


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_       :       str      						=  logging.get_logger(__name__)

lowerCamelCase_       :       Optional[int]      						=  {
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "xlm-roberta"


 def __init__(		self			,   __A=3_0522			,   __A=768			,   __A=12			,   __A=12			,   __A=3072			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=2			,   __A=0.02			,   __A=1E-1_2			,   __A=1			,   __A=0			,   __A=2			,   __A="absolute"			,   __A=True			,   __A=None			,   **__A			,   ) ->					str:
     super().__init__(pad_token_id=__A			,   bos_token_id=__A			,   eos_token_id=__A			,   **__A						)

     a					=vocab_size
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=hidden_act
     a					=intermediate_size
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=max_position_embeddings
     a					=type_vocab_size
     a					=initializer_range
     a					=layer_norm_eps
     a					=position_embedding_type
     a					=use_cache
     a					=classifier_dropout






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         a					={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         a					={0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]						)



"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    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
lowerCamelCase_       :       Any      						=  logging.getLogger(__name__)

# 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/image-classification/requirements.txt""")

lowerCamelCase_       :       Any      						=  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCamelCase_       :       Dict      						=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def _A						(						lowercase		):







    """simple docstring"""
    with open(lowercase	,   '''rb'''		) as f:
        a					=Image.open(lowercase		)
        return im.convert('''RGB'''		)






@dataclass
class 				__A    :






 """simple docstring"""




 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={
         "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
     },   )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
 __lowerCAmelCase			 =  field(default=_SCREAMING_SNAKE_CASE,   metadata={"help": "A folder containing the training data."}      )
 __lowerCAmelCase			 =  field(default=_SCREAMING_SNAKE_CASE,   metadata={"help": "A folder containing the validation data."}      )
 __lowerCAmelCase			 =  field(
     default=0.1_5,   metadata={"help": "Percent to split off of train for validation."}      )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     },   )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
             "value if set."
         )
     },   )


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
         raise ValueError(
             '''You must specify either a dataset name from the hub or a train and/or validation directory.'''						)






@dataclass
class 				__A    :






 """simple docstring"""




 __lowerCAmelCase			 =  field(
     default="google/vit-base-patch16-224-in21k",   metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},   )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_SCREAMING_SNAKE_CASE      )},   )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}      )
 __lowerCAmelCase			 =  field(
     default="main",   metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},   )
 __lowerCAmelCase			 =  field(default=_SCREAMING_SNAKE_CASE,   metadata={"help": "Name or path of preprocessor config."}      )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={
         "help": (
             "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
             "with private models)."
         )
     },   )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},   )

def _A						(						lowercase		):







    """simple docstring"""
    a					=torch.stack([example['''pixel_values'''] for example in examples]		)
    a					=torch.tensor([example['''labels'''] for example in examples]		)
    return {"pixel_values": pixel_values, "labels": labels}
def _A						(						):







    """simple docstring"""
    # 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)		)
    if len(sys.argv		) == 2 and sys.argv[1].endswith('''.json'''		):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        a		,				a		,				a					=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
    else:
        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_image_classification'''	,   lowercase	,   lowercase		)

    # 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(lowercase		)
    transformers.utils.logging.set_verbosity(lowercase		)
    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 and training_args.resume_from_checkpoint is 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		)

    # Initialize our dataset and prepare it for the 'image-classification' task.
    if data_args.dataset_name is not None:
        a					=load_dataset(
            data_args.dataset_name	,   data_args.dataset_config_name	,   cache_dir=model_args.cache_dir	,   task='''image-classification'''	,   use_auth_token=True if model_args.use_auth_token else None	,   )
    else:
        a					={}
        if data_args.train_dir is not None:
            a					=os.path.join(data_args.train_dir	,   '''**'''		)
        if data_args.validation_dir is not None:
            a					=os.path.join(data_args.validation_dir	,   '''**'''		)
        a					=load_dataset(
            '''imagefolder'''	,   data_files=lowercase	,   cache_dir=model_args.cache_dir	,   task='''image-classification'''	,   )

