


import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_SCREAMING_SNAKE_CASE      :			Dict      				=   False

class   A__ (   unittest.TestCase     ):







    """simple docstring"""

    def 		a_      (	self   ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    @property
    def 		a_      (	self   ):
         return 1_2
    @property
    def 		a_      (	self   ):
         return 1_2
    @property
    def 		a_      (	self   ):
         return 3_2
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    VQModel(
             block_out_channels=[3_2, 6_4]      , in_channels=3      , out_channels=3      , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      , latent_channels=3      , num_vq_embeddings=self.num_embed      , vq_embed_dim=3      , )
         return model
    @property
    def 		a_      (	self   ):
         snake_case					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )
         return tokenizer
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )
         snake_case					=    CLIPTextConfig(
             bos_token_id=0      , eos_token_id=2      , hidden_size=self.text_embedder_hidden_size      , intermediate_size=3_7      , layer_norm_eps=1E-05      , num_attention_heads=4      , num_hidden_layers=5      , pad_token_id=1      , vocab_size=1_0_0_0      , )
         return CLIPTextModel(__snake_case   )
    @property
    def 		a_      (	self   ):
         torch.manual_seed(0   )

         snake_case					=    1_2
         snake_case					=    1_2

         snake_case					=    {
             '''attention_bias''': True,
             '''cross_attention_dim''': 3_2,
             '''attention_head_dim''': height * width,
             '''num_attention_heads''': 1,
             '''num_vector_embeds''': self.num_embed,
             '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
             '''norm_num_groups''': 3_2,
             '''sample_size''': width,
             '''activation_fn''': '''geglu-approximate''',
         }

         snake_case					=    TransformeraDModel(**__snake_case   )
         return model
    def 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.dummy_vqvae
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_transformer
         snake_case					=    VQDiffusionScheduler(self.num_embed   )
         snake_case					=    LearnedClassifierFreeSamplingEmbeddings(learnable=__snake_case   )

         snake_case					=    VQDiffusionPipeline(
             vqvae=__snake_case      , text_encoder=__snake_case      , tokenizer=__snake_case      , transformer=__snake_case      , scheduler=__snake_case      , learned_classifier_free_sampling_embeddings=__snake_case      , )
         snake_case					=    pipe.to(__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''teddy bear playing in the pool'''

         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipe([prompt]      , generator=__snake_case      , num_inference_steps=2      , output_type='''np'''   )
         snake_case					=    output.images

         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipe(
             [prompt]      , generator=__snake_case      , output_type='''np'''      , return_dict=__snake_case      , num_inference_steps=2   )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 2_4, 2_4, 3)

         snake_case					=    np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]   )

         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 		a_      (	self   ):
         snake_case					=    '''cpu'''

         snake_case					=    self.dummy_vqvae
         snake_case					=    self.dummy_text_encoder
         snake_case					=    self.dummy_tokenizer
         snake_case					=    self.dummy_transformer
         snake_case					=    VQDiffusionScheduler(self.num_embed   )
         snake_case					=    LearnedClassifierFreeSamplingEmbeddings(
             learnable=__snake_case      , hidden_size=self.text_embedder_hidden_size      , length=tokenizer.model_max_length   )

         snake_case					=    VQDiffusionPipeline(
             vqvae=__snake_case      , text_encoder=__snake_case      , tokenizer=__snake_case      , transformer=__snake_case      , scheduler=__snake_case      , learned_classifier_free_sampling_embeddings=__snake_case      , )
         snake_case					=    pipe.to(__snake_case   )
         pipe.set_progress_bar_config(disable=__snake_case   )

         snake_case					=    '''teddy bear playing in the pool'''

         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipe([prompt]      , generator=__snake_case      , num_inference_steps=2      , output_type='''np'''   )
         snake_case					=    output.images

         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipe(
             [prompt]      , generator=__snake_case      , output_type='''np'''      , return_dict=__snake_case      , num_inference_steps=2   )[0]

         snake_case					=    image[0, -3:, -3:, -1]
         snake_case					=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 2_4, 2_4, 3)

         snake_case					=    np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]   )

         assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2.0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






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







    """simple docstring"""

    def 		a_      (	self   ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




    def 		a_      (	self   ):
         snake_case					=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''   )

         snake_case					=    VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''   )
         snake_case					=    pipeline.to(__snake_case   )
         pipeline.set_progress_bar_config(disable=__snake_case   )

         # requires GPU generator for gumbel softmax
         # don't use GPU generator in tests though
         snake_case					=    torch.Generator(device=__snake_case   ).manual_seed(0   )
         snake_case					=    pipeline(
             '''teddy bear playing in the pool'''      , num_images_per_prompt=1      , generator=__snake_case      , output_type='''np'''      , )

         snake_case					=    output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)
         assert np.abs(expected_image - image   ).max() < 2.0








from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_SCREAMING_SNAKE_CASE      :			str      				=   "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def       UpperCAmelCase__						(UpperCamelCase_ = "mumbai"    ):



     """simple docstring"""






     snake_case					=    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'''}    ):
          snake_case					=    job.find('''a'''							,attrs={'''data-tn-element''': '''jobTitle'''}    ).text.strip()
          snake_case					=    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]}''')




