






import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

a__       =     logging.getLogger(__name__)


@dataclass
class    _lowerCAmelCase			:



			"""simple docstring"""



			_lowercase				:   Dict      =  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.'''
			        )
			    }     ,      )
			_lowercase				:   List[str]      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}      )
			_lowercase				:   Any      =  field(
			    default=__lowerCamelCase     ,      metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    }     ,      )
			_lowercase				:   int      =  field(
			    default=__lowerCamelCase     ,      metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }     ,      )
			_lowercase				:   Optional[Any]      =  field(
			    default=__lowerCamelCase     ,      metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }     ,      )
			_lowercase				:   Optional[Any]      =  field(
			    default=__lowerCamelCase     ,      metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
			            '''value if set.'''
			        )
			    }     ,      )


@dataclass
class    _lowerCAmelCase			:



			"""simple docstring"""



			_lowercase				:   Union[str, Any]      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
			_lowercase				:   Any      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}      )
			_lowercase				:   int      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Train language if it is different from the evaluation language.'''}      )
			_lowercase				:   List[str]      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
			_lowercase				:   Union[str, Any]      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}      )
			_lowercase				:   str      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,      )
			_lowercase				:   Union[str, Any]      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}     ,      )
			_lowercase				:   Union[str, Any]      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}     ,      )
			_lowercase				:   Optional[Any]      =  field(
			    default='''main'''     ,      metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}     ,      )
			_lowercase				:   List[Any]      =  field(
			    default=__lowerCamelCase     ,      metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }     ,      )
			_lowercase				:   str      =  field(
			    default=__lowerCamelCase     ,      metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}     ,      )


def _UpperCAmelCase   (	):
							# 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.

							snake_case__	  =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
							snake_case__	  =       parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry("""run_xnli"""  ,					lowerCamelCase_ )

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,					datefmt="""%m/%d/%Y %H:%M:%S"""  ,					handlers=[logging.StreamHandler(sys.stdout )]  ,					)

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							snake_case__	  =       training_args.get_process_log_level()
							logger.setLevel(lowerCamelCase_ )
							datasets.utils.logging.set_verbosity(lowerCamelCase_ )
							transformers.utils.logging.set_verbosity(lowerCamelCase_ )
							transformers.utils.logging.enable_default_handler()
							transformers.utils.logging.enable_explicit_format()

							# Log on each process the small summary:
							logger.warning(
							    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
							    + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
							logger.info(F'''Training/evaluation parameters {training_args}''' )

							# Detecting last checkpoint.
							snake_case__	  =       None
							if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
														snake_case__	  =       get_last_checkpoint(training_args.output_dir )
														if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
																					raise ValueError(
																					    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																					    """Use --overwrite_output_dir to overcome.""" )
														elif last_checkpoint is not None:
																					logger.info(
																					    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																					    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Set seed before initializing model.
							set_seed(training_args.seed )

							# In distributed training, the load_dataset function guarantees that only one local process can concurrently
							# download the dataset.
							# Downloading and loading xnli dataset from the hub.
							if training_args.do_train:
														if model_args.train_language is None:
																					snake_case__	  =       load_dataset(
																					    """xnli"""  ,					model_args.language  ,					split="""train"""  ,					cache_dir=model_args.cache_dir  ,					use_auth_token=True if model_args.use_auth_token else None  ,					)
														else:
																					snake_case__	  =       load_dataset(
																					    """xnli"""  ,					model_args.train_language  ,					split="""train"""  ,					cache_dir=model_args.cache_dir  ,					use_auth_token=True if model_args.use_auth_token else None  ,					)
														snake_case__	  =       train_dataset.features['label'].names

							if training_args.do_eval:
														snake_case__	  =       load_dataset(
														    """xnli"""  ,					model_args.language  ,					split="""validation"""  ,					cache_dir=model_args.cache_dir  ,					use_auth_token=True if model_args.use_auth_token else None  ,					)
														snake_case__	  =       eval_dataset.features['label'].names

							if training_args.do_predict:
														snake_case__	  =       load_dataset(
														    """xnli"""  ,					model_args.language  ,					split="""test"""  ,					cache_dir=model_args.cache_dir  ,					use_auth_token=True if model_args.use_auth_token else None  ,					)
														snake_case__	  =       predict_dataset.features['label'].names

