



"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	UnCLIPImageVariationPipeline
	SCREAMING_SNAKE_CASE          =	IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
	SCREAMING_SNAKE_CASE          =	IMAGE_VARIATION_BATCH_PARAMS

	SCREAMING_SNAKE_CASE          =	[
	    'generator',
	    'return_dict',
	    'decoder_num_inference_steps',
	    'super_res_num_inference_steps',
	]
	SCREAMING_SNAKE_CASE          =	False

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Union[str, Any]:

				"""simple docstring"""

				return 32

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Any:

				"""simple docstring"""

				return 32

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Tuple:

				"""simple docstring"""

				return self.time_input_dim

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       int:

				"""simple docstring"""

				return self.time_input_dim * 4

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Tuple:

				"""simple docstring"""

				return 100

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
				return tokenizer

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Tuple:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase      :  Optional[int]												=       CLIPTextConfig(
				    bos_token_id=0       ,			eos_token_id=2       ,			hidden_size=self.text_embedder_hidden_size       ,			projection_dim=self.text_embedder_hidden_size       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			pad_token_id=1       ,			vocab_size=1000       ,			)
				return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE)

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       str:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase      :  List[str]												=       CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size       ,			projection_dim=self.text_embedder_hidden_size       ,			num_hidden_layers=5       ,			num_attention_heads=4       ,			image_size=32       ,			intermediate_size=37       ,			patch_size=1       ,			)
				return CLIPVisionModelWithProjection(_SCREAMING_SNAKE_CASE)

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Union[str, Any]:

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase      :  List[Any]												=       {
				    "clip_embeddings_dim": self.text_embedder_hidden_size,
				    "time_embed_dim": self.time_embed_dim,
				    "cross_attention_dim": self.cross_attention_dim,
				}

				__lowerCAmelCase      :  Tuple												=       UnCLIPTextProjModel(**_SCREAMING_SNAKE_CASE)
				return model

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Dict:

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase      :  Optional[Any]												=       {
				    "sample_size": 32,
				    # RGB in channels
				    "in_channels": 3,
				    # Out channels is double in channels because predicts mean and variance
				    "out_channels": 6,
				    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
				    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
				    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    "layers_per_block": 1,
				    "cross_attention_dim": self.cross_attention_dim,
				    "attention_head_dim": 4,
				    "resnet_time_scale_shift": "scale_shift",
				    "class_embed_type": "identity",
				}

				__lowerCAmelCase      :  str												=       UNetaDConditionModel(**_SCREAMING_SNAKE_CASE)
				return model

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[int]:

				"""simple docstring"""

				return {
				    "sample_size": 64,
				    "layers_per_block": 1,
				    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
				    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    "in_channels": 6,
				    "out_channels": 3,
				}

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Tuple:

				"""simple docstring"""

				torch.manual_seed(0)

				__lowerCAmelCase      :  Optional[Any]												=       UNetaDModel(**self.dummy_super_res_kwargs)
				return model

	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[Any]:

				"""simple docstring"""

				torch.manual_seed(1)

				__lowerCAmelCase      :  List[str]												=       UNetaDModel(**self.dummy_super_res_kwargs)
				return model

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       self.dummy_decoder
				__lowerCAmelCase      :  str												=       self.dummy_text_proj
				__lowerCAmelCase      :  List[Any]												=       self.dummy_text_encoder
				__lowerCAmelCase      :  int												=       self.dummy_tokenizer
				__lowerCAmelCase      :  Union[str, Any]												=       self.dummy_super_res_first
				__lowerCAmelCase      :  str												=       self.dummy_super_res_last

				__lowerCAmelCase      :  Union[str, Any]												=       UnCLIPScheduler(
				    variance_type="learned_range"       ,			prediction_type="epsilon"       ,			num_train_timesteps=1000       ,			)

				__lowerCAmelCase      :  str												=       UnCLIPScheduler(
				    variance_type="fixed_small_log"       ,			prediction_type="epsilon"       ,			num_train_timesteps=1000       ,			)

				__lowerCAmelCase      :  List[str]												=       CLIPImageProcessor(crop_size=32       ,			size=32)

				__lowerCAmelCase      :  List[str]												=       self.dummy_image_encoder

				return {
				    "decoder": decoder,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "text_proj": text_proj,
				    "feature_extractor": feature_extractor,
				    "image_encoder": image_encoder,
				    "super_res_first": super_res_first,
				    "super_res_last": super_res_last,
				    "decoder_scheduler": decoder_scheduler,
				    "super_res_scheduler": super_res_scheduler,
				}

