






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
UpperCamelCase			    =     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''')

UpperCamelCase			    =     list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCamelCase			    =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def 	__lowerCamelCase      (		snake_case__		)							->  List[str]:






						"""simple docstring"""





						with open(snake_case__	,"""rb"""		) as f:
												_SCREAMING_SNAKE_CASE     				=  Image.open(snake_case__		)
												return im.convert("""RGB"""		)



@dataclass
class 				__UpperCAmelCase		:
			__snake_case    :				Optional[str]									=							field(
			    default=_UpperCAmelCase	,metadata={
			        "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
			    }	,)
			__snake_case    :				Optional[str]									=							field(
			    default=_UpperCAmelCase	,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
			__snake_case    :				Optional[str]									=							field(default=_UpperCAmelCase	,metadata={"help": "A folder containing the training data."}   )
			__snake_case    :				Optional[str]									=							field(default=_UpperCAmelCase	,metadata={"help": "A folder containing the validation data."}   )
			__snake_case    :				Optional[float]									=							field(
			    default=0.15	,metadata={"help": "Percent to split off of train for validation."}   )
			__snake_case    :				Optional[int]									=							field(
			    default=_UpperCAmelCase	,metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of training examples to this "
			            "value if set."
			        )
			    }	,)
			__snake_case    :				Optional[int]									=							field(
			    default=_UpperCAmelCase	,metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
			            "value if set."
			        )
			    }	,)







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




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

def 	__lowerCamelCase      (		snake_case__		)							->  Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  torch.stack([example["""pixel_values"""] for example in examples]		)
						_SCREAMING_SNAKE_CASE     				=  torch.tensor([example["""labels"""] for example in examples]		)
						return {"pixel_values": pixel_values, "labels": labels}

def 	__lowerCamelCase      (		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  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.
												_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  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"""	,snake_case__	,snake_case__		)

						# 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()

						_SCREAMING_SNAKE_CASE     				=  training_args.get_process_log_level()
						logger.setLevel(snake_case__		)
						transformers.utils.logging.set_verbosity(snake_case__		)
						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.
						_SCREAMING_SNAKE_CASE     				=  None
						if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
												_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  {}
												if data_args.train_dir is not None:
																		_SCREAMING_SNAKE_CASE     				=  os.path.join(data_args.train_dir	,"""**"""		)
												if data_args.validation_dir is not None:
																		_SCREAMING_SNAKE_CASE     				=  os.path.join(data_args.validation_dir	,"""**"""		)
												_SCREAMING_SNAKE_CASE     				=  load_dataset(
												    """imagefolder"""	,data_files=snake_case__	,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.
						_SCREAMING_SNAKE_CASE     				=  None if """validation""" in dataset.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split	,snake_case__		) and data_args.train_val_split > 0.0:
												_SCREAMING_SNAKE_CASE     				=  dataset["""train"""].train_test_split(data_args.train_val_split		)
												_SCREAMING_SNAKE_CASE     				=  split["""train"""]
												_SCREAMING_SNAKE_CASE     				=  split["""test"""]

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

						# Load the accuracy metric from the datasets package
						_SCREAMING_SNAKE_CASE     				=  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(snake_case__		):
												return metric.compute(predictions=np.argmax(p.predictions	,axis=1		)	,references=p.label_ids		)

						_SCREAMING_SNAKE_CASE     				=  AutoConfig.from_pretrained(
						    model_args.config_name or model_args.model_name_or_path	,num_labels=len(snake_case__		)	,labelaid=snake_case__	,idalabel=snake_case__	,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	,)
						_SCREAMING_SNAKE_CASE     				=  AutoModelForImageClassification.from_pretrained(
						    model_args.model_name_or_path	,from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)	,config=snake_case__	,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	,)
						_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  image_processor.size["""shortest_edge"""]
						else:
												_SCREAMING_SNAKE_CASE     				=  (image_processor.size["""height"""], image_processor.size["""width"""])
						_SCREAMING_SNAKE_CASE     				=  Normalize(mean=image_processor.image_mean	,std=image_processor.image_std		)
						_SCREAMING_SNAKE_CASE     				=  Compose(
						    [
						        RandomResizedCrop(snake_case__		),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]		)
						_SCREAMING_SNAKE_CASE     				=  Compose(
						    [
						        Resize(snake_case__		),
						        CenterCrop(snake_case__		),
						        ToTensor(),
						        normalize,
						    ]		)

						def train_transforms(snake_case__		):
												_SCREAMING_SNAKE_CASE     				=  [
												    _train_transforms(pil_img.convert("""RGB"""		)		) for pil_img in example_batch["""image"""]
												]
												return example_batch

						def val_transforms(snake_case__		):
												_SCREAMING_SNAKE_CASE     				=  [_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:
																		_SCREAMING_SNAKE_CASE     				=  (
																		    dataset["""train"""].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
																		)
												# Set the training transforms
												dataset["train"].set_transform(snake_case__		)

