




'''simple docstring'''






import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	@property
	def   A			(		self    :  int	)    ->	Dict:
								return self.get_dummy_input()







	@property
	def   A			(		self    :  Dict	)    ->	List[str]:
								if self.block_type == "down":
															return (4, 32, 16, 16)
								elif self.block_type == "mid":
															return (4, 32, 32, 32)
								elif self.block_type == "up":
															return (4, 32, 64, 64)

								raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""	)







	def   A			(		self    :  Optional[int]	,    __snake_case    :  str=True	,    __snake_case    :  Any=False	,    __snake_case    :  Any=False	,    __snake_case    :  Union[str, Any]=False	,    )    ->	Dict:
								UpperCAmelCase				: str											=	4
								UpperCAmelCase				: List[str]											=	32
								UpperCAmelCase				: str											=	(32, 32)

								UpperCAmelCase				: List[str]											=	torch.manual_seed(0	)
								UpperCAmelCase				: Tuple											=	torch.device(A__	)
								UpperCAmelCase				: str											=	(batch_size, num_channels) + sizes
								UpperCAmelCase				: int											=	randn_tensor(A__	,    generator=A__	,    device=A__	)
								UpperCAmelCase				: Union[str, Any]											=	{"""hidden_states""": hidden_states}

								if include_temb:
															UpperCAmelCase				: List[Any]											=	128
															UpperCAmelCase				: Dict											=	randn_tensor((batch_size, temb_channels)	,    generator=A__	,    device=A__	)

								if include_res_hidden_states_tuple:
															UpperCAmelCase				: Optional[int]											=	torch.manual_seed(1	)
															UpperCAmelCase				: List[Any]											=	(randn_tensor(A__	,    generator=A__	,    device=A__	),)

								if include_encoder_hidden_states:
															UpperCAmelCase				: List[Any]											=	floats_tensor((batch_size, 32, 32)	).to(A__	)

								if include_skip_sample:
															UpperCAmelCase				: Dict											=	randn_tensor(((batch_size, 3) + sizes)	,    generator=A__	,    device=A__	)

								return dummy_input







	def   A			(		self    :  int	)    ->	Optional[int]:
								UpperCAmelCase				: Dict											=	{
								    """in_channels""": 32,
								    """out_channels""": 32,
								    """temb_channels""": 128,
								}
								if self.block_type == "up":
															UpperCAmelCase				: Union[str, Any]											=	32

								if self.block_type == "mid":
															init_dict.pop('''out_channels'''	)

								UpperCAmelCase				: Any											=	self.dummy_input
								return init_dict, inputs_dict







	def   A			(		self    :  Dict	,    __snake_case    :  int	)    ->	List[Any]:
								UpperCAmelCase				: Optional[Any]											=	self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase				: Optional[Any]											=	self.block_class(**A__	)
								unet_block.to(A__	)
								unet_block.eval()

								with torch.no_grad():
															UpperCAmelCase				: Union[str, Any]											=	unet_block(**A__	)

								if isinstance(A__	,    A__	):
															UpperCAmelCase				: Any											=	output[0]

								self.assertEqual(output.shape	,    self.output_shape	)

								UpperCAmelCase				: str											=	output[0, -1, -3:, -3:]
								UpperCAmelCase				: List[Any]											=	torch.tensor(A__	).to(A__	)
								assert torch_all_close(output_slice.flatten()	,    A__	,    atol=5E-3	)







	@unittest.skipIf(torch_device == '''mps'''	,    '''Training is not supported in mps'''	)
	def   A			(		self    :  Any	)    ->	List[str]:
								UpperCAmelCase				: Optional[int]											=	self.prepare_init_args_and_inputs_for_common()
								UpperCAmelCase				: str											=	self.block_class(**A__	)
								model.to(A__	)
								model.train()
								UpperCAmelCase				: Optional[int]											=	model(**A__	)

								if isinstance(A__	,    A__	):
															UpperCAmelCase				: Tuple											=	output[0]

								UpperCAmelCase				: Tuple											=	torch.device(A__	)
								UpperCAmelCase				: str											=	randn_tensor(output.shape	,    device=A__	)
								UpperCAmelCase				: Union[str, Any]											=	torch.nn.functional.mse_loss(A__	,    A__	)
								loss.backward()










'''simple docstring'''






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

UpperCamelCase__:     Union[str, Any]										=    "https://www.indeed.co.in/jobs?q=mobile+app+development&l="


def 				snake_case_  (      _lowerCAmelCase						:		str = "mumbai"						)   ->     Generator[tuple[str, str], None, None]:
							UpperCAmelCase				: Optional[int]											=	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'''}						):
														UpperCAmelCase				: Optional[Any]											=	job.find('''a'''     ,   attrs={'''data-tn-element''': '''jobTitle'''}						).text.strip()
														UpperCAmelCase				: int											=	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]}")





