



import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     a_    :  Tuple						  =		False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor





class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def __init__(self					,      __a					,      __a=7					,      __a=3					,      __a=18					,      __a=30					,      __a=4_00					,      __a=None					,      __a=True					,      __a=True					,      __a=None					,      ):






           '''simple docstring'''



           lowerCamelCase             =		size if size is not None else {"height": 20, "width": 20}
           lowerCamelCase             =		parent
           lowerCamelCase             =		batch_size
           lowerCamelCase             =		num_channels
           lowerCamelCase             =		image_size
           lowerCamelCase             =		min_resolution
           lowerCamelCase             =		max_resolution
           lowerCamelCase             =		size
           lowerCamelCase             =		do_normalize
           lowerCamelCase             =		do_convert_rgb
           lowerCamelCase             =		[5_12, 10_24, 20_48, 40_96]
           lowerCamelCase             =		patch_size if patch_size is not None else {"height": 16, "width": 16}







     def     _a   (self     ):






           '''simple docstring'''



           return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
           lowerCamelCase             =		Image.open(requests.get(__a					,      stream=__a     ).raw     ).convert("RGB"     )
           return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,     reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'							,     )
@require_torch
@require_vision
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      PixaStructImageProcessor if is_vision_available() else None







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		PixaStructImageProcessingTester(self     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self.image_processor_tester.prepare_image_processor_dict()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(__a					,      "do_normalize"     )     )
           self.assertTrue(hasattr(__a					,      "do_convert_rgb"     )     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processor_tester.prepare_dummy_image()

           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           lowerCamelCase             =		20_48

           lowerCamelCase             =		image_processor(__a					,      return_tensors="pt"					,      max_patches=__a     )
           self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,      torch.tensor(0.0606     )					,      atol=1E-3					,      rtol=1E-3     )     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           lowerCamelCase             =		prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__a     )
           for image in image_inputs:
                 self.assertIsInstance(__a					,      Image.Image     )

           # Test not batched input
           lowerCamelCase             =		(
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * self.image_processor_tester.num_channels
           ) + 2

           for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 lowerCamelCase             =		image_processor(
                     image_inputs[0]					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

                 # Test batched
                 lowerCamelCase             =		image_processor(
                     __a					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           lowerCamelCase             =		prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__a     )
           for image in image_inputs:
                 self.assertIsInstance(__a					,      Image.Image     )

           # Test not batched input
           lowerCamelCase             =		(
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * self.image_processor_tester.num_channels
           ) + 2

           lowerCamelCase             =		True

           for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 with self.assertRaises(__a     ):
                       lowerCamelCase             =		image_processor(
                           image_inputs[0]					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches

                 lowerCamelCase             =		"Hello"

                 lowerCamelCase             =		image_processor(
                     image_inputs[0]					,      return_tensors="pt"					,      max_patches=__a					,      header_text=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

                 # Test batched
                 lowerCamelCase             =		image_processor(
                     __a					,      return_tensors="pt"					,      max_patches=__a					,      header_text=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           # create random numpy tensors
           lowerCamelCase             =		prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__a					,      numpify=__a     )
           for image in image_inputs:
                 self.assertIsInstance(__a					,      np.ndarray     )

           lowerCamelCase             =		(
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * self.image_processor_tester.num_channels
           ) + 2

           for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 lowerCamelCase             =		image_processor(
                     image_inputs[0]					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

                 # Test batched
                 lowerCamelCase             =		image_processor(
                     __a					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           # create random PyTorch tensors
           lowerCamelCase             =		prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__a					,      torchify=__a     )
           for image in image_inputs:
                 self.assertIsInstance(__a					,      torch.Tensor     )

           # Test not batched input
           lowerCamelCase             =		(
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * self.image_processor_tester.num_channels
           ) + 2

           for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 lowerCamelCase             =		image_processor(
                     image_inputs[0]					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

                 # Test batched
                 lowerCamelCase             =		image_processor(
                     __a					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,     reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'							,     )
@require_torch
@require_vision
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      PixaStructImageProcessor if is_vision_available() else None







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		PixaStructImageProcessingTester(self					,      num_channels=4     )
           lowerCamelCase             =		3







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self.image_processor_tester.prepare_image_processor_dict()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(__a					,      "do_normalize"     )     )
           self.assertTrue(hasattr(__a					,      "do_convert_rgb"     )     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           lowerCamelCase             =		prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__a     )
           for image in image_inputs:
                 self.assertIsInstance(__a					,      Image.Image     )

           # Test not batched input
           lowerCamelCase             =		(
               (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
               * (self.image_processor_tester.num_channels - 1)
           ) + 2

           for max_patch in self.image_processor_tester.max_patches:
                 # Test not batched input
                 lowerCamelCase             =		image_processor(
                     image_inputs[0]					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (1, max_patch, expected_hidden_dim)					,      )

                 # Test batched
                 lowerCamelCase             =		image_processor(
                     __a					,      return_tensors="pt"					,      max_patches=__a     ).flattened_patches
                 self.assertEqual(
                     encoded_images.shape					,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,      )




from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def      __lowercase(						UpperCAmelCase__ = "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(UpperCAmelCase__ , headers=UpperCAmelCase__			).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("," , ""			)			)
                            )
                            * 100			)
                  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__":
     a_    :  Dict						  =		'headphones'
     get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")