						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:
																		_SCREAMING_SNAKE_CASE     				=  (
																		    dataset["""validation"""].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
																		)
												# Set the validation transforms
												dataset["validation"].set_transform(snake_case__		)

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

						# Training
						if training_args.do_train:
												_SCREAMING_SNAKE_CASE     				=  None
												if training_args.resume_from_checkpoint is not None:
																		_SCREAMING_SNAKE_CASE     				=  training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		_SCREAMING_SNAKE_CASE     				=  last_checkpoint
												_SCREAMING_SNAKE_CASE     				=  trainer.train(resume_from_checkpoint=snake_case__		)
												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:
												_SCREAMING_SNAKE_CASE     				=  trainer.evaluate()
												trainer.log_metrics("""eval"""	,snake_case__		)
												trainer.save_metrics("""eval"""	,snake_case__		)

						# Write model card and (optionally) push to hub
						_SCREAMING_SNAKE_CASE     				=  {
						    """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(**snake_case__		)
						else:
												trainer.create_model_card(**snake_case__		)


if __name__ == "__main__":
		main()









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_camembert import CamembertTokenizer
else:
		UpperCamelCase			    =     None


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase			    =     {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase			    =     {
    '''camembert-base''': 512,
}

UpperCamelCase			    =     '''▁'''



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				int									=							VOCAB_FILES_NAMES
			__snake_case    :				Any									=							PRETRAINED_VOCAB_FILES_MAP
			__snake_case    :				Optional[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case    :				Dict									=							["input_ids", "attention_mask"]
			__snake_case    :				Tuple									=							CamembertTokenizer







			def __init__(     self:  List[Any]				,     UpperCAmelCase_:  Optional[int]=None				,     UpperCAmelCase_:  Tuple=None				,     UpperCAmelCase_:  str="<s>"				,     UpperCAmelCase_:  List[str]="</s>"				,     UpperCAmelCase_:  Dict="</s>"				,     UpperCAmelCase_:  List[Any]="<s>"				,     UpperCAmelCase_:  Dict="<unk>"				,     UpperCAmelCase_:  Any="<pad>"				,     UpperCAmelCase_:  Tuple="<mask>"				,     UpperCAmelCase_:  str=["<s>NOTUSED", "</s>NOTUSED"]				,     **UpperCAmelCase_:  Optional[Any]				,     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  AddedToken(UpperCAmelCase_				,     lstrip=UpperCAmelCase_				,     rstrip=UpperCAmelCase_					) if isinstance(UpperCAmelCase_				,     UpperCAmelCase_					) else mask_token

									super().__init__(
									    UpperCAmelCase_				,     tokenizer_file=UpperCAmelCase_				,     bos_token=UpperCAmelCase_				,     eos_token=UpperCAmelCase_				,     sep_token=UpperCAmelCase_				,     cls_token=UpperCAmelCase_				,     unk_token=UpperCAmelCase_				,     pad_token=UpperCAmelCase_				,     mask_token=UpperCAmelCase_				,     additional_special_tokens=UpperCAmelCase_				,     **UpperCAmelCase_				,     )

									_SCREAMING_SNAKE_CASE     				=  vocab_file
									_SCREAMING_SNAKE_CASE     				=  False if not self.vocab_file else True







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None					):







									'''simple docstring'''




									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_SCREAMING_SNAKE_CASE     				=  [self.cls_token_id]
									_SCREAMING_SNAKE_CASE     				=  [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  List[int]				,     UpperCAmelCase_:  Optional[List[int]] = None					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  [self.sep_token_id]
									_SCREAMING_SNAKE_CASE     				=  [self.cls_token_id]

									if token_ids_a is None:
															return len(cls + token_ids_a + sep					) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  Optional[str] = None					):







									'''simple docstring'''




									if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""					)

									if not os.path.isdir(UpperCAmelCase_					):
															logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
															return
									_SCREAMING_SNAKE_CASE     				=  os.path.join(
									    UpperCAmelCase_				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCAmelCase_					):
															copyfile(self.vocab_file				,     UpperCAmelCase_					)

									return (out_vocab_file,)

