






import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ImageGPTImageProcessor







class 				UpperCAmelCase_  (		unittest.TestCase		):




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase=7	,						_lowerCAmelCase=3	,						_lowerCAmelCase=18	,						_lowerCAmelCase=30	,						_lowerCAmelCase=400	,						_lowerCAmelCase=True	,						_lowerCAmelCase=None	,						_lowerCAmelCase=True	,						):
          UpperCAmelCase__      :  List[str]				      =			size if size is not None else {"""height""": 18, """width""": 18}
          UpperCAmelCase__      :  Union[str, Any]				      =			parent
          UpperCAmelCase__      :  int				      =			batch_size
          UpperCAmelCase__      :  Tuple				      =			num_channels
          UpperCAmelCase__      :  Dict				      =			image_size
          UpperCAmelCase__      :  List[Any]				      =			min_resolution
          UpperCAmelCase__      :  str				      =			max_resolution
          UpperCAmelCase__      :  Union[str, Any]				      =			do_resize
          UpperCAmelCase__      :  Tuple				      =			size
          UpperCAmelCase__      :  int				      =			do_normalize




     def   __UpperCAmelCase  (				self		):
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                      [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
                  ]		),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }







@require_torch
@require_vision
class 				UpperCAmelCase_  (		__lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  ImageGPTImageProcessor if is_vision_available() else None




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Any				      =			ImageGPTImageProcessingTester(self		)




     @property
     def   __UpperCAmelCase  (				self		):
          return self.image_processor_tester.prepare_image_processor_dict()




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  str				      =			self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""clusters"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_resize"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""size"""		)		)
          self.assertTrue(hasattr(_lowerCAmelCase	,						"""do_normalize"""		)		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[Any]				      =			self.image_processing_class.from_dict(self.image_processor_dict		)
          self.assertEqual(image_processor.size	,						{"""height""": 18, """width""": 18}		)

          UpperCAmelCase__      :  Optional[Any]				      =			self.image_processing_class.from_dict(self.image_processor_dict	,						size=42		)
          self.assertEqual(image_processor.size	,						{"""height""": 42, """width""": 42}		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			self.image_processing_class(**self.image_processor_dict		)
          UpperCAmelCase__      :  Optional[int]				      =			json.loads(image_processor.to_json_string()		)
          for key, value in self.image_processor_dict.items():
               if key == "clusters":
                    self.assertTrue(np.array_equal(_lowerCAmelCase	,						obj[key]		)		)
               else:
                    self.assertEqual(obj[key]	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			self.image_processing_class(**self.image_processor_dict		)

          with tempfile.TemporaryDirectory() as tmpdirname:
               UpperCAmelCase__      :  Union[str, Any]				      =			os.path.join(_lowerCAmelCase	,						"""image_processor.json"""		)
               image_processor_first.to_json_file(_lowerCAmelCase		)
               UpperCAmelCase__      :  Optional[Any]				      =			self.image_processing_class.from_json_file(_lowerCAmelCase		).to_dict()

          UpperCAmelCase__      :  Dict				      =			image_processor_first.to_dict()
          for key, value in image_processor_first.items():
               if key == "clusters":
                    self.assertTrue(np.array_equal(_lowerCAmelCase	,						image_processor_second[key]		)		)
               else:
                    self.assertEqual(image_processor_first[key]	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  str				      =			self.image_processing_class(**self.image_processor_dict		)

          with tempfile.TemporaryDirectory() as tmpdirname:
               image_processor_first.save_pretrained(_lowerCAmelCase		)
               UpperCAmelCase__      :  List[Any]				      =			self.image_processing_class.from_pretrained(_lowerCAmelCase		).to_dict()

          UpperCAmelCase__      :  Tuple				      =			image_processor_first.to_dict()
          for key, value in image_processor_first.items():
               if key == "clusters":
                    self.assertTrue(np.array_equal(_lowerCAmelCase	,						image_processor_second[key]		)		)
               else:
                    self.assertEqual(image_processor_first[key]	,						_lowerCAmelCase		)




     @unittest.skip("""ImageGPT requires clusters at initialization"""		)
     def   __UpperCAmelCase  (				self		):
          pass





def  _lowerCamelCase						(						) ->			Tuple:

     '''simple docstring'''
     UpperCAmelCase__      :  Any				      =			load_dataset("""hf-internal-testing/fixtures_image_utils""" ,							split="""test"""       )

     UpperCAmelCase__      :  Dict				      =			Image.open(dataset[4]["""file"""]       )
     UpperCAmelCase__      :  Optional[Any]				      =			Image.open(dataset[5]["""file"""]       )

     UpperCAmelCase__      :  List[Any]				      =			[imagea, imagea]

     return images







@require_vision
@require_torch
class 				UpperCAmelCase_  (		unittest.TestCase		):




     @slow
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Tuple				      =			ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""		)

          UpperCAmelCase__      :  int				      =			prepare_images()

          # test non-batched
          UpperCAmelCase__      :  List[str]				      =			image_processing(images[0]	,						return_tensors="""pt"""		)

          self.assertIsInstance(encoding.input_ids	,						torch.LongTensor		)
          self.assertEqual(encoding.input_ids.shape	,						(1, 1024)		)

          UpperCAmelCase__      :  List[Any]				      =			[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist()	,						_lowerCAmelCase		)

          # test batched
          UpperCAmelCase__      :  List[str]				      =			image_processing(_lowerCAmelCase	,						return_tensors="""pt"""		)

          self.assertIsInstance(encoding.input_ids	,						torch.LongTensor		)
          self.assertEqual(encoding.input_ids.shape	,						(2, 1024)		)

          UpperCAmelCase__      :  Any				      =			[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist()	,						_lowerCAmelCase		)














"""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')
