


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		int			=      StableUnCLIPPipeline
     A:		List[str]			=      TEXT_TO_IMAGE_PARAMS
     A:		Union[str, Any]			=      TEXT_TO_IMAGE_BATCH_PARAMS
     A:		Optional[int]			=      TEXT_TO_IMAGE_IMAGE_PARAMS
     A:		str			=      TEXT_TO_IMAGE_IMAGE_PARAMS

     # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
     A:		str			=      False


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   32
      UpperCamelCase__					:    Any          =   embedder_hidden_size

      # prior components

      torch.manual_seed(0			)
      UpperCamelCase__					:    Optional[int]          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    int          =   CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0							,	eos_token_id=2							,	hidden_size=lowerCamelCase__							,	projection_dim=lowerCamelCase__							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    List[str]          =   PriorTransformer(
          num_attention_heads=2							,	attention_head_dim=12							,	embedding_dim=lowerCamelCase__							,	num_layers=1							,	)

      torch.manual_seed(0			)
      UpperCamelCase__					:    Any          =   DDPMScheduler(
          variance_type='''fixed_small_log'''							,	prediction_type='''sample'''							,	num_train_timesteps=1000							,	clip_sample=lowerCamelCase__							,	clip_sample_range=5.0							,	beta_schedule='''squaredcos_cap_v2'''							,	)

      # regular denoising components

      torch.manual_seed(0			)
      UpperCamelCase__					:    List[Any]          =   StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    Any          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    Dict          =   CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0							,	eos_token_id=2							,	hidden_size=lowerCamelCase__							,	projection_dim=32							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    List[Any]          =   UNetaDConditionModel(
          sample_size=32							,	in_channels=4							,	out_channels=4							,	down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')							,	up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')							,	block_out_channels=(32, 64)							,	attention_head_dim=(2, 4)							,	class_embed_type='''projection'''							,	projection_class_embeddings_input_dim=embedder_projection_dim * 2							,	cross_attention_dim=lowerCamelCase__							,	layers_per_block=1							,	upcast_attention=lowerCamelCase__							,	use_linear_projection=lowerCamelCase__							,	)

      torch.manual_seed(0			)
      UpperCamelCase__					:    str          =   DDIMScheduler(
          beta_schedule='''scaled_linear'''							,	beta_start=0.0_0085							,	beta_end=0.012							,	prediction_type='''v_prediction'''							,	set_alpha_to_one=lowerCamelCase__							,	steps_offset=1							,	)

      torch.manual_seed(0			)
      UpperCamelCase__					:    List[str]          =   AutoencoderKL()

      UpperCamelCase__					:    List[str]          =   {
          # prior components
          '''prior_tokenizer''': prior_tokenizer,
          '''prior_text_encoder''': prior_text_encoder,
          '''prior''': prior,
          '''prior_scheduler''': prior_scheduler,
          # image noising components
          '''image_normalizer''': image_normalizer,
          '''image_noising_scheduler''': image_noising_scheduler,
          # regular denoising components
          '''tokenizer''': tokenizer,
          '''text_encoder''': text_encoder,
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
      }

      return components


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





      '''simple docstring'''






      if str(lowerCamelCase__			).startswith('''mps'''			):
       UpperCamelCase__					:    Union[str, Any]          =   torch.manual_seed(lowerCamelCase__			)
      else:
       UpperCamelCase__					:    Tuple          =   torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''prior_num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }
      return inputs


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   torch_device == '''cpu'''

      self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   torch_device in ['''cpu''', '''mps''']

      self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase__			)



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


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Dict:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Any			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''			)

      UpperCamelCase__					:    Any          =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''							,	torch_dtype=torch.floataa			)
      pipe.to(lowerCamelCase__			)
      pipe.set_progress_bar_config(disable=lowerCamelCase__			)
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      UpperCamelCase__					:    Dict          =   torch.Generator(device='''cpu'''			).manual_seed(0			)
      UpperCamelCase__					:    Tuple          =   pipe('''anime turle'''							,	generator=lowerCamelCase__							,	output_type='''np'''			)

      UpperCamelCase__					:    Union[str, Any]          =   output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      Tuple:





      '''simple docstring'''






      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      UpperCamelCase__					:    Dict          =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''							,	torch_dtype=torch.floataa			)
      UpperCamelCase__					:    List[str]          =   pipe.to(lowerCamelCase__			)
      pipe.set_progress_bar_config(disable=lowerCamelCase__			)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      UpperCamelCase__					:    Optional[Any]          =   pipe(
          '''anime turtle'''							,	prior_num_inference_steps=2							,	num_inference_steps=2							,	output_type='''np'''							,	)

      UpperCamelCase__					:    int          =   torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 10**9






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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

def 			_a  (				SCREAMING_SNAKE_CASE	:				str = "mumbai"     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   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'''}     ):
  UpperCamelCase__					:    Optional[Any]          =   job.find('''a'''    , attrs={'''data-tn-element''': '''jobTitle'''}     ).text.strip()
  UpperCamelCase__					:    Union[str, Any]          =   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]}")