							# Labels
							snake_case__	  =       len(lowerCamelCase_ )

							# Load pretrained model and tokenizer
							# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							snake_case__	  =       AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,					num_labels=lowerCamelCase_  ,					idalabel={str(lowerCamelCase_ ): label for i, label in enumerate(lowerCamelCase_ )}  ,					labelaid={label: i for i, label in enumerate(lowerCamelCase_ )}  ,					finetuning_task="""xnli"""  ,					cache_dir=model_args.cache_dir  ,					revision=model_args.model_revision  ,					use_auth_token=True if model_args.use_auth_token else None  ,					)
							snake_case__	  =       AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,					do_lower_case=model_args.do_lower_case  ,					cache_dir=model_args.cache_dir  ,					use_fast=model_args.use_fast_tokenizer  ,					revision=model_args.model_revision  ,					use_auth_token=True if model_args.use_auth_token else None  ,					)
							snake_case__	  =       AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path  ,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path )  ,					config=lowerCamelCase_  ,					cache_dir=model_args.cache_dir  ,					revision=model_args.model_revision  ,					use_auth_token=True if model_args.use_auth_token else None  ,					ignore_mismatched_sizes=model_args.ignore_mismatched_sizes  ,					)

							# Preprocessing the datasets
							# Padding strategy
							if data_args.pad_to_max_length:
														snake_case__	  =       'max_length'
							else:
														# We will pad later, dynamically at batch creation, to the max sequence length in each batch
														snake_case__	  =       False

							def preprocess_function(a						:	List[Any] ):
														# Tokenize the texts
														return tokenizer(
														    examples["""premise"""]  ,					examples["""hypothesis"""]  ,					padding=lowerCamelCase_  ,					max_length=data_args.max_seq_length  ,					truncation=lowerCamelCase_  ,					)

							if training_args.do_train:
														if data_args.max_train_samples is not None:
																					snake_case__	  =       min(len(lowerCamelCase_ )  ,					data_args.max_train_samples )
																					snake_case__	  =       train_dataset.select(range(lowerCamelCase_ ) )
														with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
																					snake_case__	  =       train_dataset.map(
																					    lowerCamelCase_  ,					batched=lowerCamelCase_  ,					load_from_cache_file=not data_args.overwrite_cache  ,					desc="""Running tokenizer on train dataset"""  ,					)
														# Log a few random samples from the training set:
														for index in random.sample(range(len(lowerCamelCase_ ) )  ,					3 ):
																					logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )

							if training_args.do_eval:
														if data_args.max_eval_samples is not None:
																					snake_case__	  =       min(len(lowerCamelCase_ )  ,					data_args.max_eval_samples )
																					snake_case__	  =       eval_dataset.select(range(lowerCamelCase_ ) )
														with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
																					snake_case__	  =       eval_dataset.map(
																					    lowerCamelCase_  ,					batched=lowerCamelCase_  ,					load_from_cache_file=not data_args.overwrite_cache  ,					desc="""Running tokenizer on validation dataset"""  ,					)

							if training_args.do_predict:
														if data_args.max_predict_samples is not None:
																					snake_case__	  =       min(len(lowerCamelCase_ )  ,					data_args.max_predict_samples )
																					snake_case__	  =       predict_dataset.select(range(lowerCamelCase_ ) )
														with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
																					snake_case__	  =       predict_dataset.map(
																					    lowerCamelCase_  ,					batched=lowerCamelCase_  ,					load_from_cache_file=not data_args.overwrite_cache  ,					desc="""Running tokenizer on prediction dataset"""  ,					)

    # Get the metric function
							snake_case__	  =       evaluate.load("""xnli""" )

							# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
							# predictions and label_ids field) and has to return a dictionary string to float.
							def compute_metrics(a						:	List[str] ):
														snake_case__	  =       p.predictions[0] if isinstance(p.predictions  ,					lowerCamelCase_ ) else p.predictions
														snake_case__	  =       np.argmax(lowerCamelCase_  ,					axis=1 )
														return metric.compute(predictions=lowerCamelCase_  ,					references=p.label_ids )

							# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
							if data_args.pad_to_max_length:
														snake_case__	  =       default_data_collator
							elif training_args.fpaa:
														snake_case__	  =       DataCollatorWithPadding(lowerCamelCase_  ,					pad_to_multiple_of=8 )
							else:
														snake_case__	  =       None

							# Initialize our Trainer
							snake_case__	  =       Trainer(
							    model=lowerCamelCase_  ,					args=lowerCamelCase_  ,					train_dataset=train_dataset if training_args.do_train else None  ,					eval_dataset=eval_dataset if training_args.do_eval else None  ,					compute_metrics=lowerCamelCase_  ,					tokenizer=lowerCamelCase_  ,					data_collator=lowerCamelCase_  ,					)

							# Training
							if training_args.do_train:
														snake_case__	  =       None
														if training_args.resume_from_checkpoint is not None:
																					snake_case__	  =       training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					snake_case__	  =       last_checkpoint
														snake_case__	  =       trainer.train(resume_from_checkpoint=lowerCamelCase_ )
														snake_case__	  =       train_result.metrics
														snake_case__	  =       (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase_ )
														)
														snake_case__	  =       min(lowerCamelCase_  ,					len(lowerCamelCase_ ) )

														trainer.save_model()  # Saves the tokenizer too for easy upload

														trainer.log_metrics("""train"""  ,					lowerCamelCase_ )
														trainer.save_metrics("""train"""  ,					lowerCamelCase_ )
														trainer.save_state()

							# Evaluation
							if training_args.do_eval:
														logger.info("""*** Evaluate ***""" )
														snake_case__	  =       trainer.evaluate(eval_dataset=lowerCamelCase_ )

														snake_case__	  =       data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_ )
														snake_case__	  =       min(lowerCamelCase_  ,					len(lowerCamelCase_ ) )

														trainer.log_metrics("""eval"""  ,					lowerCamelCase_ )
														trainer.save_metrics("""eval"""  ,					lowerCamelCase_ )

							# Prediction
							if training_args.do_predict:
														logger.info("""*** Predict ***""" )
														snake_case__	  =       trainer.predict(lowerCamelCase_  ,					metric_key_prefix="""predict""" )

														snake_case__	  =       (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase_ )
														)
														snake_case__	  =       min(lowerCamelCase_  ,					len(lowerCamelCase_ ) )

														trainer.log_metrics("""predict"""  ,					lowerCamelCase_ )
														trainer.save_metrics("""predict"""  ,					lowerCamelCase_ )

														snake_case__	  =       np.argmax(lowerCamelCase_  ,					axis=1 )
														snake_case__	  =       os.path.join(training_args.output_dir  ,					"""predictions.txt""" )
														if trainer.is_world_process_zero():
																					with open(lowerCamelCase_  ,					"""w""" ) as writer:
																												writer.write("""index\tprediction\n""" )
																												for index, item in enumerate(lowerCamelCase_ ):
																																			snake_case__	  =       label_list[item]
																																			writer.write(F'''{index}\t{item}\n''' )


if __name__ == "__main__":
	main()








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)

a__       =     logging.getLogger()

def _UpperCAmelCase   (	):
							snake_case__	  =       argparse.ArgumentParser()
							parser.add_argument("""-f""" )
							snake_case__	  =       parser.parse_args()
							return args.f



class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       logging.StreamHandler(sys.stdout)
											logger.addHandler(UpperCamelCase__)



				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       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(UpperCamelCase__			,		"""argv"""			,		UpperCamelCase__):
																									snake_case__	  =       run_glue_deebert.main()
																									for value in result.values():
																																self.assertGreaterEqual(UpperCamelCase__			,		0.6_66)



				@slow
				@require_torch_non_multi_gpu
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
											self.run_and_check(UpperCamelCase__)

											snake_case__	  =       """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
											self.run_and_check(UpperCamelCase__)

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