


'''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
__lowercase     :						Optional[Any]	=     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')

__lowercase     :						Dict	=     list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowercase     :						Tuple	=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					with open(_SCREAMING_SNAKE_CASE						,				'rb'       ) as f:
										__a      :	Dict    =  Image.open(_SCREAMING_SNAKE_CASE       )
										return im.convert('RGB'       )





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




	def 				__UpperCAmelCase							(       self							):

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					__a      :	Tuple    =  torch.stack([example['pixel_values'] for example in examples]       )
					__a      :	str    =  torch.tensor([example['labels'] for example in examples]       )
					return {"pixel_values": pixel_values, "labels": labels}






def   lowerCamelCase							():
					# 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.

					__a      :	List[str]    =  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   ,    __a   ,    __a      :	str    =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
					else:
										__a   ,    __a   ,    __a      :	Union[str, Any]    =  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()

					__a      :	Optional[int]    =  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.
					__a      :	Optional[int]    =  None
					if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
										__a      :	int    =  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      :	Optional[Any]    =  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      :	Union[str, Any]    =  {}
										if data_args.train_dir is not None:
															__a      :	Any    =  os.path.join(data_args.train_dir						,				'**'       )
										if data_args.validation_dir is not None:
															__a      :	Optional[Any]    =  os.path.join(data_args.validation_dir						,				'**'       )
										__a      :	Any    =  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.
					__a      :	Optional[Any]    =  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:
										__a      :	Any    =  dataset['train'].train_test_split(data_args.train_val_split       )
										__a      :	str    =  split['train']
										__a      :	Optional[int]    =  split['test']

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

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

					__a      :	Union[str, Any]    =  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						,				)
					__a      :	Union[str, Any]    =  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						,				)
					__a      :	Tuple    =  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      :	List[Any]    =  image_processor.size['shortest_edge']
					else:
										__a      :	str    =  (image_processor.size['height'], image_processor.size['width'])
					__a      :	int    =  Normalize(mean=image_processor.image_mean						,				std=image_processor.image_std       )
					__a      :	List[Any]    =  Compose(
					    [
					        RandomResizedCrop(_SCREAMING_SNAKE_CASE       ),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        normalize,
					    ]       )
					__a      :	str    =  Compose(
					    [
					        Resize(_SCREAMING_SNAKE_CASE       ),
					        CenterCrop(_SCREAMING_SNAKE_CASE       ),
					        ToTensor(),
					        normalize,
					    ]       )

					def train_transforms(_SCREAMING_SNAKE_CASE				:       Optional[int]       ):
										__a      :	Any    =  [
										    _train_transforms(pil_img.convert('RGB'       )       ) for pil_img in example_batch['image']
										]
										return example_batch

					def val_transforms(_SCREAMING_SNAKE_CASE				:       int       ):
										__a      :	Optional[int]    =  [_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      :	List[str]    =  (
															    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:
															__a      :	Tuple    =  (
															    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
					__a      :	Optional[int]    =  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:
										__a      :	Dict    =  None
										if training_args.resume_from_checkpoint is not None:
															__a      :	Union[str, Any]    =  training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															__a      :	Any    =  last_checkpoint
										__a      :	List[Any]    =  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:
										__a      :	str    =  trainer.evaluate()
										trainer.log_metrics('eval'						,				_SCREAMING_SNAKE_CASE       )
										trainer.save_metrics('eval'						,				_SCREAMING_SNAKE_CASE       )

					# Write model card and (optionally) push to hub
					__a      :	List[Any]    =  {
					    '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'''



import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__lowercase     :						str	=     {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'
}






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str = "dhaka"						,				_SCREAMING_SNAKE_CASE				:       int = 5       ):
					__a      :	Optional[Any]    =  min(_SCREAMING_SNAKE_CASE						,				50       )  # Prevent abuse!
					__a      :	Optional[Any]    =  {
					    'q': query,
					    'tbm': 'isch',
					    'hl': 'en',
					    'ijn': '0',
					}

					__a      :	Tuple    =  requests.get('https://www.google.com/search'						,				params=_SCREAMING_SNAKE_CASE						,				headers=_SCREAMING_SNAKE_CASE       )
					__a      :	Dict    =  BeautifulSoup(html.text						,				'html.parser'       )
					__a      :	List[str]    =  ''.join(
					    re.findall(r'AF_initDataCallback\(([^<]+)\);'						,				str(soup.select('script'       )       )       )       )

					__a      :	Optional[Any]    =  json.dumps(_SCREAMING_SNAKE_CASE       )
					__a      :	List[str]    =  json.loads(_SCREAMING_SNAKE_CASE       )

					__a      :	List[Any]    =  re.findall(
					    r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",'						,				_SCREAMING_SNAKE_CASE						,				)
					if not matched_google_image_data:
										return 0

					__a      :	Tuple    =  re.sub(
					    r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]'						,				''						,				str(_SCREAMING_SNAKE_CASE       )						,				)

					__a      :	Optional[Any]    =  re.findall(
					    r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'						,				_SCREAMING_SNAKE_CASE						,				)
					for index, fixed_full_res_image in enumerate(_SCREAMING_SNAKE_CASE       ):
										if index >= max_images:
															return index
										__a      :	List[str]    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Tuple    =  bytes(_SCREAMING_SNAKE_CASE						,				'ascii'       ).decode(
										    'unicode-escape'       )
										__a      :	Dict    =  urllib.request.build_opener()
										__a      :	Union[str, Any]    =  [
										    (
										        'User-Agent',
										        'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
										        ' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
										    )
										]
										urllib.request.install_opener(_SCREAMING_SNAKE_CASE       )
										__a      :	List[Any]    =  F"""query_{query.replace(" "						,				"_"       )}"""
										if not os.path.exists(_SCREAMING_SNAKE_CASE       ):
															os.makedirs(_SCREAMING_SNAKE_CASE       )
										urllib.request.urlretrieve(  # noqa: S310
										    _SCREAMING_SNAKE_CASE						,				F"""{path_name}/original_size_img_{index}.jpg"""       )
					return index


if __name__ == "__main__":
	try:
		__lowercase     :						Optional[int]	=     download_images_from_google_query(sys.argv[1])
		print(f'''{image_count} images were downloaded to disk.''')
	except IndexError:
		print('Please provide a search term.')
		raise



