



import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

     if is_vision_available():
          from transformers import OneFormerImageProcessor
          from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
          from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
     from PIL import Image

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__="shi-labs/oneformer_demo" ):


      """simple docstring"""



      with open(hf_hub_download(UpperCAmelCase__   ,   UpperCAmelCase__   ,   repo_type="dataset" )   ,   "r" ) as f:
            lowerCamelCase								=		json.load(UpperCAmelCase__ )
      lowerCamelCase								=		{}
      lowerCamelCase								=		[]
      lowerCamelCase								=		[]
      for key, info in class_info.items():
            lowerCamelCase								=		info["name"]
            class_names.append(info["name"] )
            if info["isthing"]:
                  thing_ids.append(int(UpperCAmelCase__ ) )
      lowerCamelCase								=		thing_ids
      lowerCamelCase								=		class_names
      return metadata





class    lowerCamelCase__  (						unittest.TestCase):
     """simple docstring"""



     def __init__(self				,			__a				,			__a=7				,			__a=3				,			__a=30				,			__a=4_00				,			__a=None				,			__a=True				,			__a=True				,			__a=[0.5, 0.5, 0.5]				,			__a=[0.5, 0.5, 0.5]				,			__a=10				,			__a=False				,			__a=2_55				,			__a="shi-labs/oneformer_demo"				,			__a="ade20k_panoptic.json"				,			__a=10				,			):






           '''simple docstring'''



           lowerCamelCase								=		parent
           lowerCamelCase								=		batch_size
           lowerCamelCase								=		num_channels
           lowerCamelCase								=		min_resolution
           lowerCamelCase								=		max_resolution
           lowerCamelCase								=		do_resize
           lowerCamelCase								=		{"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
           lowerCamelCase								=		do_normalize
           lowerCamelCase								=		image_mean
           lowerCamelCase								=		image_std
           lowerCamelCase								=		class_info_file
           lowerCamelCase								=		prepare_metadata(__a				,			__a    )
           lowerCamelCase								=		num_text
           lowerCamelCase								=		repo_path

           # for the post_process_functions
           lowerCamelCase								=		2
           lowerCamelCase								=		10
           lowerCamelCase								=		10
           lowerCamelCase								=		3
           lowerCamelCase								=		4
           lowerCamelCase								=		num_labels
           lowerCamelCase								=		do_reduce_labels
           lowerCamelCase								=		ignore_index







     def 						_a      (self    ):






           '''simple docstring'''



           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "num_labels": self.num_labels,
               "do_reduce_labels": self.do_reduce_labels,
               "ignore_index": self.ignore_index,
               "class_info_file": self.class_info_file,
               "metadata": self.metadata,
               "num_text": self.num_text,
           }







     def 						_a      (self				,			__a				,			__a=False    ):






           '''simple docstring'''



           if not batched:
                 lowerCamelCase								=		image_inputs[0]
                 if isinstance(__a				,			Image.Image    ):
                       lowerCamelCase			,							lowerCamelCase								=		image.size
                 else:
                       lowerCamelCase			,							lowerCamelCase								=		image.shape[1], image.shape[2]
                 if w < h:
                       lowerCamelCase								=		int(self.size["shortest_edge"] * h / w    )
                       lowerCamelCase								=		self.size["shortest_edge"]
                 elif w > h:
                       lowerCamelCase								=		self.size["shortest_edge"]
                       lowerCamelCase								=		int(self.size["shortest_edge"] * w / h    )
                 else:
                       lowerCamelCase								=		self.size["shortest_edge"]
                       lowerCamelCase								=		self.size["shortest_edge"]

           else:
                 lowerCamelCase								=		[]
                 for image in image_inputs:
                       lowerCamelCase			,							lowerCamelCase								=		self.get_expected_values([image]    )
                       expected_values.append((expected_height, expected_width)    )
                 lowerCamelCase								=		max(__a				,			key=lambda __a    : item[0]    )[0]
                 lowerCamelCase								=		max(__a				,			key=lambda __a    : item[1]    )[1]

           return expected_height, expected_width







     def 						_a      (self    ):






           '''simple docstring'''



           return OneFormerForUniversalSegmentationOutput(
               # +1 for null class
               class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)    )				,			masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)    )				,			)





@require_torch
@require_vision
class    lowerCamelCase__  (						UpperCAmelCase_       ,    unittest.TestCase):
     """simple docstring"""



     _A       =			OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     _A       =			image_processing_class







