






'''simple docstring'''



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCAmelCase_					(    __A				,       __A				):




      """simple docstring"""




      @register_to_config
      def __init__( self			:		Dict     ,      *,
          UpperCAmelCase			:		int = 4     ,      UpperCAmelCase			:		int = 768     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[Any]     ,      )  ->  int:


       '''simple docstring'''

       super().__init__()

       lowercase					:   Optional[Any]				  =nn.Parameter(torch.zeros(UpperCAmelCase		)		)

       # parameters for additional clip time embeddings
       lowercase					:   int				  =nn.Linear(UpperCAmelCase     ,      UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =nn.Linear(UpperCAmelCase     ,      UpperCAmelCase		)

       # parameters for encoder hidden states
       lowercase					:   List[Any]				  =clip_extra_context_tokens
       lowercase					:   int				  =nn.Linear(
           UpperCAmelCase     ,      self.clip_extra_context_tokens * cross_attention_dim		)
       lowercase					:   Optional[int]				  =nn.Linear(UpperCAmelCase     ,      UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  =nn.LayerNorm(UpperCAmelCase		)






      def 				A__      ( self			:		Any     ,      *, UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[Any]		)  ->  List[str]:


       '''simple docstring'''

       if do_classifier_free_guidance:
        # Add the classifier free guidance embeddings to the image embeddings
        lowercase					:   Any				  =image_embeddings.shape[0]
        lowercase					:   int				  =self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
        lowercase					:   List[str]				  =classifier_free_guidance_embeddings.expand(
            UpperCAmelCase     ,      -1		)
        lowercase					:   Union[str, Any]				  =torch.cat([classifier_free_guidance_embeddings, image_embeddings]     ,      dim=0		)

       # The image embeddings batch size and the text embeddings batch size are equal
       assert image_embeddings.shape[0] == prompt_embeds.shape[0]

       lowercase					:   str				  =prompt_embeds.shape[0]

       # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
       # adding CLIP embeddings to the existing timestep embedding, ...
       lowercase					:   str				  =self.embedding_proj(UpperCAmelCase		)
       lowercase					:   str				  =self.clip_image_embeddings_project_to_time_embeddings(UpperCAmelCase		)
       lowercase					:   List[str]				  =time_projected_image_embeddings + time_projected_prompt_embeds

       # ... and by projecting CLIP embeddings into four
       # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
       lowercase					:   List[str]				  =self.clip_extra_context_tokens_proj(UpperCAmelCase		)
       lowercase					:   List[Any]				  =clip_extra_context_tokens.reshape(UpperCAmelCase     ,      -1     ,      self.clip_extra_context_tokens		)
       lowercase					:   str				  =clip_extra_context_tokens.permute(0     ,      2     ,      1		)

       lowercase					:   Any				  =self.encoder_hidden_states_proj(UpperCAmelCase		)
       lowercase					:   Optional[Any]				  =self.text_encoder_hidden_states_norm(UpperCAmelCase		)
       lowercase					:   str				  =torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]     ,      dim=1		)

       return text_encoder_hidden_states, additive_clip_time_embeddings











'''simple docstring'''



from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

SCREAMING_SNAKE_CASE						=	'https://www.indeed.co.in/jobs?q=mobile+app+development&l='


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



 """simple docstring"""




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



