






"""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 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__      ,A__ = None      ,):
			UpperCAmelCase_	:					int     	=   {}

			if train_file is not None:
						UpperCAmelCase_	:					Tuple     	=   [train_file]
			if eval_file is not None:
						UpperCAmelCase_	:					Union[str, Any]     	=   [eval_file]
			if test_file is not None:
						UpperCAmelCase_	:					Tuple     	=   [test_file]

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

			if len(A__		) == 1:
						for k in files.keys():
									UpperCAmelCase_	:					Any     	=   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():
									UpperCAmelCase_	:					Dict     	=   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]:
									UpperCAmelCase_	:					Union[str, Any]     	=   {k: v for k, v in ex.items() if k in input_names}
									UpperCAmelCase_	:					Any     	=   labelaid[ex[label_name]]
									yield (d, label)

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

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

			UpperCAmelCase_	:					List[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:
						UpperCAmelCase_	:					Optional[Any]     	=   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

			UpperCAmelCase_	:					Optional[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.VALIDATION in transformed_ds
			    else None
			)

			if val_ds is not None:
						UpperCAmelCase_	:					Optional[Any]     	=   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

			UpperCAmelCase_	:					Dict     	=   (
			    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:
						UpperCAmelCase_	:					List[Any]     	=   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]		)		)		)

			return train_ds, val_ds, test_ds, labelaid


lowerCamelCase_							      = logging.getLogger(__name__)







@dataclass
class      UpperCamelCase_			:
	__magic_name__       =							field(metadata={'''help''': '''Which column contains the label'''}	)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''The path of the training file'''}	)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''The path of the development file'''}	)
	__magic_name__       =							field(default=__A							,		metadata={'''help''': '''The path of the test file'''}	)
	__magic_name__       =							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.'''
	        )
	    }							,		)
	__magic_name__       =							field(
	    default=__A							,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)







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





def 							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.
			UpperCAmelCase_	:					Dict     	=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
			UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   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_	:					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      ,)

			UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   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      ,)

			UpperCAmelCase_	:					Optional[int]     	=   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():
						UpperCAmelCase_	:					Any     	=   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:
						UpperCAmelCase_	:					Any     	=   np.argmax(p.predictions      ,axis=1		)

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

			# Initialize our Trainer
			UpperCAmelCase_	:					Optional[int]     	=   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
			UpperCAmelCase_	:					List[str]     	=   {}
			if training_args.do_eval:
						logger.info("*** Evaluate ***"		)
						UpperCAmelCase_	:					str     	=   trainer.evaluate()
						UpperCAmelCase_	:					Tuple     	=   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"""
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase_							      = logging.getLogger()

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   argparse.ArgumentParser()
			parser.add_argument("-f"		)
			UpperCAmelCase_	:					Optional[Any]     	=   parser.parse_args()
			return args.f







class      UpperCamelCase_			(__A	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     None:
				UpperCAmelCase_	:					List[str]     	=   logging.StreamHandler(sys.stdout		)
				logger.addHandler(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					List[str]		)		->     Tuple:
				UpperCAmelCase_	:					Optional[int]     	=   get_gpu_count()

				if n_gpu > 1:
							pass
							# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
							# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
							# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
							# cmd = [sys.executable] + distributed_args + args
							# execute_subprocess_async(cmd, env=self.get_env())
							# XXX: test the results - need to save them first into .json file
				else:
							args.insert(0      ,      "run_glue_deebert.py"		)
							with patch.object(lowerCAmelCase_      ,      "argv"      ,      lowerCAmelCase_		):
										UpperCAmelCase_	:					Dict     	=   run_glue_deebert.main()
										for value in result.values():
													self.assertGreaterEqual(lowerCAmelCase_      ,      0.6_6_6		)





	@slow
	@require_torch_non_multi_gpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     int:
				UpperCAmelCase_	:					Tuple     	=   "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
				self.run_and_check(lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
				self.run_and_check(lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
				self.run_and_check(lowerCAmelCase_		)



