any]    							=      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()







"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number < 0 or shift_amount < 0:
		raise ValueError('''both inputs must be positive integers'''					)

	lowerCAmelCase__      :		int    							=      str(bin(A_					)					)
	binary_number += "0" * shift_amount
	return binary_number

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number < 0 or shift_amount < 0:
		raise ValueError('''both inputs must be positive integers'''					)

	lowerCAmelCase__      :		Any    							=      str(bin(A_					)					)[2:]
	if shift_amount >= len(A_					):
		return "0b0"
	lowerCAmelCase__      :		Tuple    							=      binary_number[: len(A_					) - shift_amount]
	return "0b" + shifted_binary_number

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number >= 0:  # Get binary representation of positive number
		lowerCAmelCase__      :		Union[str, Any]    							=      '''0''' + str(bin(A_					)					).strip('''-'''					)[2:]
	else:  # Get binary (2's complement) representation of negative number
		lowerCAmelCase__      :		Optional[Any]    							=      len(bin(A_					)[3:]					)  # Find 2's complement of number
		lowerCAmelCase__      :		Optional[int]    							=      bin(abs(A_					) - (1 << binary_number_length)					)[3:]
		lowerCAmelCase__      :		int    							=      (
		    '''1''' + '''0''' * (binary_number_length - len(A_					)) + binary_number
		)

	if shift_amount >= len(A_					):
		return "0b" + binary_number[0] * len(A_					)
	return (
	    "0b"
	    + binary_number[0] * shift_amount
	    + binary_number[: len(A_					) - shift_amount]
	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




