






"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class      UpperCamelCase		(unittest.TestCase     ):






						def      __snake_case      (		self  :int				)      ->Optional[int]:
										lowercase    :      Tuple       = tempfile.mkdtemp()

										lowercase    :      Tuple       = [
										    """[UNK]""",
										    """[CLS]""",
										    """[SEP]""",
										    """[PAD]""",
										    """[MASK]""",
										    """want""",
										    """##want""",
										    """##ed""",
										    """wa""",
										    """un""",
										    """runn""",
										    """##ing""",
										    """,""",
										    """low""",
										    """lowest""",
										]
										lowercase    :      str       = os.path.join(self.tmpdirname			, VOCAB_FILES_NAMES["""vocab_file"""]				)
										with open(self.vocab_file			, """w"""			, encoding="""utf-8"""				) as vocab_writer:
														vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

										lowercase    :      List[Any]       = {
										    """do_resize""": True,
										    """size""": 20,
										    """do_center_crop""": True,
										    """crop_size""": 18,
										    """do_normalize""": True,
										    """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
										    """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
										}
										lowercase    :      Dict       = os.path.join(self.tmpdirname			, __magic_name__				)
										with open(self.image_processor_file			, """w"""			, encoding="""utf-8"""				) as fp:
														json.dump(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :List[Any]			, **__magic_name__  :str				)      ->List[Any]:
										return BertTokenizer.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :str			, **__magic_name__  :List[Any]				)      ->Optional[Any]:
										return BertTokenizerFast.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :List[str]			, **__magic_name__  :List[str]				)      ->str:
										return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			, **__magic_name__				)






						def      __snake_case      (		self  :List[str]				)      ->Any:
										shutil.rmtree(self.tmpdirname				)






						def      __snake_case      (		self  :Any				)      ->Tuple:
										lowercase    :      Union[str, Any]       = [np.random.randint(255			, size=(3, 30, 400)			, dtype=np.uinta				)]
										lowercase    :      int       = [Image.fromarray(np.moveaxis(__magic_name__			, 0			, -1				)				) for x in image_inputs]
										return image_inputs






						def      __snake_case      (		self  :List[Any]				)      ->Optional[int]:
										lowercase    :      Optional[Any]       = self.get_tokenizer()
										lowercase    :      Union[str, Any]       = self.get_rust_tokenizer()
										lowercase    :      Optional[Any]       = self.get_image_processor()

										lowercase    :      Optional[Any]       = AlignProcessor(tokenizer=__magic_name__			, image_processor=__magic_name__				)
										processor_slow.save_pretrained(self.tmpdirname				)
										lowercase    :      List[str]       = AlignProcessor.from_pretrained(self.tmpdirname			, use_fast=__magic_name__				)

										lowercase    :      Union[str, Any]       = AlignProcessor(tokenizer=__magic_name__			, image_processor=__magic_name__				)
										processor_fast.save_pretrained(self.tmpdirname				)
										lowercase    :      int       = AlignProcessor.from_pretrained(self.tmpdirname				)

										self.assertEqual(processor_slow.tokenizer.get_vocab()			, tokenizer_slow.get_vocab()				)
										self.assertEqual(processor_fast.tokenizer.get_vocab()			, tokenizer_fast.get_vocab()				)
										self.assertEqual(tokenizer_slow.get_vocab()			, tokenizer_fast.get_vocab()				)
										self.assertIsInstance(processor_slow.tokenizer			, __magic_name__				)
										self.assertIsInstance(processor_fast.tokenizer			, __magic_name__				)

										self.assertEqual(processor_slow.image_processor.to_json_string()			, image_processor.to_json_string()				)
										self.assertEqual(processor_fast.image_processor.to_json_string()			, image_processor.to_json_string()				)
										self.assertIsInstance(processor_slow.image_processor			, __magic_name__				)
										self.assertIsInstance(processor_fast.image_processor			, __magic_name__				)






						def      __snake_case      (		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      Optional[int]       = AlignProcessor(tokenizer=self.get_tokenizer()			, image_processor=self.get_image_processor()				)
										processor.save_pretrained(self.tmpdirname				)

										lowercase    :      Any       = self.get_tokenizer(bos_token="""(BOS)"""			, eos_token="""(EOS)"""				)
										lowercase    :      int       = self.get_image_processor(do_normalize=__magic_name__			, padding_value=1.0				)

										lowercase    :      List[str]       = AlignProcessor.from_pretrained(
										    self.tmpdirname			, bos_token="""(BOS)"""			, eos_token="""(EOS)"""			, do_normalize=__magic_name__			, padding_value=1.0				)

										self.assertEqual(processor.tokenizer.get_vocab()			, tokenizer_add_kwargs.get_vocab()				)
										self.assertIsInstance(processor.tokenizer			, __magic_name__				)

										self.assertEqual(processor.image_processor.to_json_string()			, image_processor_add_kwargs.to_json_string()				)
										self.assertIsInstance(processor.image_processor			, __magic_name__				)






						def      __snake_case      (		self  :int				)      ->Union[str, Any]:
										lowercase    :      Optional[Any]       = self.get_image_processor()
										lowercase    :      Optional[int]       = self.get_tokenizer()

