



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

a_						:     Union[str, Any]	     =		logging.get_logger(__name__)

a_						:     Union[str, Any]	     =		{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			'imagegpt'
     _A       =			['past_key_values']
     _A       =			{
         'hidden_size': 'n_embd',
         'max_position_embeddings': 'n_positions',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }







     def __init__(self				,			__a=5_12 + 1				,			__a=32 * 32				,			__a=5_12				,			__a=24				,			__a=8				,			__a=None				,			__a="quick_gelu"				,			__a=0.1				,			__a=0.1				,			__a=0.1				,			__a=1E-5				,			__a=0.02				,			__a=True				,			__a=True				,			__a=False				,			__a=False				,			__a=False				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		vocab_size
           lowerCamelCase								=		n_positions
           lowerCamelCase								=		n_embd
           lowerCamelCase								=		n_layer
           lowerCamelCase								=		n_head
           lowerCamelCase								=		n_inner
           lowerCamelCase								=		activation_function
           lowerCamelCase								=		resid_pdrop
           lowerCamelCase								=		embd_pdrop
           lowerCamelCase								=		attn_pdrop
           lowerCamelCase								=		layer_norm_epsilon
           lowerCamelCase								=		initializer_range
           lowerCamelCase								=		scale_attn_weights
           lowerCamelCase								=		use_cache
           lowerCamelCase								=		scale_attn_by_inverse_layer_idx
           lowerCamelCase								=		reorder_and_upcast_attn
           lowerCamelCase								=		tie_word_embeddings

           super().__init__(tie_word_embeddings=__a				,			**__a    )





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "sequence"}),
               ]    )







     def 						_a      (self				,			__a				,			__a = 1				,			__a = -1				,			__a = False				,			__a = None				,			__a = 3				,			__a = 32				,			__a = 32				,			):






           '''simple docstring'''



           lowerCamelCase								=		self._generate_dummy_images(__a				,			__a				,			__a				,			__a    )
           lowerCamelCase								=		dict(preprocessor(images=__a				,			return_tensors=__a    )    )

           return inputs




from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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

def     __lowercase(	UpperCAmelCase__ = "mumbai" ):


      """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]}""")