




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')

lowercase_	:				Tuple  				= logging.getLogger(__name__)






@dataclass
class     __lowerCAmelCase :
			snake_case_      : List[Any]      =     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."
			        )
			    }					,		)
			snake_case_      : Optional[int]      =     field(
			    default=A__					,		metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
			snake_case_      : Optional[Any]      =     field(
			    default=A__					,		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."
			        )
			    }					,		)
			snake_case_      : List[Any]      =     field(
			    default=A__					,		metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of training examples to this "
			            "value if set."
			        )
			    }					,		)
			snake_case_      : Any      =     field(
			    default=A__					,		metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
			            "value if set."
			        )
			    }					,		)
			snake_case_      : Optional[Any]      =     field(
			    default=A__					,		metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
			            "value if set."
			        )
			    }					,		)






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




def 							__SCREAMING_SNAKE_CASE  (			):

						'''simple docstring'''


						_UpperCAmelCase													=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
						_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase													=     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()

						_UpperCAmelCase													=     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.
						_UpperCAmelCase													=     None
						if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
												_UpperCAmelCase													=     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													=     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													=     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													=     train_dataset.features["label"].names

						if training_args.do_eval:
												_UpperCAmelCase													=     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													=     eval_dataset.features["label"].names

						if training_args.do_predict:
												_UpperCAmelCase													=     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													=     predict_dataset.features["label"].names

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

						def preprocess_function(snake_case_						):
												# 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:
																		_UpperCAmelCase													=     min(len(_UpperCamelCase						)	,    data_args.max_train_samples						)
																		_UpperCAmelCase													=     train_dataset.select(range(_UpperCamelCase						)						)
												with training_args.main_process_first(desc="train dataset map pre-processing"						):
																		_UpperCAmelCase													=     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:
																		_UpperCAmelCase													=     min(len(_UpperCamelCase						)	,    data_args.max_eval_samples						)
																		_UpperCAmelCase													=     eval_dataset.select(range(_UpperCamelCase						)						)
												with training_args.main_process_first(desc="validation dataset map pre-processing"						):
																		_UpperCAmelCase													=     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:
																		_UpperCAmelCase													=     min(len(_UpperCamelCase						)	,    data_args.max_predict_samples						)
																		_UpperCAmelCase													=     predict_dataset.select(range(_UpperCamelCase						)						)
												with training_args.main_process_first(desc="prediction dataset map pre-processing"						):
																		_UpperCAmelCase													=     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
						_UpperCAmelCase													=     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(snake_case_						):
												_UpperCAmelCase													=     p.predictions[0] if isinstance(p.predictions	,    _UpperCamelCase						) else p.predictions
												_UpperCAmelCase													=     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:
												_UpperCAmelCase													=     default_data_collator
						elif training_args.fpaa:
												_UpperCAmelCase													=     DataCollatorWithPadding(_UpperCamelCase	,    pad_to_multiple_of=8						)
						else:
												_UpperCAmelCase													=     None

						# Initialize our Trainer
						_UpperCAmelCase													=     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:
												_UpperCAmelCase													=     None
												if training_args.resume_from_checkpoint is not None:
																		_UpperCAmelCase													=     training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		_UpperCAmelCase													=     last_checkpoint
												_UpperCAmelCase													=     trainer.train(resume_from_checkpoint=_UpperCamelCase						)
												_UpperCAmelCase													=     train_result.metrics
												_UpperCAmelCase													=     (
												    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCamelCase						)
												)
												_UpperCAmelCase													=     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 ***"						)
												_UpperCAmelCase													=     trainer.evaluate(eval_dataset=_UpperCamelCase						)

												_UpperCAmelCase													=     data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCamelCase						)
												_UpperCAmelCase													=     min(_UpperCamelCase	,    len(_UpperCamelCase						)						)

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

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

												_UpperCAmelCase													=     (
												    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_UpperCamelCase						)
												)
												_UpperCAmelCase													=     min(_UpperCamelCase	,    len(_UpperCamelCase						)						)

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

												_UpperCAmelCase													=     np.argmax(_UpperCamelCase	,    axis=1						)
												_UpperCAmelCase													=     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						):
																														_UpperCAmelCase													=     label_list[item]
																														writer.write(f"""{index}\t{item}\n"""						)


if __name__ == "__main__":
				main()







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      int					 =     {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[Any]					 =     ["MBartTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Dict					 =     ["MBartTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     [
         "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MBartForCausalLM",
         "MBartForConditionalGeneration",
         "MBartForQuestionAnswering",
         "MBartForSequenceClassification",
         "MBartModel",
         "MBartPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TFMBartForConditionalGeneration",
         "TFMBartModel",
         "TFMBartPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     [
         "FlaxMBartForConditionalGeneration",
         "FlaxMBartForQuestionAnswering",
         "FlaxMBartForSequenceClassification",
         "FlaxMBartModel",
         "FlaxMBartPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
