'''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
_UpperCAmelCase    :     List[str]         =       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    :     str         =       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_UpperCAmelCase    :     str         =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str				):

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







@dataclass
class lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   field(
       default=SCREAMING_SNAKE_CASE__    ,    metadata={
           "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
       }    ,    )
   __lowerCAmelCase         =   field(
       default=SCREAMING_SNAKE_CASE__    ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
   __lowerCAmelCase         =   field(default=SCREAMING_SNAKE_CASE__    ,    metadata={"help": "A folder containing the training data."}			)
   __lowerCAmelCase         =   field(default=SCREAMING_SNAKE_CASE__    ,    metadata={"help": "A folder containing the validation data."}			)
   __lowerCAmelCase         =   field(
       default=0.1_5    ,    metadata={"help": "Percent to split off of train for validation."}			)
   __lowerCAmelCase         =   field(
       default=SCREAMING_SNAKE_CASE__    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of training examples to this "
               "value if set."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=SCREAMING_SNAKE_CASE__    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }    ,    )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        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 lowercase_       :







   """simple docstring"""

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






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _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  _SCREAMING_SNAKE_CASE		(      ):
     _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'   ,		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()

     _A    		=  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.
     _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=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.
     _A    		=  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:
          _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(a_				):
          _A    		=  str(a_				)
          _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(__snake_case							:			List[Any]				):
          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(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   ,		)
     _A    		=  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   ,		)
     _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(a_				),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]				)
     _A    		=  Compose(
         [
             Resize(a_				),
             CenterCrop(a_				),
             ToTensor(),
             normalize,
         ]				)

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

     def val_transforms(__snake_case							:			int				):
          _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(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:
               _A    		=  (
                   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
     _A    		=  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:
          _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=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:
          _A    		=  trainer.evaluate()
          trainer.log_metrics('eval'   ,		a_				)
          trainer.save_metrics('eval'   ,		a_				)

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


if __name__ == "__main__":
   main()











'''simple docstring'''

from __future__ import annotations



def 				snake_case       (  a_				:			list[int]     , a_				:			list[int]     , a_				:			list[int]     , a_				:			list[list[str]]     , a_				:			int     , )							->					None:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				len(a_						)

      # If row is equal to the size of the board it means there are a queen in each row in
      # the current board (possible_board)
      if row == n:
            # We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
            # this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
            boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board]						)
            return

      # We iterate each column in the row to find all possible results in each row
      for col in range(a_						):
            # We apply that we learned previously. First we check that in the current board
            # (possible_board) there are not other same value because if there is it means
            # that there are a collision in vertical. Then we apply the two formulas we
            # learned before:
            #
            # 45º: y - x = b or 45: row - col = b
            # 135º: y + x = b or row + col = b.
            #
            # And we verify if the results of this two formulas not exist in their variables
            # respectively.  (diagonal_right_collisions, diagonal_left_collisions)
            #
            # If any or these are True it means there is a collision so we continue to the
            # next value in the for loop.
            if (
                col in possible_board
                or row - col in diagonal_right_collisions
                or row + col in diagonal_left_collisions
            ):
                  continue

            # If it is False we call dfs function again and we update the inputs
            depth_first_search(
                [*possible_board, col]     , [*diagonal_right_collisions, row - col]     , [*diagonal_left_collisions, row + col]     , a_     , a_     , )



def 				snake_case       (  a_				:			int						)							->					None:





      """simple docstring"""

      UpperCamelCase_					: list[list[str]]            =				[]
      depth_first_search([]     , []     , []     , a_     , a_						)

      # Print all the boards
      for board in boards:
            for column in board:
                  print(a_						)
            print(""""""						)

      print(len(a_						)     , """solutions were found."""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      n_queens_solution(4)