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		OneFormerImageProcessorTester(self    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.image_processing_tester.prepare_image_processor_dict()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           self.assertTrue(hasattr(__a				,			"image_mean"    )    )
           self.assertTrue(hasattr(__a				,			"image_std"    )    )
           self.assertTrue(hasattr(__a				,			"do_normalize"    )    )
           self.assertTrue(hasattr(__a				,			"do_resize"    )    )
           self.assertTrue(hasattr(__a				,			"size"    )    )
           self.assertTrue(hasattr(__a				,			"ignore_index"    )    )
           self.assertTrue(hasattr(__a				,			"class_info_file"    )    )
           self.assertTrue(hasattr(__a				,			"num_text"    )    )
           self.assertTrue(hasattr(__a				,			"repo_path"    )    )
           self.assertTrue(hasattr(__a				,			"metadata"    )    )
           self.assertTrue(hasattr(__a				,			"do_reduce_labels"    )    )







     def 						_a      (self    ):






           '''simple docstring'''



           pass







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           # create random PIL images
           lowerCamelCase								=		prepare_image_inputs(self.image_processing_tester				,			equal_resolution=__a    )
           for image in image_inputs:
                 self.assertIsInstance(__a				,			Image.Image    )

           # Test not batched input
           lowerCamelCase								=		image_processor(image_inputs[0]				,			["semantic"]				,			return_tensors="pt"    ).pixel_values

           lowerCamelCase			,							lowerCamelCase								=		self.image_processing_tester.get_expected_values(__a    )

           self.assertEqual(
               encoded_images.shape				,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,			)

           # Test batched
           lowerCamelCase			,							lowerCamelCase								=		self.image_processing_tester.get_expected_values(__a				,			batched=__a    )

           lowerCamelCase								=		image_processor(
               __a				,			["semantic"] * len(__a    )				,			return_tensors="pt"    ).pixel_values
           self.assertEqual(
               encoded_images.shape				,			(
                   self.image_processing_tester.batch_size,
                   self.image_processing_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,			)







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           # create random numpy tensors
           lowerCamelCase								=		prepare_image_inputs(self.image_processing_tester				,			equal_resolution=__a				,			numpify=__a    )
           for image in image_inputs:
                 self.assertIsInstance(__a				,			np.ndarray    )

           # Test not batched input
           lowerCamelCase								=		image_processor(image_inputs[0]				,			["semantic"]				,			return_tensors="pt"    ).pixel_values

           lowerCamelCase			,							lowerCamelCase								=		self.image_processing_tester.get_expected_values(__a    )

           self.assertEqual(
               encoded_images.shape				,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,			)

           # Test batched
           lowerCamelCase			,							lowerCamelCase								=		self.image_processing_tester.get_expected_values(__a				,			batched=__a    )

           lowerCamelCase								=		image_processor(
               __a				,			["semantic"] * len(__a    )				,			return_tensors="pt"    ).pixel_values
           self.assertEqual(
               encoded_images.shape				,			(
                   self.image_processing_tester.batch_size,
                   self.image_processing_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,			)







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           # create random PyTorch tensors
           lowerCamelCase								=		prepare_image_inputs(self.image_processing_tester				,			equal_resolution=__a				,			torchify=__a    )
           for image in image_inputs:
                 self.assertIsInstance(__a				,			torch.Tensor    )

           # Test not batched input
           lowerCamelCase								=		image_processor(image_inputs[0]				,			["semantic"]				,			return_tensors="pt"    ).pixel_values

           lowerCamelCase			,							lowerCamelCase								=		self.image_processing_tester.get_expected_values(__a    )

           self.assertEqual(
               encoded_images.shape				,			(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,			)

           # Test batched
           lowerCamelCase			,							lowerCamelCase								=		self.image_processing_tester.get_expected_values(__a				,			batched=__a    )

           lowerCamelCase								=		image_processor(
               __a				,			["semantic"] * len(__a    )				,			return_tensors="pt"    ).pixel_values
           self.assertEqual(
               encoded_images.shape				,			(
                   self.image_processing_tester.batch_size,
                   self.image_processing_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,			)







     def 						_a      (self				,			__a=False				,			__a=False				,			__a="np"    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(**self.image_processor_dict    )
           # prepare image and target
           lowerCamelCase								=		self.image_processing_tester.num_labels
           lowerCamelCase								=		None
           lowerCamelCase								=		None
           lowerCamelCase								=		prepare_image_inputs(self.image_processing_tester				,			equal_resolution=__a    )
           if with_segmentation_maps:
                 lowerCamelCase								=		num_labels
                 if is_instance_map:
                       lowerCamelCase								=		list(range(__a    )    ) * 2
                       lowerCamelCase								=		dict(enumerate(__a    )    )
                 lowerCamelCase								=		[
                     np.random.randint(0				,			high * 2				,			(img.size[1], img.size[0])    ).astype(np.uinta    ) for img in image_inputs
                 ]
                 if segmentation_type == "pil":
                       lowerCamelCase								=		[Image.fromarray(__a    ) for annotation in annotations]

           lowerCamelCase								=		image_processor(
               __a				,			["semantic"] * len(__a    )				,			__a				,			return_tensors="pt"				,			instance_id_to_semantic_id=__a				,			pad_and_return_pixel_mask=__a				,			)

           return inputs







     def 						_a      (self    ):






