






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard


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






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            super().tearDown()
            gc.collect()






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__  ,    lowerCAmelCase__        =  FlaxControlNetModel.from_pretrained(
                'lllyasviel/sd-controlnet-canny'       ,  from_pt=_UpperCamelCase       ,  dtype=jnp.bfloataa				)
            lowerCAmelCase__  ,    lowerCAmelCase__        =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
                'runwayml/stable-diffusion-v1-5'       ,  controlnet=_UpperCamelCase       ,  from_pt=_UpperCamelCase       ,  dtype=jnp.bfloataa				)
            lowerCAmelCase__        =  controlnet_params

            lowerCAmelCase__        =  'bird'
            lowerCAmelCase__        =  jax.device_count()
            lowerCAmelCase__        =  pipe.prepare_text_inputs([prompts] * num_samples				)

            lowerCAmelCase__        =  load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'				)
            lowerCAmelCase__        =  pipe.prepare_image_inputs([canny_image] * num_samples				)

            lowerCAmelCase__        =  jax.random.PRNGKey(0				)
            lowerCAmelCase__        =  jax.random.split(_UpperCamelCase       ,  jax.device_count()				)

            lowerCAmelCase__        =  replicate(_UpperCamelCase				)
            lowerCAmelCase__        =  shard(_UpperCamelCase				)
            lowerCAmelCase__        =  shard(_UpperCamelCase				)

            lowerCAmelCase__        =  pipe(
                prompt_ids=_UpperCamelCase       ,  image=_UpperCamelCase       ,  params=_UpperCamelCase       ,  prng_seed=_UpperCamelCase       ,  num_inference_steps=50       ,  jit=_UpperCamelCase       ,  ).images
            assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

            lowerCAmelCase__        =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
            lowerCAmelCase__        =  images[0, 2_53:2_56, 2_53:2_56, -1]

            lowerCAmelCase__        =  jnp.asarray(jax.device_get(image_slice.flatten()				)				)
            lowerCAmelCase__        =  jnp.array(
                [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]				)
            print(F"output_slice: {output_slice}"				)
            assert jnp.abs(output_slice - expected_slice				).max() < 1E-2







      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__  ,    lowerCAmelCase__        =  FlaxControlNetModel.from_pretrained(
                'lllyasviel/sd-controlnet-openpose'       ,  from_pt=_UpperCamelCase       ,  dtype=jnp.bfloataa				)
            lowerCAmelCase__  ,    lowerCAmelCase__        =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
                'runwayml/stable-diffusion-v1-5'       ,  controlnet=_UpperCamelCase       ,  from_pt=_UpperCamelCase       ,  dtype=jnp.bfloataa				)
            lowerCAmelCase__        =  controlnet_params

            lowerCAmelCase__        =  'Chef in the kitchen'
            lowerCAmelCase__        =  jax.device_count()
            lowerCAmelCase__        =  pipe.prepare_text_inputs([prompts] * num_samples				)

            lowerCAmelCase__        =  load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'				)
            lowerCAmelCase__        =  pipe.prepare_image_inputs([pose_image] * num_samples				)

            lowerCAmelCase__        =  jax.random.PRNGKey(0				)
            lowerCAmelCase__        =  jax.random.split(_UpperCamelCase       ,  jax.device_count()				)

            lowerCAmelCase__        =  replicate(_UpperCamelCase				)
            lowerCAmelCase__        =  shard(_UpperCamelCase				)
            lowerCAmelCase__        =  shard(_UpperCamelCase				)

            lowerCAmelCase__        =  pipe(
                prompt_ids=_UpperCamelCase       ,  image=_UpperCamelCase       ,  params=_UpperCamelCase       ,  prng_seed=_UpperCamelCase       ,  num_inference_steps=50       ,  jit=_UpperCamelCase       ,  ).images
            assert images.shape == (jax.device_count(), 1, 7_68, 5_12, 3)

            lowerCAmelCase__        =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]				)
            lowerCAmelCase__        =  images[0, 2_53:2_56, 2_53:2_56, -1]

            lowerCAmelCase__        =  jnp.asarray(jax.device_get(image_slice.flatten()				)				)
            lowerCAmelCase__        =  jnp.array(
                [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]				)
            print(F"output_slice: {output_slice}"				)
            assert jnp.abs(output_slice - expected_slice				).max() < 1E-2













from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

__snake_case      :      Any             =       """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""







def 		_UpperCamelCase				(      UpperCamelCase_    :   str = "mumbai"						)					->				Generator[tuple[str, str], None, None]:

						"""simple docstring"""



						lowerCAmelCase__        =  BeautifulSoup(requests.get(url + location						).content      ,    'html.parser'						)
						# This attribute finds out all the specifics listed in a job
						for job in soup.find_all('div'      ,    attrs={'data-tn-component': 'organicJob'}						):
												lowerCAmelCase__        =  job.find('a'      ,    attrs={'data-tn-element': 'jobTitle'}						).text.strip()
												lowerCAmelCase__        =  job.find('span'      ,    {'class': 'company'}						).text.strip()
												yield job_title, company_name


if __name__ == "__main__":
					for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
										print(f'Job {i:>2} is {job[0]} at {job[1]}')





