"""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
SCREAMING_SNAKE_CASE__:Optional[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""")

SCREAMING_SNAKE_CASE__:Tuple			=	list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
SCREAMING_SNAKE_CASE__:Tuple			=	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def 					_lowerCamelCase(					a							):
							with open(lowerCAmelCase_ ,  "rb"							) as f:
														__a     				=  Image.open(lowerCAmelCase_							)
														return im.convert("RGB"							)






@dataclass
class 				snake_case__     :
	_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 		a__ (				self		):
								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 				snake_case__     :
	_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(					a							):
							__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 					_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.

							__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     				=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
							else:
														__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" ,  lowerCAmelCase_ ,  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_							)
							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 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=lowerCAmelCase_ ,  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 ,  lowerCAmelCase_							) 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     				=  {}, {}
							for i, label in enumerate(lowerCAmelCase_							):
														__a     				=  str(lowerCAmelCase_							)
														__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(a							):
														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(lowerCAmelCase_							) ,  labelaid=lowerCAmelCase_ ,  idalabel=lowerCAmelCase_ ,  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=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 ,  )
							__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(lowerCAmelCase_							),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ]							)
							__a     				=  Compose(
							    [
							        Resize(lowerCAmelCase_							),
							        CenterCrop(lowerCAmelCase_							),
							        ToTensor(),
							        normalize,
							    ]							)

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

							def val_transforms(a							):
														__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(lowerCAmelCase_							)

							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(lowerCAmelCase_							)

							# Initalize our trainer
							__a     				=  Trainer(
							    model=lowerCAmelCase_ ,  args=lowerCAmelCase_ ,  train_dataset=dataset["train"] if training_args.do_train else None ,  eval_dataset=dataset["validation"] 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_							)
														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" ,  lowerCAmelCase_							)
														trainer.save_metrics("eval" ,  lowerCAmelCase_							)

							# 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(**lowerCAmelCase_							)
							else:
														trainer.create_model_card(**lowerCAmelCase_							)


if __name__ == "__main__":
			main()

"""simple docstring"""



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:
   SCREAMING_SNAKE_CASE__:List[Any]			=	None


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Tuple			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """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""",
    },
}

SCREAMING_SNAKE_CASE__:List[str]			=	{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE__:str			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		VOCAB_FILES_NAMES
 _snake_case   :			List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Any           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		CamembertTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"]  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

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

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

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


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        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(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

        return (out_vocab_file,)