           '''simple docstring'''



           pass







     def 						_a      (self    ):






           '''simple docstring'''



           def common(__a=False				,			__a=None    ):
                 lowerCamelCase								=		self.comm_get_image_processor_inputs(
                     with_segmentation_maps=__a				,			is_instance_map=__a				,			segmentation_type=__a    )

                 lowerCamelCase								=		inputs["mask_labels"]
                 lowerCamelCase								=		inputs["class_labels"]
                 lowerCamelCase								=		inputs["pixel_values"]
                 lowerCamelCase								=		inputs["text_inputs"]

                 # check the batch_size
                 for mask_label, class_label, text_input in zip(__a				,			__a				,			__a    ):
                       self.assertEqual(mask_label.shape[0]				,			class_label.shape[0]    )
                       # this ensure padding has happened
                       self.assertEqual(mask_label.shape[1:]				,			pixel_values.shape[2:]    )
                       self.assertEqual(len(__a    )				,			self.image_processing_tester.num_text    )

           common()
           common(is_instance_map=__a    )
           common(is_instance_map=__a				,			segmentation_type="pil"    )
           common(is_instance_map=__a				,			segmentation_type="pil"    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		np.zeros((20, 50)    )
           lowerCamelCase								=		1
           lowerCamelCase								=		1
           lowerCamelCase								=		1

           lowerCamelCase								=		binary_mask_to_rle(__a    )
           self.assertEqual(len(__a    )				,			4    )
           self.assertEqual(rle[0]				,			21    )
           self.assertEqual(rle[1]				,			45    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(
               num_labels=self.image_processing_tester.num_classes				,			max_seq_length=77				,			task_seq_length=77				,			class_info_file="ade20k_panoptic.json"				,			num_text=self.image_processing_tester.num_text				,			repo_path="shi-labs/oneformer_demo"				,			)
           lowerCamelCase								=		self.image_processing_tester.get_fake_oneformer_outputs()

           lowerCamelCase								=		fature_extractor.post_process_semantic_segmentation(__a    )

           self.assertEqual(len(__a    )				,			self.image_processing_tester.batch_size    )
           self.assertEqual(
               segmentation[0].shape				,			(
                   self.image_processing_tester.height,
                   self.image_processing_tester.width,
               )				,			)

           lowerCamelCase								=		[(1, 4) for i in range(self.image_processing_tester.batch_size    )]
           lowerCamelCase								=		fature_extractor.post_process_semantic_segmentation(__a				,			target_sizes=__a    )

           self.assertEqual(segmentation[0].shape				,			target_sizes[0]    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(
               num_labels=self.image_processing_tester.num_classes				,			max_seq_length=77				,			task_seq_length=77				,			class_info_file="ade20k_panoptic.json"				,			num_text=self.image_processing_tester.num_text				,			repo_path="shi-labs/oneformer_demo"				,			)
           lowerCamelCase								=		self.image_processing_tester.get_fake_oneformer_outputs()
           lowerCamelCase								=		image_processor.post_process_instance_segmentation(__a				,			threshold=0    )

           self.assertTrue(len(__a    ) == self.image_processing_tester.batch_size    )
           for el in segmentation:
                 self.assertTrue("segmentation" in el    )
                 self.assertTrue("segments_info" in el    )
                 self.assertEqual(type(el["segments_info"]    )				,			__a    )
                 self.assertEqual(
                     el["segmentation"].shape				,			(self.image_processing_tester.height, self.image_processing_tester.width)    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.image_processing_class(
               num_labels=self.image_processing_tester.num_classes				,			max_seq_length=77				,			task_seq_length=77				,			class_info_file="ade20k_panoptic.json"				,			num_text=self.image_processing_tester.num_text				,			repo_path="shi-labs/oneformer_demo"				,			)
           lowerCamelCase								=		self.image_processing_tester.get_fake_oneformer_outputs()
           lowerCamelCase								=		image_processor.post_process_panoptic_segmentation(__a				,			threshold=0    )

           self.assertTrue(len(__a    ) == self.image_processing_tester.batch_size    )
           for el in segmentation:
                 self.assertTrue("segmentation" in el    )
                 self.assertTrue("segments_info" in el    )
                 self.assertEqual(type(el["segments_info"]    )				,			__a    )
                 self.assertEqual(
                     el["segmentation"].shape				,			(self.image_processing_tester.height, self.image_processing_tester.width)    )




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]}""")