
import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
				from PIL import Image
else:





				class       _snake_case					:







							@staticmethod
							def 			SCREAMING_SNAKE_CASE    (				*_a						,   **_a		):
								pass





@is_pipeline_test
@require_vision
class       _snake_case					(     unittest.TestCase				):







			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     pipeline(
				    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"						,   )
				__magic_name__		:		List[Any]	   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				__magic_name__		:		Tuple	   =     image_classifier(_a						,   candidate_labels=["a", "b", "c"]		)

				# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
				# python and torch versions.
				self.assertIn(
				    nested_simplify(_a		)						,   [
				        [{"score": 0.3_33, "label": "a"}, {"score": 0.3_33, "label": "b"}, {"score": 0.3_33, "label": "c"}],
				        [{"score": 0.3_33, "label": "a"}, {"score": 0.3_33, "label": "c"}, {"score": 0.3_33, "label": "b"}],
				    ]						,   )

				__magic_name__		:		Optional[Any]	   =     image_classifier([image] * 5						,   candidate_labels=["A", "B", "C"]						,   batch_size=2		)
				self.assertEqual(
				    nested_simplify(_a		)						,   [
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				    ]						,   )







			@require_tf
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     pipeline(
				    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"						,   framework="tf"		)
				__magic_name__		:		int	   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				__magic_name__		:		Any	   =     image_classifier(_a						,   candidate_labels=["a", "b", "c"]		)

				self.assertEqual(
				    nested_simplify(_a		)						,   [{"score": 0.3_33, "label": "a"}, {"score": 0.3_33, "label": "b"}, {"score": 0.3_33, "label": "c"}]						,   )

				__magic_name__		:		int	   =     image_classifier([image] * 5						,   candidate_labels=["A", "B", "C"]						,   batch_size=2		)
				self.assertEqual(
				    nested_simplify(_a		)						,   [
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				        [
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				            {"score": 0.3_33, "label": ANY(_a		)},
				        ],
				    ]						,   )







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     pipeline(
				    task="zero-shot-image-classification"						,   model="openai/clip-vit-base-patch32"						,   )
				# This is an image of 2 cats with remotes and no planes
				__magic_name__		:		str	   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				__magic_name__		:		Union[str, Any]	   =     image_classifier(_a						,   candidate_labels=["cat", "plane", "remote"]		)

				self.assertEqual(
				    nested_simplify(_a		)						,   [
				        {"score": 0.5_11, "label": "remote"},
				        {"score": 0.4_85, "label": "cat"},
				        {"score": 0.0_04, "label": "plane"},
				    ]						,   )

				__magic_name__		:		Optional[Any]	   =     image_classifier([image] * 5						,   candidate_labels=["cat", "plane", "remote"]						,   batch_size=2		)
				self.assertEqual(
				    nested_simplify(_a		)						,   [
				        [
				            {"score": 0.5_11, "label": "remote"},
				            {"score": 0.4_85, "label": "cat"},
				            {"score": 0.0_04, "label": "plane"},
				        ],
				    ]
				    * 5						,   )







			@slow
			@require_tf
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     pipeline(
				    task="zero-shot-image-classification"						,   model="openai/clip-vit-base-patch32"						,   framework="tf"		)
				# This is an image of 2 cats with remotes and no planes
				__magic_name__		:		str	   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				__magic_name__		:		List[str]	   =     image_classifier(_a						,   candidate_labels=["cat", "plane", "remote"]		)
				self.assertEqual(
				    nested_simplify(_a		)						,   [
				        {"score": 0.5_11, "label": "remote"},
				        {"score": 0.4_85, "label": "cat"},
				        {"score": 0.0_04, "label": "plane"},
				    ]						,   )

				__magic_name__		:		Dict	   =     image_classifier([image] * 5						,   candidate_labels=["cat", "plane", "remote"]						,   batch_size=2		)
				self.assertEqual(
				    nested_simplify(_a		)						,   [
				        [
				            {"score": 0.5_11, "label": "remote"},
				            {"score": 0.4_85, "label": "cat"},
				            {"score": 0.0_04, "label": "plane"},
				        ],
				    ]
				    * 5						,   )


from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def   lowerCAmelCase_						(					_snake_case      :  str = "laptop"   )				->		DataFrame:







	'''simple docstring'''


	__magic_name__		:		Tuple	   =     F'''https://www.amazon.in/laptop/s?k={product}'''
	__magic_name__		:		Dict	   =     {
	    "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",
	}
	__magic_name__		:		Tuple	   =     BeautifulSoup(requests.get(_snake_case      ,   headers=_snake_case   ).text   )
	# Initialize a Pandas dataframe with the column titles
	__magic_name__		:		int	   =     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:
			__magic_name__		:		Dict	   =     item.ha.text
			__magic_name__		:		Optional[int]	   =     "https://www.amazon.in/" + item.ha.a["href"]
			__magic_name__		:		Optional[Any]	   =     item.find("span"      ,   attrs={"class": "a-offscreen"}   ).text
			try:
				__magic_name__		:		Union[str, Any]	   =     item.find("span"      ,   attrs={"class": "a-icon-alt"}   ).text
			except AttributeError:
				__magic_name__		:		Dict	   =     "Not available"
			try:
				__magic_name__		:		Optional[int]	   =     (
				    "₹"
				    + item.find(
				        "span"      ,   attrs={"class": "a-price a-text-price"}   ).text.split("₹"   )[1]
				)
			except AttributeError:
				__magic_name__		:		List[str]	   =     ""
			try:
				__magic_name__		:		int	   =     float(
				    (
				        (
				            float(product_mrp.strip("₹"   ).replace(","      ,   ""   )   )
				            - float(product_price.strip("₹"   ).replace(","      ,   ""   )   )
				        )
				        / float(product_mrp.strip("₹"   ).replace(","      ,   ""   )   )
				    )
				    * 100   )
			except ValueError:
				__magic_name__		:		str	   =     float("nan"   )
		except AttributeError:
			pass
		__magic_name__		:		Optional[int]	   =     [
		    product_title,
		    product_link,
		    product_price,
		    product_rating,
		    product_mrp,
		    discount,
		]
	__magic_name__		:		Optional[Any]	   =     " "
	__magic_name__		:		str	   =     " "
	data_frame.index += 1
	return data_frame


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