





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')

_lowerCamelCase 					=      logging.getLogger(__name__)




@dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       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."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={"help": "Overwrite the cached preprocessed datasets or not."}     )
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	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."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }					,	)
							lowerCamelCase_							     =       field(
							    default=UpperCamelCase__					,	metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
							            "value if set."
							        )
							    }					,	)




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






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.

		SCREAMING_SNAKE_CASE__								=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_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"""       , UpperCamelCase__     )

		# 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()

		SCREAMING_SNAKE_CASE__								=   training_args.get_process_log_level()
		logger.setLevel(UpperCamelCase__     )
		datasets.utils.logging.set_verbosity(UpperCamelCase__     )
		transformers.utils.logging.set_verbosity(UpperCamelCase__     )
		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.
		SCREAMING_SNAKE_CASE__								=   None
		if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
				SCREAMING_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:
						SCREAMING_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:
						SCREAMING_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       , )
				SCREAMING_SNAKE_CASE__								=   train_dataset.features["""label"""].names

		if training_args.do_eval:
				SCREAMING_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       , )
				SCREAMING_SNAKE_CASE__								=   eval_dataset.features["""label"""].names

		if training_args.do_predict:
				SCREAMING_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       , )
				SCREAMING_SNAKE_CASE__								=   predict_dataset.features["""label"""].names

		# Labels
		SCREAMING_SNAKE_CASE__								=   len(UpperCamelCase__     )

		# Load pretrained model and tokenizer
		# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		SCREAMING_SNAKE_CASE__								=   AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path       , num_labels=UpperCamelCase__       , idalabel={str(UpperCamelCase__     ): label for i, label in enumerate(UpperCamelCase__     )}       , labelaid={label: i for i, label in enumerate(UpperCamelCase__     )}       , 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       , )
		SCREAMING_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       , )
		SCREAMING_SNAKE_CASE__								=   AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path       , from_tf=bool(""".ckpt""" in model_args.model_name_or_path     )       , config=UpperCamelCase__       , 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:
				SCREAMING_SNAKE_CASE__								=   """max_length"""
		else:
				# We will pad later, dynamically at batch creation, to the max sequence length in each batch
				SCREAMING_SNAKE_CASE__								=   False

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

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						SCREAMING_SNAKE_CASE__								=   min(len(UpperCamelCase__     )       , data_args.max_train_samples     )
						SCREAMING_SNAKE_CASE__								=   train_dataset.select(range(UpperCamelCase__     )     )
				with training_args.main_process_first(desc="""train dataset map pre-processing"""     ):
						SCREAMING_SNAKE_CASE__								=   train_dataset.map(
						    UpperCamelCase__       , batched=UpperCamelCase__       , 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(UpperCamelCase__     )     )       , 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:
						SCREAMING_SNAKE_CASE__								=   min(len(UpperCamelCase__     )       , data_args.max_eval_samples     )
						SCREAMING_SNAKE_CASE__								=   eval_dataset.select(range(UpperCamelCase__     )     )
				with training_args.main_process_first(desc="""validation dataset map pre-processing"""     ):
						SCREAMING_SNAKE_CASE__								=   eval_dataset.map(
						    UpperCamelCase__       , batched=UpperCamelCase__       , 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:
						SCREAMING_SNAKE_CASE__								=   min(len(UpperCamelCase__     )       , data_args.max_predict_samples     )
						SCREAMING_SNAKE_CASE__								=   predict_dataset.select(range(UpperCamelCase__     )     )
				with training_args.main_process_first(desc="""prediction dataset map pre-processing"""     ):
						SCREAMING_SNAKE_CASE__								=   predict_dataset.map(
						    UpperCamelCase__       , batched=UpperCamelCase__       , load_from_cache_file=not data_args.overwrite_cache       , desc="""Running tokenizer on prediction dataset"""       , )

    # Get the metric function
		SCREAMING_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(UpperCamelCase__:				EvalPrediction     ):
				SCREAMING_SNAKE_CASE__								=   p.predictions[0] if isinstance(p.predictions       , UpperCamelCase__     ) else p.predictions
				SCREAMING_SNAKE_CASE__								=   np.argmax(UpperCamelCase__       , axis=1     )
				return metric.compute(predictions=UpperCamelCase__       , 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:
				SCREAMING_SNAKE_CASE__								=   default_data_collator
		elif training_args.fpaa:
				SCREAMING_SNAKE_CASE__								=   DataCollatorWithPadding(UpperCamelCase__       , pad_to_multiple_of=8     )
		else:
				SCREAMING_SNAKE_CASE__								=   None

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

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

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

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

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

				SCREAMING_SNAKE_CASE__								=   data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   min(UpperCamelCase__       , len(UpperCamelCase__     )     )

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

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

				SCREAMING_SNAKE_CASE__								=   (
				    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCamelCase__     )
				)
				SCREAMING_SNAKE_CASE__								=   min(UpperCamelCase__       , len(UpperCamelCase__     )     )

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

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


if __name__ == "__main__":
	main()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				float       , UpperCamelCase__:				float     ):
		if mass < 0:
				raise ValueError("""The mass of a body cannot be negative"""     )
		return 0.5 * mass * abs(UpperCamelCase__     ) * abs(UpperCamelCase__     )


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)