


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''')

snake_case							: List[Any]      =   logging.getLogger(__name__)







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







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






def 							__lowercase     (				):
							# 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.

							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'      ,					__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()

							a__    =  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.
							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(__lowerCAmelCase     )

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

							# Initialize our Trainer
							a__    =  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:
														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=__lowerCAmelCase     )
														a__    =  train_result.metrics
														a__    =  (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase     )
														)
														a__    =  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 ***'     )
														a__    =  trainer.evaluate(eval_dataset=__lowerCAmelCase     )

														a__    =  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase     )
														a__    =  min(__lowerCAmelCase      ,					len(__lowerCAmelCase     )     )

														trainer.log_metrics('eval'      ,					__lowerCAmelCase     )
														trainer.save_metrics('eval'      ,					__lowerCAmelCase     )

							# Prediction
							if training_args.do_predict:
														logger.info('*** Predict ***'     )
														a__   ,      a__   ,      a__    =  trainer.predict(__lowerCAmelCase      ,					metric_key_prefix='predict'     )

														a__    =  (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase     )
														)
														a__    =  min(__lowerCAmelCase      ,					len(__lowerCAmelCase     )     )

														trainer.log_metrics('predict'      ,					__lowerCAmelCase     )
														trainer.save_metrics('predict'      ,					__lowerCAmelCase     )

														a__    =  np.argmax(__lowerCAmelCase      ,					axis=1     )
														a__    =  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     ):
																																			a__    =  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_tf_available,
    is_torch_available,
    is_vision_available,
)


snake_case							: Any      =   {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   ['''MobileViTFeatureExtractor''']
						snake_case							: int      =   ['''MobileViTImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Dict      =   [
						    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileViTForImageClassification''',
						    '''MobileViTForSemanticSegmentation''',
						    '''MobileViTModel''',
						    '''MobileViTPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Tuple      =   [
						    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFMobileViTForImageClassification''',
						    '''TFMobileViTForSemanticSegmentation''',
						    '''TFMobileViTModel''',
						    '''TFMobileViTPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilevit import MobileViTFeatureExtractor
												from .image_processing_mobilevit import MobileViTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilevit import (
												    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileViTForImageClassification,
												    MobileViTForSemanticSegmentation,
												    MobileViTModel,
												    MobileViTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilevit import (
												    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileViTForImageClassification,
												    TFMobileViTForSemanticSegmentation,
												    TFMobileViTModel,
												    TFMobileViTPreTrainedModel,
												)


else:
						import sys

						snake_case							: Optional[int]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




