import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 					__SCREAMING_SNAKE_CASE       (       UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase):






       """simple docstring"""

       __UpperCAmelCase       =					StableDiffusionXLImgaImgPipeline
       __UpperCAmelCase       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
       __UpperCAmelCase       =					PipelineTesterMixin.required_optional_params - {"latents"}
       __UpperCAmelCase       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       __UpperCAmelCase       =					IMAGE_TO_IMAGE_IMAGE_PARAMS
       __UpperCAmelCase       =					IMAGE_TO_IMAGE_IMAGE_PARAMS







       def      lowercase_   ( self				):
          torch.manual_seed(0				)
          __snake_case   :			Tuple										=						UNetaDConditionModel(
              block_out_channels=(32, 64)			,		layers_per_block=2			,		sample_size=32			,		in_channels=4			,		out_channels=4			,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,		attention_head_dim=(2, 4)			,		use_linear_projection=__a			,		addition_embed_type='text_time'			,		addition_time_embed_dim=8			,		transformer_layers_per_block=(1, 2)			,		projection_class_embeddings_input_dim=80			,		cross_attention_dim=64			,		)
          __snake_case   :			str										=						EulerDiscreteScheduler(
              beta_start=0.00085			,		beta_end=0.012			,		steps_offset=1			,		beta_schedule='scaled_linear'			,		timestep_spacing='leading'			,		)
          torch.manual_seed(0				)
          __snake_case   :			List[str]										=						AutoencoderKL(
              block_out_channels=[32, 64]			,		in_channels=3			,		out_channels=3			,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,		latent_channels=4			,		sample_size=128			,		)
          torch.manual_seed(0				)
          __snake_case   :			List[Any]										=						CLIPTextConfig(
              bos_token_id=0			,		eos_token_id=2			,		hidden_size=32			,		intermediate_size=37			,		layer_norm_eps=1E-05			,		num_attention_heads=4			,		num_hidden_layers=5			,		pad_token_id=1			,		vocab_size=1_000			,		hidden_act='gelu'			,		projection_dim=32			,		)
          __snake_case   :			Dict										=						CLIPTextModel(__a				)
          __snake_case   :			Tuple										=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,		local_files_only=__a				)

          __snake_case   :			Dict										=						CLIPTextModelWithProjection(__a				)
          __snake_case   :			str										=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			,		local_files_only=__a				)

          __snake_case   :			List[Any]										=						{
              'unet': unet,
              'scheduler': scheduler,
              'vae': vae,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'text_encoder_2': text_encoder_a,
              'tokenizer_2': tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components







       def      lowercase_   ( self			,		_UpperCAmelCase			,		_UpperCAmelCase=0				):
          __snake_case   :			List[Any]										=						floats_tensor((1, 3, 32, 32)			,		rng=random.Random(__a				)				).to(__a				)
          __snake_case   :			str										=						image / 2 + 0.5
          if str(__a				).startswith('mps'				):
             __snake_case   :			Union[str, Any]										=						torch.manual_seed(__a				)
          else:
             __snake_case   :			List[Any]										=						torch.Generator(device=__a				).manual_seed(__a				)
          __snake_case   :			Dict										=						{
              'prompt': 'A painting of a squirrel eating a burger',
              'image': image,
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 5.0,
              'output_type': 'numpy',
              'strength': 0.75,
          }
          return inputs







       def      lowercase_   ( self				):
          __snake_case   :			str										=						'cpu'  # ensure determinism for the device-dependent torch.Generator
          __snake_case   :			List[Any]										=						self.get_dummy_components()
          __snake_case   :			Union[str, Any]										=						StableDiffusionXLImgaImgPipeline(**__a				)
          __snake_case   :			str										=						sd_pipe.to(__a				)
          sd_pipe.set_progress_bar_config(disable=__a				)

          __snake_case   :			Optional[Any]										=						self.get_dummy_inputs(__a				)
          __snake_case   :			Any										=						sd_pipe(**__a				).images
          __snake_case   :			Optional[Any]										=						image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          __snake_case   :			List[Any]										=						np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]				)

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







       def      lowercase_   ( self				):
          super().test_attention_slicing_forward_pass(expected_max_diff=3E-3				)







       def      lowercase_   ( self				):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







       def      lowercase_   ( self				):
          pass







       def      lowercase_   ( self				):
          __snake_case   :			Dict										=						self.get_dummy_components()
          __snake_case   :			Optional[Any]										=						StableDiffusionXLImgaImgPipeline(**__a				)
          __snake_case   :			List[str]										=						sd_pipe.to(__a				)
          __snake_case   :			Optional[Any]										=						sd_pipe.to(__a				)
          sd_pipe.set_progress_bar_config(disable=__a				)

          # forward without prompt embeds
          __snake_case   :			str										=						self.get_dummy_inputs(__a				)
          __snake_case   :			Union[str, Any]										=						3 * ['this is a negative prompt']
          __snake_case   :			Union[str, Any]										=						negative_prompt
          __snake_case   :			str										=						3 * [inputs['prompt']]

          __snake_case   :			Optional[Any]										=						sd_pipe(**__a				)
          __snake_case   :			str										=						output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          __snake_case   :			int										=						self.get_dummy_inputs(__a				)
          __snake_case   :			Tuple										=						3 * ['this is a negative prompt']
          __snake_case   :			Optional[Any]										=						3 * [inputs.pop('prompt'				)]

          (
              (
              __snake_case				
          )						,       (
              __snake_case				
          )						,       (
              __snake_case				
          )						,       (
              __snake_case				
          )						,       				
          )   :			int										=						sd_pipe.encode_prompt(__a			,		negative_prompt=__a				)

          __snake_case   :			List[str]										=						sd_pipe(
              **__a			,		prompt_embeds=__a			,		negative_prompt_embeds=__a			,		pooled_prompt_embeds=__a			,		negative_pooled_prompt_embeds=__a			,		)
          __snake_case   :			Dict										=						output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()				).max() < 1E-4





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






       """simple docstring"""

       def      lowercase_   ( self				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







       def      lowercase_   ( self			,		_UpperCAmelCase			,		_UpperCAmelCase="cpu"			,		_UpperCAmelCase=torch.floataa			,		_UpperCAmelCase=0				):
          __snake_case   :			Optional[Any]										=						torch.Generator(device=__a				).manual_seed(__a				)
          __snake_case   :			Dict										=						np.random.RandomState(__a				).standard_normal((1, 4, 64, 64)				)
          __snake_case   :			Dict										=						torch.from_numpy(__a				).to(device=__a			,		dtype=__a				)
          __snake_case   :			Optional[Any]										=						{
              'prompt': 'a photograph of an astronaut riding a horse',
              'latents': latents,
              'generator': generator,
              'num_inference_steps': 3,
              'guidance_scale': 7.5,
              'output_type': 'numpy',
          }
          return inputs







       def      lowercase_   ( self				):
          __snake_case   :			Any										=						DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'				)
          pipe.to(__a				)
          pipe.set_progress_bar_config(disable=__a				)

          __snake_case   :			Optional[Any]										=						self.get_inputs(__a				)
          __snake_case   :			List[Any]										=						pipe(**__a				).images
          __snake_case   :			Tuple										=						image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          __snake_case   :			int										=						np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]				)
          assert np.abs(image_slice - expected_slice				).max() < 7E-3








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""")