




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		:				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')

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




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







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




@dataclass
class     snake_case__							:
				"""simple docstring"""



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





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							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     snake_case__							:
				"""simple docstring"""



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




def 			__magic_name__				( A		:					Dict   ):






			'''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 			__magic_name__				( ):






			'''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    ,		a    ,		a    =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						a    ,		a    ,		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    ,		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(A		:					int   ):
						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(A		:					Dict   ):
						a    =			[
						    _train_transforms(pil_img.convert("RGB"   )   ) for pil_img in example_batch["image"]
						]
						return example_batch

			def val_transforms(A		:					List[str]   ):
						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()












from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str = "laptop"     ):


      '''simple docstring'''



      lowerCamelCase_		     =    f"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase_		     =    {
          'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36',
          'Accept-Language': 'en-US, en;q=0.5',
      }
      lowerCamelCase_		     =    BeautifulSoup(requests.get(lowercase	,							headers=lowercase     ).text     )
      # Initialize a Pandas dataframe with the column titles
      lowerCamelCase_		     =    DataFrame(
          columns=[
              'Product Title',
              'Product Link',
              'Current Price of the product',
              'Product Rating',
              'MRP of the product',
              'Discount',
          ]     )
      # Loop through each entry and store them in the dataframe
      for item, _ in zip_longest(
          soup.find_all(
              'div'	,							attrs={'class': 's-result-item', 'data-component-type': 's-search-result'}	,							)	,							soup.find_all('div'	,							attrs={'class': 'a-row a-size-base a-color-base'}     )	,							):
            try:
                  lowerCamelCase_		     =    item.ha.text
                  lowerCamelCase_		     =    'https://www.amazon.in/' + item.ha.a['href']
                  lowerCamelCase_		     =    item.find('span'	,							attrs={'class': 'a-offscreen'}     ).text
                  try:
                        lowerCamelCase_		     =    item.find('span'	,							attrs={'class': 'a-icon-alt'}     ).text
                  except AttributeError:
                        lowerCamelCase_		     =    'Not available'
                  try:
                        lowerCamelCase_		     =    (
                            '₹'
                            + item.find(
                                'span'	,							attrs={'class': 'a-price a-text-price'}     ).text.split('₹'     )[1]
                        )
                  except AttributeError:
                        lowerCamelCase_		     =    ''
                  try:
                        lowerCamelCase_		     =    float(
                            (
                                (
                                    float(product_mrp.strip('₹'     ).replace(','	,							''     )     )
                                    - float(product_price.strip('₹'     ).replace(','	,							''     )     )
                                )
                                / float(product_mrp.strip('₹'     ).replace(','	,							''     )     )
                            )
                            * 1_00     )
                  except ValueError:
                        lowerCamelCase_		     =    float('nan'     )
            except AttributeError:
                  pass
            lowerCamelCase_		     =    [
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      lowerCamelCase_		     =    ' '
      lowerCamelCase_		     =    ' '
      data_frame.index += 1
      return data_frame


if __name__ == "__main__":
 lowerCamelCase				:		Tuple     =   "headphones"
 get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")



