





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  (a__ :Tuple							):






							"""simple docstring"""





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

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






							def 					_lowerCamelCase		(    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 							__SCREAMING_SNAKE_CASE       :
							snake_case  :   Union[str, Any]      =							field(
							    default="""google/vit-base-patch16-224-in21k""" ,		metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} ,		)
							snake_case  :   Dict      =							field(
							    default=lowerCAmelCase__ ,		metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCAmelCase__							)} ,		)
							snake_case  :   Dict      =							field(
							    default=lowerCAmelCase__ ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
							snake_case  :   str      =							field(
							    default=lowerCAmelCase__ ,		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  :   Tuple      =							field(default=lowerCAmelCase__ ,		metadata={"""help""": """Name or path of preprocessor config."""}							)
							snake_case  :   List[str]      =							field(
							    default=lowerCAmelCase__ ,		metadata={
							        """help""": (
							            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
							            """with private models)."""
							        )
							    } ,		)
							snake_case  :   Any      =							field(
							    default=lowerCAmelCase__ ,		metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} ,		)






def      _UpperCamelCase  (a__ :List[str]							):






							"""simple docstring"""





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





def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      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.
														UpperCamelCase__					  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
							else:
														UpperCamelCase__					  =      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()

							UpperCamelCase__					  =      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.
							UpperCamelCase__					  =      None
							if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
														UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      {}
														if data_args.train_dir is not None:
																					UpperCamelCase__					  =      os.path.join(data_args.train_dir ,      """**"""							)
														if data_args.validation_dir is not None:
																					UpperCamelCase__					  =      os.path.join(data_args.validation_dir ,      """**"""							)
														UpperCamelCase__					  =      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.
							UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      dataset["""train"""].train_test_split(data_args.train_val_split							)
														UpperCamelCase__					  =      split["""train"""]
														UpperCamelCase__					  =      split["""test"""]

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

							# Load the accuracy metric from the datasets package
							UpperCamelCase__					  =      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__ :List[str]							):
														return metric.compute(predictions=np.argmax(p.predictions ,      axis=1							) ,      references=p.label_ids							)

							UpperCamelCase__					  =      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 ,      )
							UpperCamelCase__					  =      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 ,      )
							UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      image_processor.size["""shortest_edge"""]
							else:
														UpperCamelCase__					  =      (image_processor.size["""height"""], image_processor.size["""width"""])
							UpperCamelCase__					  =      Normalize(mean=image_processor.image_mean ,      std=image_processor.image_std							)
							UpperCamelCase__					  =      Compose(
							    [
							        RandomResizedCrop(a__							),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ]							)
							UpperCamelCase__					  =      Compose(
							    [
							        Resize(a__							),
							        CenterCrop(a__							),
							        ToTensor(),
							        normalize,
							    ]							)

							def train_transforms(a__ :Optional[Any]							):
														UpperCamelCase__					  =      [
														    _train_transforms(pil_img.convert("""RGB"""							)							) for pil_img in example_batch["""image"""]
														]
														return example_batch

							def val_transforms(a__ :Optional[int]							):
														UpperCamelCase__					  =      [_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:
																					UpperCamelCase__					  =      (
																					    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:
																					UpperCamelCase__					  =      (
																					    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
							UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      None
														if training_args.resume_from_checkpoint is not None:
																					UpperCamelCase__					  =      training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					UpperCamelCase__					  =      last_checkpoint
														UpperCamelCase__					  =      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:
														UpperCamelCase__					  =      trainer.evaluate()
														trainer.log_metrics("""eval""" ,      a__							)
														trainer.save_metrics("""eval""" ,      a__							)

							# Write model card and (optionally) push to hub
							UpperCamelCase__					  =      {
							    """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"""
from __future__ import annotations

import math

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    size
        # approximate the overall size of segment tree with given value
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]
        # create array to store lazy update
        UpperCAmelCase__			:							int							=    [0 for i in range(0							,       4 * size	)]
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]  # flag for lazy update



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2 + 1



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if left_element == right_element:
              UpperCAmelCase__			:							Union[str, Any]							=    a[left_element - 1]
        else:
              UpperCAmelCase__			:							List[str]							=    (left_element + right_element) // 2
              self.build(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
              self.build(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							int							=    max(
                  self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Dict							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Optional[Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Optional[int]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Tuple							=    True
                    UpperCAmelCase__			:							str							=    True

        if right_element < a or left_element > b:
              return True
        if left_element >= a and right_element <= b:
              UpperCAmelCase__			:							Union[str, Any]							=    val
              if left_element != right_element:
                    UpperCAmelCase__			:							Union[str, Any]							=    val
                    UpperCAmelCase__			:							List[Any]							=    val
                    UpperCAmelCase__			:							Union[str, Any]							=    True
                    UpperCAmelCase__			:							List[Any]							=    True
              return True
        UpperCAmelCase__			:							Tuple							=    (left_element + right_element) // 2
        self.update(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        self.update(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							Dict							=    max(
            self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)
        return True



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							List[Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Optional[Any]							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Tuple							=    self.lazy[idx]
                    UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							List[str]							=    True
                    UpperCAmelCase__			:							str							=    True
        if right_element < a or left_element > b:
              return -math.inf
        if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
        UpperCAmelCase__			:							Union[str, Any]							=    (left_element + right_element) // 2
        UpperCAmelCase__			:							Tuple							=    self.query(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    self.query(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        return max(_lowerCamelCase							,       _lowerCamelCase	)




  def __str__(self	):


        """simple docstring"""

        return str([self.query(1							,       1							,       self.size							,       _lowerCamelCase							,       _lowerCamelCase	) for i in range(1							,       self.size + 1	)]	)


if __name__ == "__main__":
    _A							=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    _A							=  15
    _A							=  SegmentTree(size)
    segt.build(1, 1, size, A)
    print(segt.query(1, 1, size, 4, 6))
    print(segt.query(1, 1, size, 7, 11))
    print(segt.query(1, 1, size, 7, 12))
    segt.update(1, 1, size, 1, 3, 1_11)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 2_35)
    print(segt)

