
from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

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






def      lowerCamelCase__       (       __lowerCAmelCase						:						str = "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]}""")





import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin





class 			_lowerCAmelCase      (			__a      ,   __a ):




				@register_to_config
				def __init__(      self       ,       _UpperCamelCase = 128       ,       _UpperCamelCase = 256       ,       _UpperCamelCase = 2000.0       ,       _UpperCamelCase = 768       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 12       ,       _UpperCamelCase = 64       ,       _UpperCamelCase = 2_048       ,       _UpperCamelCase = 0.1       ,       )       ->   str:
							super().__init__()

							lowerCAmelCase_      			=			nn.Sequential(
							    nn.Linear(_UpperCamelCase       ,       d_model * 4       ,       bias=_UpperCamelCase  )       ,       nn.SiLU()       ,       nn.Linear(d_model * 4       ,       d_model * 4       ,       bias=_UpperCamelCase  )       ,       nn.SiLU()       ,       )

							lowerCAmelCase_      			=			nn.Embedding(_UpperCamelCase       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			False

							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.Dropout(p=_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.ModuleList()
							for lyr_num in range(_UpperCamelCase  ):
										# FiLM conditional T5 decoder
										lowerCAmelCase_      			=			DecoderLayer(d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )
										self.decoders.append(_UpperCamelCase  )

							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase  )

							lowerCAmelCase_      			=			nn.Dropout(p=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			torch.mul(query_input.unsqueeze(-1  )       ,       key_input.unsqueeze(-2  )  )
							return mask.unsqueeze(-3  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Any:
							lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_      			=			decoder_input_tokens.shape
							assert decoder_noise_time.shape == (batch,)

							# decoder_noise_time is in [0, 1), so rescale to expected timing range.
							lowerCAmelCase_      			=			get_timestep_embedding(
							    decoder_noise_time * self.config.max_decoder_noise_time       ,       embedding_dim=self.config.d_model       ,       max_period=self.config.max_decoder_noise_time       ,       ).to(dtype=self.dtype  )

							lowerCAmelCase_      			=			self.conditioning_emb(_UpperCamelCase  ).unsqueeze(1  )

							assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

							lowerCAmelCase_      			=			decoder_input_tokens.shape[1]

							# If we want to use relative positions for audio context, we can just offset
							# this sequence by the length of encodings_and_masks.
							lowerCAmelCase_      			=			torch.broadcast_to(
							    torch.arange(_UpperCamelCase       ,       device=decoder_input_tokens.device  )       ,       (batch, seq_length)       ,       )

							lowerCAmelCase_      			=			self.position_encoding(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.continuous_inputs_projection(_UpperCamelCase  )
							inputs += position_encodings
							lowerCAmelCase_      			=			self.dropout(_UpperCamelCase  )

							# decoder: No padding present.
							lowerCAmelCase_      			=			torch.ones(
							    decoder_input_tokens.shape[:2]       ,       device=decoder_input_tokens.device       ,       dtype=inputs.dtype  )

							# Translate encoding masks to encoder-decoder masks.
							lowerCAmelCase_      			=			[(x, self.encoder_decoder_mask(_UpperCamelCase       ,       _UpperCamelCase  )) for x, y in encodings_and_masks]

							# cross attend style: concat encodings
							lowerCAmelCase_      			=			torch.cat([x[0] for x in encodings_and_encdec_masks]       ,       dim=1  )
							lowerCAmelCase_      			=			torch.cat([x[1] for x in encodings_and_encdec_masks]       ,       dim=-1  )

							for lyr in self.decoders:
										lowerCAmelCase_      			=			lyr(
										    _UpperCamelCase       ,       conditioning_emb=_UpperCamelCase       ,       encoder_hidden_states=_UpperCamelCase       ,       encoder_attention_mask=_UpperCamelCase       ,       )[0]

							lowerCAmelCase_      			=			self.decoder_norm(_UpperCamelCase  )
							lowerCAmelCase_      			=			self.post_dropout(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.spec_out(_UpperCamelCase  )
							return spec_out





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   Dict:
							super().__init__()
							lowerCAmelCase_      			=			nn.ModuleList()

							# cond self attention: layer 0
							self.layer.append(
							    TaLayerSelfAttentionCond(d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )  )

							# cross attention: layer 1
							self.layer.append(
							    TaLayerCrossAttention(
							        d_model=_UpperCamelCase       ,       d_kv=_UpperCamelCase       ,       num_heads=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase       ,       layer_norm_epsilon=_UpperCamelCase       ,       )  )

