'''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
__lowercase          =  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''')

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






def 				snake_case__							(			_A:					str )    ->	str:

		'''simple docstring'''


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






@dataclass
class 				a__:


			'''simple docstring'''






			UpperCAmelCase_			:     Optional[str]      =       field(
			    default=lowerCAmelCase__ ,						metadata={
			        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
			    } ,						)
			UpperCAmelCase_			:     Optional[str]      =       field(
			    default=lowerCAmelCase__ ,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
			UpperCAmelCase_			:     Optional[str]      =       field(default=lowerCAmelCase__ ,						metadata={'''help''': '''A folder containing the training data.'''}	)
			UpperCAmelCase_			:     Optional[str]      =       field(default=lowerCAmelCase__ ,						metadata={'''help''': '''A folder containing the validation data.'''}	)
			UpperCAmelCase_			:     Optional[float]      =       field(
			    default=0.15 ,						metadata={'''help''': '''Percent to split off of train for validation.'''}	)
			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.'''
			        )
			    } ,						)



			def 							a_       (   self):




					"""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 				a__:


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       field(
			    default='''google/vit-base-patch16-224-in21k''' ,						metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} ,						)
			UpperCAmelCase_			:     Optional[str]      =       field(
			    default=lowerCAmelCase__ ,						metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowerCAmelCase__	)} ,						)
			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''': '''Where do you want to store the pretrained models downloaded from s3'''}	)
			UpperCAmelCase_			:     str      =       field(
			    default='''main''' ,						metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} ,						)
			UpperCAmelCase_			:     str      =       field(default=lowerCAmelCase__ ,						metadata={'''help''': '''Name or path of preprocessor config.'''}	)
			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 				snake_case__							(			_A:					List[str] )    ->	Dict:

		'''simple docstring'''


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






def 				snake_case__							(			)    ->	List[Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  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.
				lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
		else:
				lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  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"""      ,   _A      ,   _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()

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

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

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

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

		def train_transforms(_A:					List[str] ):
				lowerCAmelCase   					=  [
				    _train_transforms(pil_img.convert("""RGB""" ) ) for pil_img in example_batch["""image"""]
				]
				return example_batch

		def val_transforms(_A:					Union[str, Any] ):
				lowerCAmelCase   					=  [_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:
						lowerCAmelCase   					=  (
						    dataset["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
						)
				# Set the training transforms
				dataset["train"].set_transform(_A )

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

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

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

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


if __name__ == "__main__":
						main()





'''simple docstring'''




import os
import string
import sys


__lowercase          =  1 << 8

__lowercase          =  {
    '''tab''': ord('''\t'''),
    '''newline''': ord('''\r'''),
    '''esc''': 2_7,
    '''up''': 6_5 + ARROW_KEY_FLAG,
    '''down''': 6_6 + ARROW_KEY_FLAG,
    '''right''': 6_7 + ARROW_KEY_FLAG,
    '''left''': 6_8 + ARROW_KEY_FLAG,
    '''mod_int''': 9_1,
    '''undefined''': sys.maxsize,
    '''interrupt''': 3,
    '''insert''': 5_0,
    '''delete''': 5_1,
    '''pg_up''': 5_3,
    '''pg_down''': 5_4,
}

__lowercase          =  KEYMAP['''up''']
__lowercase          =  KEYMAP['''left''']

if sys.platform == "win32":
						__lowercase          =  []
						__lowercase          =  {
						    B'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
						    B'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
						    B'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
						    B'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
						    B'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
						    B'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
						    B'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
						    B'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
						}

for i in range(1_0):
						__lowercase          =  ord(str(i))






def 				snake_case__							(			)    ->	List[Any]:

		'''simple docstring'''


		if os.name == "nt":
				import msvcrt

				lowerCAmelCase   					=  """mbcs"""
				# Flush the keyboard buffer
				while msvcrt.kbhit():
						msvcrt.getch()
				if len(_A ) == 0:
						# Read the keystroke
						lowerCAmelCase   					=  msvcrt.getch()

						# If it is a prefix char, get second part
						if ch in (b"\x00", b"\xe0"):
								lowerCAmelCase   					=  ch + msvcrt.getch()
								# Translate actual Win chars to bullet char types
								try:
										lowerCAmelCase   					=  chr(WIN_KEYMAP[cha] )
										WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
										WIN_CH_BUFFER.append(_A )
										if ord(_A ) in (
										    KEYMAP["insert"] - 1 << 9,
										    KEYMAP["delete"] - 1 << 9,
										    KEYMAP["pg_up"] - 1 << 9,
										    KEYMAP["pg_down"] - 1 << 9,
										):
												WIN_CH_BUFFER.append(chr(126 ) )
										lowerCAmelCase   					=  chr(KEYMAP["""esc"""] )
								except KeyError:
										lowerCAmelCase   					=  cha[1]
						else:
								lowerCAmelCase   					=  ch.decode(_A )
				else:
						lowerCAmelCase   					=  WIN_CH_BUFFER.pop(0 )
		elif os.name == "posix":
				import termios
				import tty

				lowerCAmelCase   					=  sys.stdin.fileno()
				lowerCAmelCase   					=  termios.tcgetattr(_A )
				try:
						tty.setraw(_A )
						lowerCAmelCase   					=  sys.stdin.read(1 )
				finally:
						termios.tcsetattr(_A      ,   termios.TCSADRAIN      ,   _A )
		return ch






def 				snake_case__							(			)    ->	Tuple:

		'''simple docstring'''


		lowerCAmelCase   					=  get_raw_chars()
		if ord(_A ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
				return char

		elif ord(_A ) == KEYMAP["esc"]:
				lowerCAmelCase   					=  get_raw_chars()
				if ord(_A ) == KEYMAP["mod_int"]:
						lowerCAmelCase   					=  get_raw_chars()
						if ord(_A ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_A ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
								return chr(ord(_A ) + ARROW_KEY_FLAG )
						else:
								return KEYMAP["undefined"]
				else:
						return get_raw_chars()

		else:
				if char in string.printable:
						return char
				else:
						return KEYMAP["undefined"]




