

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')

SCREAMING_SNAKE_CASE_        =      logging.getLogger(__name__)

@dataclass
class 				a  :
						_lowercase    						=			field(
						    default=1_2_8							, 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=UpperCAmelCase							, metadata={"help": "Overwrite the cached preprocessed datasets or not."}				)
						_lowercase    						=			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."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of training examples to this "
						            "value if set."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
						            "value if set."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
						            "value if set."
						        )
						    }							, )







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







def 							__SCREAMING_SNAKE_CASE							(				)   ->       Tuple:
				# 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       :		Optional[int] 		=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    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()

				_UpperCAmelCase       :		int 		=    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.
				_UpperCAmelCase       :		Optional[Any] 		=    None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								_UpperCAmelCase       :		Tuple 		=    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:
												_UpperCAmelCase       :		Union[str, Any] 		=    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:
												_UpperCAmelCase       :		Dict 		=    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  ,      )
								_UpperCAmelCase       :		Optional[Any] 		=    train_dataset.features["label"].names

				if training_args.do_eval:
								_UpperCAmelCase       :		str 		=    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  ,      )
								_UpperCAmelCase       :		Any 		=    eval_dataset.features["label"].names

				if training_args.do_predict:
								_UpperCAmelCase       :		List[str] 		=    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  ,      )
								_UpperCAmelCase       :		Union[str, Any] 		=    predict_dataset.features["label"].names

				# Labels
				_UpperCAmelCase       :		Any 		=    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.
				_UpperCAmelCase       :		Union[str, Any] 		=    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  ,      )
				_UpperCAmelCase       :		List[Any] 		=    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  ,      )
				_UpperCAmelCase       :		Union[str, Any] 		=    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:
								_UpperCAmelCase       :		Any 		=    "max_length"
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								_UpperCAmelCase       :		List[str] 		=    False

				def preprocess_function(lowerCAmelCase:  Union[str, 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:
												_UpperCAmelCase       :		int 		=    min(len(lowerCAmelCase )  ,      data_args.max_train_samples )
												_UpperCAmelCase       :		str 		=    train_dataset.select(range(lowerCAmelCase ) )
								with training_args.main_process_first(desc="train dataset map pre-processing" ):
												_UpperCAmelCase       :		str 		=    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:
												_UpperCAmelCase       :		Dict 		=    min(len(lowerCAmelCase )  ,      data_args.max_eval_samples )
												_UpperCAmelCase       :		Optional[Any] 		=    eval_dataset.select(range(lowerCAmelCase ) )
								with training_args.main_process_first(desc="validation dataset map pre-processing" ):
												_UpperCAmelCase       :		Tuple 		=    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:
												_UpperCAmelCase       :		List[Any] 		=    min(len(lowerCAmelCase )  ,      data_args.max_predict_samples )
												_UpperCAmelCase       :		List[Any] 		=    predict_dataset.select(range(lowerCAmelCase ) )
								with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
												_UpperCAmelCase       :		Optional[int] 		=    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
				_UpperCAmelCase       :		Tuple 		=    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(lowerCAmelCase:  EvalPrediction ):
								_UpperCAmelCase       :		Tuple 		=    p.predictions[0] if isinstance(p.predictions  ,      lowerCAmelCase ) else p.predictions
								_UpperCAmelCase       :		List[Any] 		=    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:
								_UpperCAmelCase       :		Optional[int] 		=    default_data_collator
				elif training_args.fpaa:
								_UpperCAmelCase       :		str 		=    DataCollatorWithPadding(lowerCAmelCase  ,      pad_to_multiple_of=8 )
				else:
								_UpperCAmelCase       :		int 		=    None

				# Initialize our Trainer
				_UpperCAmelCase       :		Optional[int] 		=    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:
								_UpperCAmelCase       :		List[Any] 		=    None
								if training_args.resume_from_checkpoint is not None:
												_UpperCAmelCase       :		str 		=    training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_UpperCAmelCase       :		int 		=    last_checkpoint
								_UpperCAmelCase       :		List[str] 		=    trainer.train(resume_from_checkpoint=lowerCAmelCase )
								_UpperCAmelCase       :		int 		=    train_result.metrics
								_UpperCAmelCase       :		Tuple 		=    (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase )
								)
								_UpperCAmelCase       :		Optional[int] 		=    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 ***" )
								_UpperCAmelCase       :		int 		=    trainer.evaluate(eval_dataset=lowerCAmelCase )

								_UpperCAmelCase       :		Tuple 		=    data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase )
								_UpperCAmelCase       :		Optional[Any] 		=    min(lowerCAmelCase  ,      len(lowerCAmelCase ) )

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

				# Prediction
				if training_args.do_predict:
								logger.info("*** Predict ***" )
								_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    trainer.predict(lowerCAmelCase  ,      metric_key_prefix="predict" )

								_UpperCAmelCase       :		int 		=    (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCAmelCase )
								)
								_UpperCAmelCase       :		Union[str, Any] 		=    min(lowerCAmelCase  ,      len(lowerCAmelCase ) )

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

								_UpperCAmelCase       :		str 		=    np.argmax(lowerCAmelCase  ,      axis=1 )
								_UpperCAmelCase       :		Union[str, Any] 		=    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 ):
																				_UpperCAmelCase       :		Tuple 		=    label_list[item]
																				writer.write(F'{index}\t{item}\n' )


if __name__ == "__main__":
					main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_        =      {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NllbMoeForConditionalGeneration',
					    'NllbMoeModel',
					    'NllbMoePreTrainedModel',
					    'NllbMoeTop2Router',
					    'NllbMoeSparseMLP',
					]


if TYPE_CHECKING:
					from .configuration_nllb_moe import (
					    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    NllbMoeConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nllb_moe import (
										    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NllbMoeForConditionalGeneration,
										    NllbMoeModel,
										    NllbMoePreTrainedModel,
										    NllbMoeSparseMLP,
										    NllbMoeTopaRouter,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