    # If we don't have a validation split, split off a percentage of train as validation.
    a					=None if '''validation''' in dataset.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split	,   lowercase		) and data_args.train_val_split > 0.0:
        a					=dataset['''train'''].train_test_split(data_args.train_val_split		)
        a					=split['''train''']
        a					=split['''test''']

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    a					=dataset['''train'''].features['''labels'''].names
    a		,				a					={}, {}
    for i, label in enumerate(lowercase		):
        a					=str(lowercase		)
        a					=label

    # Load the accuracy metric from the datasets package
    a					=evaluate.load('''accuracy'''		)

    # Define our 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(lowercase		):
        return metric.compute(predictions=np.argmax(p.predictions	,   axis=1		)	,   references=p.label_ids		)

    a					=AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path	,   num_labels=len(lowercase		)	,   labelaid=lowercase	,   idalabel=lowercase	,   finetuning_task='''image-classification'''	,   cache_dir=model_args.cache_dir	,   revision=model_args.model_revision	,   use_auth_token=True if model_args.use_auth_token else None	,   )
    a					=AutoModelForImageClassification.from_pretrained(
        model_args.model_name_or_path	,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)	,   config=lowercase	,   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	,   )
    a					=AutoImageProcessor.from_pretrained(
        model_args.image_processor_name or model_args.model_name_or_path	,   cache_dir=model_args.cache_dir	,   revision=model_args.model_revision	,   use_auth_token=True if model_args.use_auth_token else None	,   )

    # Define torchvision transforms to be applied to each image.
    if "shortest_edge" in image_processor.size:
        a					=image_processor.size['''shortest_edge''']
    else:
        a					=(image_processor.size['''height'''], image_processor.size['''width'''])
    a					=Normalize(mean=image_processor.image_mean	,   std=image_processor.image_std		)
    a					=Compose(
        [
            RandomResizedCrop(lowercase		),
            RandomHorizontalFlip(),
            ToTensor(),
            normalize,
        ]		)
    a					=Compose(
        [
            Resize(lowercase		),
            CenterCrop(lowercase		),
            ToTensor(),
            normalize,
        ]		)

    def train_transforms(lowercase		):
        a					=[
            _train_transforms(pil_img.convert('''RGB'''		)		) for pil_img in example_batch['''image''']
        ]
        return example_batch

    def val_transforms(lowercase		):
        a					=[_val_transforms(pil_img.convert('''RGB'''		)		) for pil_img in example_batch['''image''']]
        return example_batch

    if training_args.do_train:
        if "train" not in dataset:
            raise ValueError('''--do_train requires a train dataset'''		)
        if data_args.max_train_samples is not None:
            a					=(
                dataset['''train'''].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
            )
        # Set the training transforms
        dataset["train"].set_transform(lowercase		)

    if training_args.do_eval:
        if "validation" not in dataset:
            raise ValueError('''--do_eval requires a validation dataset'''		)
        if data_args.max_eval_samples is not None:
            a					=(
                dataset['''validation'''].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
            )
        # Set the validation transforms
        dataset["validation"].set_transform(lowercase		)

    # Initalize our trainer
    a					=Trainer(
        model=lowercase	,   args=lowercase	,   train_dataset=dataset['''train'''] if training_args.do_train else None	,   eval_dataset=dataset['''validation'''] if training_args.do_eval else None	,   compute_metrics=lowercase	,   tokenizer=lowercase	,   data_collator=lowercase	,   )

    # 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=lowercase		)
        trainer.save_model()
        trainer.log_metrics('''train'''	,   train_result.metrics		)
        trainer.save_metrics('''train'''	,   train_result.metrics		)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        a					=trainer.evaluate()
        trainer.log_metrics('''eval'''	,   lowercase		)
        trainer.save_metrics('''eval'''	,   lowercase		)

    # Write model card and (optionally) push to hub
    a					={
        '''finetuned_from''': model_args.model_name_or_path,
        '''tasks''': '''image-classification''',
        '''dataset''': data_args.dataset_name,
        '''tags''': ['''image-classification''', '''vision'''],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**lowercase		)
    else:
        trainer.create_model_card(**lowercase		)


if __name__ == "__main__":
       main()