										lowercase    :      Optional[int]       = AlignProcessor(tokenizer=__magic_name__			, image_processor=__magic_name__				)

										lowercase    :      Any       = self.prepare_image_inputs()

										lowercase    :      Dict       = image_processor(__magic_name__			, return_tensors="""np"""				)
										lowercase    :      Any       = processor(images=__magic_name__			, return_tensors="""np"""				)

										for key in input_image_proc.keys():
														self.assertAlmostEqual(input_image_proc[key].sum()			, input_processor[key].sum()			, delta=1E-2				)






						def      __snake_case      (		self  :Optional[int]				)      ->Optional[Any]:
										lowercase    :      List[str]       = self.get_image_processor()
										lowercase    :      Any       = self.get_tokenizer()

										lowercase    :      Any       = AlignProcessor(tokenizer=__magic_name__			, image_processor=__magic_name__				)

										lowercase    :      int       = """lower newer"""

										lowercase    :      int       = processor(text=__magic_name__				)

										lowercase    :      List[Any]       = tokenizer(__magic_name__			, padding="""max_length"""			, max_length=64				)

										for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]			, encoded_processor[key]				)






						def      __snake_case      (		self  :List[str]				)      ->Optional[int]:
										lowercase    :      Dict       = self.get_image_processor()
										lowercase    :      Optional[Any]       = self.get_tokenizer()

										lowercase    :      Tuple       = AlignProcessor(tokenizer=__magic_name__			, image_processor=__magic_name__				)

										lowercase    :      Any       = """lower newer"""
										lowercase    :      Optional[int]       = self.prepare_image_inputs()

										lowercase    :      Any       = processor(text=__magic_name__			, images=__magic_name__				)

										self.assertListEqual(list(inputs.keys()				)			, ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

										# test if it raises when no input is passed
										with pytest.raises(__magic_name__				):
														processor()






						def      __snake_case      (		self  :List[Any]				)      ->List[Any]:
										lowercase    :      Optional[int]       = self.get_image_processor()
										lowercase    :      Any       = self.get_tokenizer()

										lowercase    :      Any       = AlignProcessor(tokenizer=__magic_name__			, image_processor=__magic_name__				)

										lowercase    :      Any       = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowercase    :      Optional[Any]       = processor.batch_decode(__magic_name__				)
										lowercase    :      str       = tokenizer.batch_decode(__magic_name__				)

										self.assertListEqual(__magic_name__			, __magic_name__				)






						def      __snake_case      (		self  :Tuple				)      ->int:
										lowercase    :      Optional[Any]       = self.get_image_processor()
										lowercase    :      Optional[Any]       = self.get_tokenizer()

										lowercase    :      Any       = AlignProcessor(tokenizer=__magic_name__			, image_processor=__magic_name__				)

										lowercase    :      int       = """lower newer"""
										lowercase    :      Optional[int]       = self.prepare_image_inputs()

										lowercase    :      Optional[int]       = processor(text=__magic_name__			, images=__magic_name__				)

										self.assertListEqual(list(inputs.keys()				)			, processor.model_input_names				)








"""simple docstring"""







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


def      UpperCamelCase       ( _A = "laptop" )     ->       DataFrame:
				lowercase    :      List[str]       = F"""https://www.amazon.in/laptop/s?k={product}"""
				lowercase    :      str       = {
				    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
				    """Accept-Language""": """en-US, en;q=0.5""",
				}
				lowercase    :      str       = BeautifulSoup(requests.get(_A  ,				headers=_A ).text )
				# Initialize a Pandas dataframe with the column titles
				lowercase    :      Optional[Any]       = 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:
												lowercase    :      str       = item.ha.text
												lowercase    :      Tuple       = """https://www.amazon.in/""" + item.ha.a["""href"""]
												lowercase    :      Tuple       = item.find("""span"""  ,				attrs={"""class""": """a-offscreen"""} ).text
												try:
																lowercase    :      Any       = item.find("""span"""  ,				attrs={"""class""": """a-icon-alt"""} ).text
												except AttributeError:
																lowercase    :      Tuple       = """Not available"""
												try:
																lowercase    :      Any       = (
																    """₹"""
																    + item.find(
																        """span"""  ,				attrs={"""class""": """a-price a-text-price"""} ).text.split("""₹""" )[1]
																)
												except AttributeError:
																lowercase    :      Any       = """"""
												try:
																lowercase    :      List[str]       = float(
																    (
																        (
																            float(product_mrp.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																            - float(product_price.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																        )
																        / float(product_mrp.strip("""₹""" ).replace(""","""  ,				"""""" ) )
																    )
																    * 100 )
												except ValueError:
																lowercase    :      Tuple       = float("""nan""" )
								except AttributeError:
												pass
								lowercase    :      Union[str, Any]       = [
								    product_title,
								    product_link,
								    product_price,
								    product_rating,
								    product_mrp,
								    discount,
								]
				lowercase    :      Tuple       = """ """
				lowercase    :      List[str]       = """ """
				data_frame.index += 1
				return data_frame


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