



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")

UpperCAmelCase_							:      int      =  logging.getLogger(__name__)






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





def  UpperCamelCase						(       )->       str:





						"""simple docstring"""





						A__							=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
						A__						,       A__						,       A__							=       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"			,							_A    )

						# 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()

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

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

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

						# Labels
						A__							=       len(_A    )

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

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

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

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

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

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

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

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

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

												A__							=       data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_A    )
												A__							=       min(_A			,							len(_A    )    )

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

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

												A__							=       (
												    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_A    )
												)
												A__							=       min(_A			,							len(_A    )    )

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

												A__							=       np.argmax(_A			,							axis=1    )
												A__							=       os.path.join(training_args.output_dir			,							"predictions.txt"    )
												if trainer.is_world_process_zero():
																		with open(_A			,							"w"    ) as writer:
																								writer.write("index\tprediction\n"    )
																								for index, item in enumerate(_A    ):
																														A__							=       label_list[item]
																														writer.write(f"""{index}\t{item}\n"""    )


if __name__ == "__main__":
						main()










import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCAmelCase_							:      Optional[Any]      =  logging.get_logger(__name__)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[str]				=	"""linear"""
	lowerCAmelCase	:   int				=	"""cosine"""
	lowerCAmelCase	:   Dict				=	"""cosine_with_restarts"""
	lowerCAmelCase	:   Optional[Any]				=	"""polynomial"""
	lowerCAmelCase	:   Dict				=	"""constant"""
	lowerCAmelCase	:   Any				=	"""constant_with_warmup"""
	lowerCAmelCase	:   Union[str, Any]				=	"""piecewise_constant"""





def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						return LambdaLR(_A			,							lambda _A    : 1			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int = -1    )->       Optional[Any]:





						"""simple docstring"""





						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1.0			,							_A    )    )
												return 1.0

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				str			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						A__							=       {}
						A__							=       step_rules.split(","    )
						for rule_str in rule_list[:-1]:
												A__						,       A__							=       rule_str.split(":"    )
												A__							=       int(_A    )
												A__							=       float(_A    )
												A__							=       value
						A__							=       float(rule_list[-1]    )

						def create_rules_function(_A			:				Any			,							_A			:				Optional[int]    ):
												def rule_func(_A			:				int    ) -> float:
																		A__							=       sorted(rules_dict.keys()    )
																		for i, sorted_step in enumerate(_A    ):
																								if steps < sorted_step:
																														return rules_dict[sorted_steps[i]]
																		return last_lr_multiple

												return rule_func

						A__							=       create_rules_function(_A			,							_A    )

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Any			,							_A			:				Union[str, Any]			,							_A			:				str			,							_A			:				str=-1    )->       Tuple:





						"""simple docstring"""





						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												return max(
												    0.0			,							float(num_training_steps - current_step    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int			,							_A			:				float = 0.5			,							_A			:				int = -1    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       float(current_step - num_warmup_steps    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )
												return max(0.0			,							0.5 * (1.0 + math.cos(math.pi * float(_A    ) * 2.0 * progress    ))    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int			,							_A			:				int = 1			,							_A			:				int = -1    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       float(current_step - num_warmup_steps    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )
												if progress >= 1.0:
																		return 0.0
												return max(0.0			,							0.5 * (1.0 + math.cos(math.pi * ((float(_A    ) * progress) % 1.0)    ))    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Union[str, Any]			,							_A			:				List[str]			,							_A			:				Tuple=1E-7			,							_A			:				Dict=1.0			,							_A			:				Union[str, Any]=-1    )->       Any:





						"""simple docstring"""





						A__							=       optimizer.defaults["lr"]
						if not (lr_init > lr_end):
												raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""    )

						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												elif current_step > num_training_steps:
																		return lr_end / lr_init  # as LambdaLR multiplies by lr_init
												else:
																		A__							=       lr_init - lr_end
																		A__							=       num_training_steps - num_warmup_steps
																		A__							=       1 - (current_step - num_warmup_steps) / decay_steps
																		A__							=       lr_range * pct_remaining**power + lr_end
																		return decay / lr_init  # as LambdaLR multiplies by lr_init

						return LambdaLR(_A			,							_A			,							_A    )


UpperCAmelCase_							:      Any      =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def  UpperCamelCase						(       _A			:				Union[str, SchedulerType]			,							_A			:				Optimizer			,							_A			:				Optional[str] = None			,							_A			:				Optional[int] = None			,							_A			:				Optional[int] = None			,							_A			:				int = 1			,							_A			:				float = 1.0			,							_A			:				int = -1			,							)->       Union[str, Any]:





						"""simple docstring"""





						A__							=       SchedulerType(_A    )
						A__							=       TYPE_TO_SCHEDULER_FUNCTION[name]
						if name == SchedulerType.CONSTANT:
												return schedule_func(_A			,							last_epoch=_A    )

						if name == SchedulerType.PIECEWISE_CONSTANT:
												return schedule_func(_A			,							step_rules=_A			,							last_epoch=_A    )

						# All other schedulers require `num_warmup_steps`
						if num_warmup_steps is None:
												raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""    )

						if name == SchedulerType.CONSTANT_WITH_WARMUP:
												return schedule_func(_A			,							num_warmup_steps=_A			,							last_epoch=_A    )

						# All other schedulers require `num_training_steps`
						if num_training_steps is None:
												raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""    )

						if name == SchedulerType.COSINE_WITH_RESTARTS:
												return schedule_func(
												    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							num_cycles=_A			,							last_epoch=_A			,							)

						if name == SchedulerType.POLYNOMIAL:
												return schedule_func(
												    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							power=_A			,							last_epoch=_A			,							)

						return schedule_func(
						    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							last_epoch=_A    )





