
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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	with open(__SCREAMING_SNAKE_CASE					,				'rb'				) as f:
		lowercase	    =       Image.open(__SCREAMING_SNAKE_CASE				)
		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       SCREAMING_SNAKE_CASE__		(			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 			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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       torch.stack([example['pixel_values'] for example in examples]				)
	lowercase	    =       torch.tensor([example['labels'] for example in examples]				)
	return {"pixel_values": pixel_values, "labels": labels}






def 				UpperCAmelCase_						(				):
	# 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.

	lowercase	    =       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.
		lowercase    ,   lowercase    ,   lowercase	    =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
	else:
		lowercase    ,   lowercase    ,   lowercase	    =       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'					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_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()

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

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

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

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

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

	def val_transforms(__SCREAMING_SNAKE_CASE				):
		lowercase	    =       [_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:
			lowercase	    =       (
			    dataset['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
			)
		# Set the training transforms
		dataset["train"].set_transform(__SCREAMING_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:
			lowercase	    =       (
			    dataset['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
			)
		# Set the validation transforms
		dataset["validation"].set_transform(__SCREAMING_SNAKE_CASE				)

	# Initalize our trainer
	lowercase	    =       Trainer(
	    model=__SCREAMING_SNAKE_CASE					,				args=__SCREAMING_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=__SCREAMING_SNAKE_CASE					,				tokenizer=__SCREAMING_SNAKE_CASE					,				data_collator=__SCREAMING_SNAKE_CASE					,				)

	# Training
	if training_args.do_train:
		lowercase	    =       None
		if training_args.resume_from_checkpoint is not None:
			lowercase	    =       training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			lowercase	    =       last_checkpoint
		lowercase	    =       trainer.train(resume_from_checkpoint=__SCREAMING_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:
		lowercase	    =       trainer.evaluate()
		trainer.log_metrics('eval'					,				__SCREAMING_SNAKE_CASE				)
		trainer.save_metrics('eval'					,				__SCREAMING_SNAKE_CASE				)

	# Write model card and (optionally) push to hub
	lowercase	    =       {
	    '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(**__SCREAMING_SNAKE_CASE				)
	else:
		trainer.create_model_card(**__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
	main()


'''simple docstring'''

from __future__ import annotations



def snake_case_      (					__snake_case  :							list[int | str])			->					None:
   create_state_space_tree(__snake_case	,   []	,   0	,   [0 for i in range(len(__snake_case))])



def snake_case_      (					__snake_case  :							list[int | str]	,   __snake_case  :							list[int | str]	,   __snake_case  :							int	,   __snake_case  :							list[int]	,   )			->					None:

   if index == len(__snake_case):
      print(__snake_case)
      return

   for i in range(len(__snake_case)):
      if not index_used[i]:
         current_sequence.append(sequence[i])
         lowerCAmelCase_			=      True
         create_state_space_tree(__snake_case	,   __snake_case	,   index + 1	,   __snake_case)
         current_sequence.pop()
         lowerCAmelCase_			=      False
A_     :						list[int | str]    							=[3, 1, 2, 4]
generate_all_permutations(sequence)

A_     :						list[int | str]    							=["A", "B", "C"]






generate_all_permutations(sequence_a)





