



'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase__:     Any										=    logging.get_logger(__name__)


if is_vision_available():
						import PIL

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= ["""pixel_values"""]







	def __init__(		self    :  Tuple	,    __snake_case    :  bool = True	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  PILImageResampling = PILImageResampling.BICUBIC	,    __snake_case    :  bool = True	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  bool = True	,    __snake_case    :  Union[int, float] = 1 / 255	,    __snake_case    :  bool = True	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  bool = True	,    **__snake_case    :  int	,    )    ->	None:
								super().__init__(**__snake_case	)
								UpperCAmelCase				: int											=	size if size is not None else {'''shortest_edge''': 224}
								UpperCAmelCase				: str											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	)
								UpperCAmelCase				: Any											=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase				: Any											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	,    param_name='''crop_size'''	)

								UpperCAmelCase				: Tuple											=	do_resize
								UpperCAmelCase				: str											=	size
								UpperCAmelCase				: Union[str, Any]											=	resample
								UpperCAmelCase				: str											=	do_center_crop
								UpperCAmelCase				: Dict											=	crop_size
								UpperCAmelCase				: str											=	do_rescale
								UpperCAmelCase				: Union[str, Any]											=	rescale_factor
								UpperCAmelCase				: Optional[Any]											=	do_normalize
								UpperCAmelCase				: Optional[int]											=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								UpperCAmelCase				: int											=	image_std if image_std is not None else OPENAI_CLIP_STD
								UpperCAmelCase				: str											=	do_convert_rgb







	def   A			(		self    :  Dict	,    __snake_case    :  np.ndarray	,    __snake_case    :  Dict[str, int]	,    __snake_case    :  PILImageResampling = PILImageResampling.BICUBIC	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Optional[int]	,    )    ->	np.ndarray:
								UpperCAmelCase				: Any											=	get_size_dict(__snake_case	,    default_to_square=__snake_case	)
								if "shortest_edge" not in size:
															raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
								UpperCAmelCase				: str											=	get_resize_output_image_size(__snake_case	,    size=size['''shortest_edge''']	,    default_to_square=__snake_case	)
								return resize(__snake_case	,    size=__snake_case	,    resample=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Dict	,    __snake_case    :  np.ndarray	,    __snake_case    :  Dict[str, int]	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Any	,    )    ->	np.ndarray:
								UpperCAmelCase				: Union[str, Any]											=	get_size_dict(__snake_case	)
								if "height" not in size or "width" not in size:
															raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""	)
								return center_crop(__snake_case	,    size=(size['''height'''], size['''width'''])	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  np.ndarray	,    __snake_case    :  Union[int, float]	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Optional[Any]	,    )    ->	Dict:
								return rescale(__snake_case	,    scale=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Optional[int]	,    __snake_case    :  np.ndarray	,    __snake_case    :  Union[float, List[float]]	,    __snake_case    :  Union[float, List[float]]	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Tuple	,    )    ->	np.ndarray:
								return normalize(__snake_case	,    mean=__snake_case	,    std=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  Optional[int]	,    __snake_case    :  ImageInput	,    __snake_case    :  bool = None	,    __snake_case    :  Dict[str, int] = None	,    __snake_case    :  PILImageResampling = None	,    __snake_case    :  bool = None	,    __snake_case    :  int = None	,    __snake_case    :  bool = None	,    __snake_case    :  float = None	,    __snake_case    :  bool = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  Optional[Union[float, List[float]]] = None	,    __snake_case    :  bool = None	,    __snake_case    :  Optional[Union[str, TensorType]] = None	,    __snake_case    :  Optional[ChannelDimension] = ChannelDimension.FIRST	,    **__snake_case    :  Tuple	,    )    ->	PIL.Image.Image:
								UpperCAmelCase				: List[str]											=	do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase				: str											=	size if size is not None else self.size
								UpperCAmelCase				: List[str]											=	get_size_dict(__snake_case	,    param_name='''size'''	,    default_to_square=__snake_case	)
								UpperCAmelCase				: List[Any]											=	resample if resample is not None else self.resample
								UpperCAmelCase				: Any											=	do_center_crop if do_center_crop is not None else self.do_center_crop
								UpperCAmelCase				: int											=	crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase				: Union[str, Any]											=	get_size_dict(__snake_case	,    param_name='''crop_size'''	,    default_to_square=__snake_case	)
								UpperCAmelCase				: Tuple											=	do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase				: Dict											=	rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase				: List[str]											=	do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase				: str											=	image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase				: Optional[Any]											=	image_std if image_std is not None else self.image_std
								UpperCAmelCase				: int											=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase				: Dict											=	make_list_of_images(__snake_case	)

								if not valid_images(__snake_case	):
															raise ValueError(
															    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
															    '''torch.Tensor, tf.Tensor or jax.ndarray.'''	)

								if do_resize and size is None:
															raise ValueError('''Size must be specified if do_resize is True.'''	)

								if do_center_crop and crop_size is None:
															raise ValueError('''Crop size must be specified if do_center_crop is True.'''	)

								if do_rescale and rescale_factor is None:
															raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

								if do_normalize and (image_mean is None or image_std is None):
															raise ValueError('''Image mean and std must be specified if do_normalize is True.'''	)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
															UpperCAmelCase				: Optional[Any]											=	[convert_to_rgb(__snake_case	) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase				: Optional[int]											=	[to_numpy_array(__snake_case	) for image in images]

								if do_resize:
															UpperCAmelCase				: Union[str, Any]											=	[self.resize(image=__snake_case	,    size=__snake_case	,    resample=__snake_case	) for image in images]

								if do_center_crop:
															UpperCAmelCase				: Optional[Any]											=	[self.center_crop(image=__snake_case	,    size=__snake_case	) for image in images]

								if do_rescale:
															UpperCAmelCase				: Dict											=	[self.rescale(image=__snake_case	,    scale=__snake_case	) for image in images]

								if do_normalize:
															UpperCAmelCase				: Dict											=	[self.normalize(image=__snake_case	,    mean=__snake_case	,    std=__snake_case	) for image in images]

								UpperCAmelCase				: List[str]											=	[to_channel_dimension_format(__snake_case	,    __snake_case	) for image in images]

								UpperCAmelCase				: str											=	{'''pixel_values''': images}
								return BatchFeature(data=__snake_case	,    tensor_type=__snake_case	)










'''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]}")