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]       ,			_SCREAMING_SNAKE_CASE:		str=0       ,			_SCREAMING_SNAKE_CASE:		int=True)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       floats_tensor((1, 3, 32, 32)       ,			rng=random.Random(_SCREAMING_SNAKE_CASE)).to(_SCREAMING_SNAKE_CASE)
				if str(_SCREAMING_SNAKE_CASE).startswith("mps"):
							__lowerCAmelCase      :  List[Any]												=       torch.manual_seed(_SCREAMING_SNAKE_CASE)
				else:
							__lowerCAmelCase      :  str												=       torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(_SCREAMING_SNAKE_CASE)

				if pil_image:
							__lowerCAmelCase      :  str												=       input_image * 0.5 + 0.5
							__lowerCAmelCase      :  Optional[int]												=       input_image.clamp(0       ,			1)
							__lowerCAmelCase      :  int												=       input_image.cpu().permute(0       ,			2       ,			3       ,			1).float().numpy()
							__lowerCAmelCase      :  Optional[int]												=       DiffusionPipeline.numpy_to_pil(_SCREAMING_SNAKE_CASE)[0]

				return {
				    "image": input_image,
				    "generator": generator,
				    "decoder_num_inference_steps": 2,
				    "super_res_num_inference_steps": 2,
				    "output_type": "np",
				}

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       "cpu"

				__lowerCAmelCase      :  int												=       self.get_dummy_components()

				__lowerCAmelCase      :  int												=       self.pipeline_class(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  int												=       pipe.to(_SCREAMING_SNAKE_CASE)

				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Any												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       ,			pil_image=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  int												=       pipe(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Tuple												=       output.images

				__lowerCAmelCase      :  Tuple												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       ,			pil_image=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Optional[int]												=       pipe(
				    **_SCREAMING_SNAKE_CASE       ,			return_dict=_SCREAMING_SNAKE_CASE       ,			)[0]

				__lowerCAmelCase      :  Optional[Any]												=       image[0, -3:, -3:, -1]
				__lowerCAmelCase      :  Optional[Any]												=       image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				__lowerCAmelCase      :  Optional[int]												=       np.array(
				    [
				        0.9997,
				        0.0002,
				        0.9997,
				        0.9997,
				        0.9969,
				        0.0023,
				        0.9997,
				        0.9969,
				        0.9970,
				    ])

				assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       "cpu"

				__lowerCAmelCase      :  List[str]												=       self.get_dummy_components()

				__lowerCAmelCase      :  Dict												=       self.pipeline_class(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Union[str, Any]												=       pipe.to(_SCREAMING_SNAKE_CASE)

				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[Any]												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       ,			pil_image=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Any												=       pipe(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[int]												=       output.images

				__lowerCAmelCase      :  Union[str, Any]												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       ,			pil_image=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Tuple												=       pipe(
				    **_SCREAMING_SNAKE_CASE       ,			return_dict=_SCREAMING_SNAKE_CASE       ,			)[0]

				__lowerCAmelCase      :  Optional[Any]												=       image[0, -3:, -3:, -1]
				__lowerCAmelCase      :  Optional[Any]												=       image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				__lowerCAmelCase      :  Dict												=       np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971])

				assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       "cpu"

				__lowerCAmelCase      :  List[Any]												=       self.get_dummy_components()

				__lowerCAmelCase      :  List[Any]												=       self.pipeline_class(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[int]												=       pipe.to(_SCREAMING_SNAKE_CASE)

				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[Any]												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       ,			pil_image=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       [
				    pipeline_inputs["image"],
				    pipeline_inputs["image"],
				]

				__lowerCAmelCase      :  Optional[int]												=       pipe(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       output.images

				__lowerCAmelCase      :  Dict												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       ,			pil_image=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[Any]												=       [
				    tuple_pipeline_inputs["image"],
				    tuple_pipeline_inputs["image"],
				]

				__lowerCAmelCase      :  Tuple												=       pipe(
				    **_SCREAMING_SNAKE_CASE       ,			return_dict=_SCREAMING_SNAKE_CASE       ,			)[0]

				__lowerCAmelCase      :  Dict												=       image[0, -3:, -3:, -1]
				__lowerCAmelCase      :  int												=       image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (2, 64, 64, 3)

				__lowerCAmelCase      :  List[str]												=       np.array(
				    [
				        0.9997,
				        0.9989,
				        0.0008,
				        0.0021,
				        0.9960,
				        0.0018,
				        0.0014,
				        0.0002,
				        0.9933,
				    ])

				assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       torch.device("cpu")



				class 			A__   :




					'''simple docstring'''





					SCREAMING_SNAKE_CASE          =	1






				__lowerCAmelCase      :  Optional[Any]												=       self.get_dummy_components()

				__lowerCAmelCase      :  List[str]												=       self.pipeline_class(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Tuple												=       pipe.to(_SCREAMING_SNAKE_CASE)

				pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Tuple												=       torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
				__lowerCAmelCase      :  Any												=       pipe.decoder.dtype
				__lowerCAmelCase      :  List[Any]												=       1