							# Film Cond MLP + dropout: last layer
							self.layer.append(
							    TaLayerFFCond(d_model=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase       ,       layer_norm_epsilon=_UpperCamelCase  )  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Any:
							lowerCAmelCase_      			=			self.layer[0](
							    _UpperCamelCase       ,       conditioning_emb=_UpperCamelCase       ,       attention_mask=_UpperCamelCase       ,       )

							if encoder_hidden_states is not None:
										lowerCAmelCase_      			=			torch.where(encoder_attention_mask > 0       ,       0       ,       -1e1_0  ).to(
										    encoder_hidden_states.dtype  )

										lowerCAmelCase_      			=			self.layer[1](
										    _UpperCamelCase       ,       key_value_states=_UpperCamelCase       ,       attention_mask=_UpperCamelCase       ,       )

							# Apply Film Conditional Feed Forward layer
							lowerCAmelCase_      			=			self.layer[-1](_UpperCamelCase       ,       _UpperCamelCase  )

							return (hidden_states,)





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Optional[int]:
							super().__init__()
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase  )
							lowerCAmelCase_      			=			TaFiLMLayer(in_features=d_model * 4       ,       out_features=_UpperCamelCase  )
							lowerCAmelCase_      			=			Attention(query_dim=_UpperCamelCase       ,       heads=_UpperCamelCase       ,       dim_head=_UpperCamelCase       ,       out_bias=_UpperCamelCase       ,       scale_qk=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   Dict:
							# pre_self_attention_layer_norm
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )

							if conditioning_emb is not None:
										lowerCAmelCase_      			=			self.FiLMLayer(_UpperCamelCase       ,       _UpperCamelCase  )

							# Self-attention block
							lowerCAmelCase_      			=			self.attention(_UpperCamelCase  )

							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )

							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Dict:
							super().__init__()
							lowerCAmelCase_      			=			Attention(query_dim=_UpperCamelCase       ,       heads=_UpperCamelCase       ,       dim_head=_UpperCamelCase       ,       out_bias=_UpperCamelCase       ,       scale_qk=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase       ,       eps=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       )       ->   int:
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )
							lowerCAmelCase_      			=			self.attention(
							    _UpperCamelCase       ,       encoder_hidden_states=_UpperCamelCase       ,       attention_mask=attention_mask.squeeze(1  )       ,       )
							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )
							return layer_output





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							super().__init__()
							lowerCAmelCase_      			=			TaDenseGatedActDense(d_model=_UpperCamelCase       ,       d_ff=_UpperCamelCase       ,       dropout_rate=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaFiLMLayer(in_features=d_model * 4       ,       out_features=_UpperCamelCase  )
							lowerCAmelCase_      			=			TaLayerNorm(_UpperCamelCase       ,       eps=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   Tuple:
							lowerCAmelCase_      			=			self.layer_norm(_UpperCamelCase  )
							if conditioning_emb is not None:
										lowerCAmelCase_      			=			self.film(_UpperCamelCase       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			self.DenseReluDense(_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_states + self.dropout(_UpperCamelCase  )
							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							super().__init__()
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       _UpperCamelCase       ,       bias=_UpperCamelCase  )
							lowerCAmelCase_      			=			nn.Dropout(_UpperCamelCase  )
							lowerCAmelCase_      			=			NewGELUActivation()




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			self.act(self.wi_a(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			self.wi_a(_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_gelu * hidden_linear
							lowerCAmelCase_      			=			self.dropout(_UpperCamelCase  )

							lowerCAmelCase_      			=			self.wo(_UpperCamelCase  )
							return hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=1e-6  )       ->   int:
							super().__init__()
							lowerCAmelCase_      			=			nn.Parameter(torch.ones(_UpperCamelCase  )  )
							lowerCAmelCase_      			=			eps




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Union[str, Any]:
							# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
							# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
							# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
							# half-precision inputs is done in fp32

							lowerCAmelCase_      			=			hidden_states.to(torch.floataa  ).pow(2  ).mean(-1       ,       keepdim=_UpperCamelCase  )
							lowerCAmelCase_      			=			hidden_states * torch.rsqrt(variance + self.variance_epsilon  )

							# convert into half-precision if necessary
							if self.weight.dtype in [torch.floataa, torch.bfloataa]:
										lowerCAmelCase_      			=			hidden_states.to(self.weight.dtype  )

							return self.weight * hidden_states





class 			_lowerCAmelCase      (			nn.Module ):




				def   __a		(      self       ,       _UpperCamelCase  )       ->   torch.Tensor:
							return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi  ) * (input + 0.044715 * torch.pow(_UpperCamelCase       ,       3.0  ))  ))





class 			_lowerCAmelCase      (			nn.Module ):




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   str:
							super().__init__()
							lowerCAmelCase_      			=			nn.Linear(_UpperCamelCase       ,       out_features * 2       ,       bias=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			self.scale_bias(_UpperCamelCase  )
							lowerCAmelCase_			,						lowerCAmelCase_      			=			torch.chunk(_UpperCamelCase       ,       2       ,       -1  )
							lowerCAmelCase_      			=			x * (1 + scale) + shift
							return x



