


"""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
__snake_case				=				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''')

__snake_case				=				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__snake_case				=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def 					A_      (      _lowerCAmelCase   :    Union[str, Any]				):






     """simple docstring"""

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


@dataclass
class 			__lowerCamelCase     :

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


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          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 			__lowerCamelCase     :

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







def 					A_      (      _lowerCAmelCase   :    Any				):






     """simple docstring"""

     _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 					A_      (      ):






     """simple docstring"""

     _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''',   UpperCamelCase__,   UpperCamelCase__				)

     # 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(UpperCamelCase__				)
     transformers.utils.logging.set_verbosity(UpperCamelCase__				)
     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=UpperCamelCase__,   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,   UpperCamelCase__				) 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(UpperCamelCase__				):
          _a					= str(UpperCamelCase__				)
          _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(_lowerCAmelCase   :    List[str]				):
          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(UpperCamelCase__				),   labelaid=UpperCamelCase__,   idalabel=UpperCamelCase__,   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=UpperCamelCase__,   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(UpperCamelCase__				),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]				)
     _a					= Compose(
         [
             Resize(UpperCamelCase__				),
             CenterCrop(UpperCamelCase__				),
             ToTensor(),
             normalize,
         ]				)

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

     def val_transforms(_lowerCAmelCase   :    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(UpperCamelCase__				)

     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(UpperCamelCase__				)

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

     # 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=UpperCamelCase__				)
          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''',   UpperCamelCase__				)
          trainer.save_metrics('''eval''',   UpperCamelCase__				)

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


if __name__ == "__main__":
   main()







"""simple docstring"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''

  # Check if the input is valid
  if not len(UpperCamelCase__				) == len(UpperCamelCase__				) == 3:
    raise ValueError("""Please enter a valid equation."""				)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError("""Both a & b of two equations can't be zero."""				)

  # Extract the coefficients
  _a      ,  _a      ,  _a  :       Any             =	equationa
  _a      ,  _a      ,  _a  :       Tuple             =	equationa

  # Calculate the determinants of the matrices
  _a  :       int             =	aa * ba - aa * ba
  _a  :       str             =	ca * ba - ca * ba
  _a  :       str             =	aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError("""Infinite solutions. (Consistent system)"""				)
    else:
      raise ValueError("""No solution. (Inconsistent system)"""				)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      _a  :       Dict             =	determinant_x / determinant
      _a  :       str             =	determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)

