



from typing import Mapping

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


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

a_						:     Optional[int]	     =		{
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}





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



					_A       =			't5'
					_A       =			['past_key_values']
					_A       =			{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







					def __init__(self				,			__a=3_21_28				,			__a=5_12				,			__a=64				,			__a=20_48				,			__a=6				,			__a=None				,			__a=8				,			__a=32				,			__a=1_28				,			__a=0.1				,			__a=1E-6				,			__a=1.0				,			__a="relu"				,			__a=True				,			__a=True				,			__a=0				,			__a=1				,			**__a				,			):






											'''simple docstring'''



											lowerCamelCase								=		vocab_size
											lowerCamelCase								=		d_model
											lowerCamelCase								=		d_kv
											lowerCamelCase								=		d_ff
											lowerCamelCase								=		num_layers
											lowerCamelCase								=		(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											lowerCamelCase								=		num_heads
											lowerCamelCase								=		relative_attention_num_buckets
											lowerCamelCase								=		relative_attention_max_distance
											lowerCamelCase								=		dropout_rate
											lowerCamelCase								=		layer_norm_epsilon
											lowerCamelCase								=		initializer_factor
											lowerCamelCase								=		feed_forward_proj
											lowerCamelCase								=		use_cache

											lowerCamelCase								=		self.feed_forward_proj.split("-"    )
											lowerCamelCase								=		act_info[-1]
											lowerCamelCase								=		act_info[0] == "gated"

											if len(__a    ) > 1 and act_info[0] != "gated" or len(__a    ) > 2:
																	raise ValueError(
																	    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																	    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
																	    "'gated-gelu' or 'relu'"    )

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																	lowerCamelCase								=		"gelu_new"

											super().__init__(
											    pad_token_id=__a				,			eos_token_id=__a				,			is_encoder_decoder=__a				,			**__a				,			)





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



					@property
					def 						_a      (self    ):






											'''simple docstring'''



											lowerCamelCase								=		{
											    "input_ids": {0: "batch", 1: "encoder_sequence"},
											    "attention_mask": {0: "batch", 1: "encoder_sequence"},
											}
											if self.use_past:
																	lowerCamelCase								=		"past_encoder_sequence + sequence"
																	lowerCamelCase								=		{0: "batch"}
																	lowerCamelCase								=		{0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
																	lowerCamelCase								=		{0: "batch", 1: "decoder_sequence"}
																	lowerCamelCase								=		{0: "batch", 1: "decoder_sequence"}

											if self.use_past:
																	self.fill_with_past_key_values_(__a				,			direction="inputs"    )

											return common_inputs







					@property
					def 						_a      (self    ):






											'''simple docstring'''



											return 13




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]}""")