				__lowerCAmelCase      :  int												=       (
				    batch_size,
				    pipe.decoder.config.in_channels,
				    pipe.decoder.config.sample_size,
				    pipe.decoder.config.sample_size,
				)
				__lowerCAmelCase      :  Union[str, Any]												=       pipe.prepare_latents(
				    _SCREAMING_SNAKE_CASE       ,			dtype=_SCREAMING_SNAKE_CASE       ,			device=_SCREAMING_SNAKE_CASE       ,			generator=_SCREAMING_SNAKE_CASE       ,			latents=_SCREAMING_SNAKE_CASE       ,			scheduler=DummyScheduler())

				__lowerCAmelCase      :  List[str]												=       (
				    batch_size,
				    pipe.super_res_first.config.in_channels // 2,
				    pipe.super_res_first.config.sample_size,
				    pipe.super_res_first.config.sample_size,
				)
				__lowerCAmelCase      :  Union[str, Any]												=       pipe.prepare_latents(
				    _SCREAMING_SNAKE_CASE       ,			dtype=_SCREAMING_SNAKE_CASE       ,			device=_SCREAMING_SNAKE_CASE       ,			generator=_SCREAMING_SNAKE_CASE       ,			latents=_SCREAMING_SNAKE_CASE       ,			scheduler=DummyScheduler())

				__lowerCAmelCase      :  Union[str, Any]												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       ,			pil_image=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Any												=       pipe(
				    **_SCREAMING_SNAKE_CASE       ,			decoder_latents=_SCREAMING_SNAKE_CASE       ,			super_res_latents=_SCREAMING_SNAKE_CASE).images

				__lowerCAmelCase      :  Dict												=       self.get_dummy_inputs(_SCREAMING_SNAKE_CASE       ,			pil_image=_SCREAMING_SNAKE_CASE)
				# Don't pass image, instead pass embedding
				__lowerCAmelCase      :  Any												=       pipeline_inputs.pop("image")
				__lowerCAmelCase      :  Optional[Any]												=       pipe.image_encoder(_SCREAMING_SNAKE_CASE).image_embeds

				__lowerCAmelCase      :  int												=       pipe(
				    **_SCREAMING_SNAKE_CASE       ,			decoder_latents=_SCREAMING_SNAKE_CASE       ,			super_res_latents=_SCREAMING_SNAKE_CASE       ,			image_embeddings=_SCREAMING_SNAKE_CASE       ,			).images

				# make sure passing text embeddings manually is identical
				assert np.abs(img_out_a - img_out_a).max() < 1e-4

	@skip_mps
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       torch_device == "cpu"

				# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
				__lowerCAmelCase      :  Union[str, Any]												=       1e-2

				self._test_attention_slicing_forward_pass(
				    test_max_difference=_SCREAMING_SNAKE_CASE       ,			expected_max_diff=_SCREAMING_SNAKE_CASE)

	@skip_mps
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       torch_device == "cpu"
				__lowerCAmelCase      :  int												=       True
				__lowerCAmelCase      :  int												=       [
				    "decoder_num_inference_steps",
				    "super_res_num_inference_steps",
				]

				self._test_inference_batch_single_identical(
				    test_max_difference=_SCREAMING_SNAKE_CASE       ,			relax_max_difference=_SCREAMING_SNAKE_CASE       ,			additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       [
				    "decoder_num_inference_steps",
				    "super_res_num_inference_steps",
				]

				if torch_device == "mps":
							# TODO: MPS errors with larger batch sizes
							__lowerCAmelCase      :  Dict												=       [2, 3]
							self._test_inference_batch_consistent(
							    batch_sizes=_SCREAMING_SNAKE_CASE       ,			additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE       ,			)
				else:
							self._test_inference_batch_consistent(
							    additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE)

	@skip_mps
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       str:

				"""simple docstring"""

				return super().test_dict_tuple_outputs_equivalent()

	@skip_mps
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				return super().test_save_load_local()







	@skip_mps
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       str:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png")
				__lowerCAmelCase      :  int												=       load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy")

				__lowerCAmelCase      :  Union[str, Any]												=       UnCLIPImageVariationPipeline.from_pretrained(
				    "kakaobrain/karlo-v1-alpha-image-variations"       ,			torch_dtype=torch.floataa)
				__lowerCAmelCase      :  Optional[Any]												=       pipeline.to(_SCREAMING_SNAKE_CASE)
				pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[str]												=       torch.Generator(device="cpu").manual_seed(0)
				__lowerCAmelCase      :  Any												=       pipeline(
				    _SCREAMING_SNAKE_CASE       ,			generator=_SCREAMING_SNAKE_CASE       ,			output_type="np"       ,			)

				__lowerCAmelCase      :  Optional[int]												=       output.images[0]

				assert image.shape == (256, 256, 3)

				assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			15)




"""simple docstring"""






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


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