

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:    Any 							=			logging.getLogger(__name__)

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



def       __lowerCAmelCase      (						):
	# 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_       				=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
	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"       ,     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()

	UpperCAmelCase_       				=			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.
	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(a_    )

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

	# Initialize our Trainer
	UpperCAmelCase_       				=			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:
		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=a_    )
		UpperCAmelCase_       				=			train_result.metrics
		UpperCAmelCase_       				=			(
		    data_args.max_train_samples if data_args.max_train_samples is not None else len(a_    )
		)
		UpperCAmelCase_       				=			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 ***"    )
		UpperCAmelCase_       				=			trainer.evaluate(eval_dataset=a_    )

		UpperCAmelCase_       				=			data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a_    )
		UpperCAmelCase_       				=			min(a_       ,     len(a_    )    )

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

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

		UpperCAmelCase_       				=			(
		    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(a_    )
		)
		UpperCAmelCase_       				=			min(a_       ,     len(a_    )    )

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

		UpperCAmelCase_       				=			np.argmax(a_       ,     axis=1    )
		UpperCAmelCase_       				=			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_    ):
					UpperCAmelCase_       				=			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,
)


_a:    List[str] 							=			{
    """configuration_encodec""": [
        """ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EncodecConfig""",
    ],
    """feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:    List[Any] 							=			[
				    """ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """EncodecModel""",
				    """EncodecPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

				_a:    int 							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)