




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
_lowerCamelCase			:	Optional[int]							 =     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""")

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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



       """simple docstring"""

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



@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   field(
        default=UpperCamelCase_	,      metadata={
            '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCamelCase_	,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
    UpperCAmelCase__													=   field(default=UpperCamelCase_	,      metadata={'''help''': '''A folder containing the training data.'''}  )
    UpperCAmelCase__													=   field(default=UpperCamelCase_	,      metadata={'''help''': '''A folder containing the validation data.'''}  )
    UpperCAmelCase__													=   field(
        default=0.1_5	,      metadata={'''help''': '''Percent to split off of train for validation.'''}  )
    UpperCAmelCase__													=   field(
        default=UpperCamelCase_	,      metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                '''value if set.'''
            )
        }	,      )
    UpperCAmelCase__													=   field(
        default=UpperCamelCase_	,      metadata={
            '''help''': (
                '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                '''value if set.'''
            )
        }	,      )


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''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        UpperCamelCase_					:

    '''simple docstring'''






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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[int]:



       """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 SCREAMING_SNAKE_CASE       ( )     -> List[Any]:



       """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'''				,       snake_case_				,       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()

       A__   				=					training_args.get_process_log_level()
       logger.setLevel(snake_case_		)
       transformers.utils.logging.set_verbosity(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.
       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=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.
       A__   				=					None if '''validation''' in dataset.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split				,       snake_case_		) 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(snake_case_		):
              A__   				=					str(snake_case_		)
              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(lowercase_		):
              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(snake_case_		)				,       labelaid=snake_case_				,       idalabel=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				,       )
       A__   				=					AutoModelForImageClassification.from_pretrained(
           model_args.model_name_or_path				,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)				,       config=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				,       )
       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(snake_case_		),
               RandomHorizontalFlip(),
               ToTensor(),
               normalize,
           ]		)
       A__   				=					Compose(
           [
               Resize(snake_case_		),
               CenterCrop(snake_case_		),
               ToTensor(),
               normalize,
           ]		)

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

       def val_transforms(lowercase_		):
              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(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:
                     A__   				=					(
                         dataset['''validation'''].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
                     )
              # Set the validation transforms
              dataset["validation"].set_transform(snake_case_		)

       # Initalize our trainer
       A__   				=					Trainer(
           model=snake_case_				,       args=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=snake_case_				,       tokenizer=snake_case_				,       data_collator=snake_case_				,       )

       # 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=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:
              A__   				=					trainer.evaluate()
              trainer.log_metrics('''eval'''				,       snake_case_		)
              trainer.save_metrics('''eval'''				,       snake_case_		)

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


if __name__ == "__main__":
  main()








# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       return 1 / (1 + np.exp(-z		))







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       return (-y * np.log(lowercase_		) - (1 - y) * np.log(1 - h		)).mean()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					np.dot(lowercase_				,       lowercase_		)
       return np.sum(y * scores - np.log(1 + np.exp(lowercase_		)		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=70_000		)     -> Dict:



       """simple docstring"""

       A__   				=					np.zeros(x.shape[1]		)

       for iterations in range(lowercase_		):
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					np.dot(x.T				,       h - y		) / y.size
              A__   				=					theta - alpha * gradient  # updating the weights
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					cost_function(lowercase_				,       lowercase_		)
              if iterations % 100 == 0:
                     print(f"""loss: {j} \t"""		)  # printing the loss after every 100 iterations
       return theta


# In[68]:

if __name__ == "__main__":
  _lowerCamelCase			:	str							 =     datasets.load_iris()
  _lowerCamelCase			:	Any							 =     iris.data[:, :2]
  _lowerCamelCase			:	Dict							 =     (iris.target != 0) * 1

  _lowerCamelCase			:	Union[str, Any]							 =     0.1
  _lowerCamelCase			:	Tuple							 =     logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowercase_				,       lowercase_		)		)  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	int							 =     (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	Union[str, Any]							 =     (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	List[Any]							 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase			:	str							 =     np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase			:	Tuple							 =     predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()


