



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_						:     int	     =		logging.get_logger(__name__)

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase								=		[]
      for i in range(config.num_hidden_layers ):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
            rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
            rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
            rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
            rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
            rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
            rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
            rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
            rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
            rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("cls_token", "vit.embeddings.cls_token"),
              ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
              ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
              ("pos_embed", "vit.embeddings.position_embeddings"),
          ] )

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("norm.weight", "layernorm.weight"),
                    ("norm.bias", "layernorm.bias"),
                    ("pre_logits.fc.weight", "pooler.dense.weight"),
                    ("pre_logits.fc.bias", "pooler.dense.bias"),
                ] )

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            lowerCamelCase								=		[(pair[0], pair[1][4:]) if pair[1].startswith("vit" ) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ("norm.weight", "vit.layernorm.weight"),
                    ("norm.bias", "vit.layernorm.bias"),
                    ("head.weight", "classifier.weight"),
                    ("head.bias", "classifier.bias"),
                ] )

      return rename_keys

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      for i in range(config.num_hidden_layers ):
            if base_model:
                  lowerCamelCase								=		""
            else:
                  lowerCamelCase								=		"vit."
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            lowerCamelCase								=		state_dict.pop(F"""blocks.{i}.attn.qkv.weight""" )
            lowerCamelCase								=		state_dict.pop(F"""blocks.{i}.attn.qkv.bias""" )
            # next, add query, keys and values (in that order) to the state dict
            lowerCamelCase								=		in_proj_weight[
                : config.hidden_size, :
            ]
            lowerCamelCase								=		in_proj_bias[: config.hidden_size]
            lowerCamelCase								=		in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowerCamelCase								=		in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowerCamelCase								=		in_proj_weight[
                -config.hidden_size :, :
            ]
            lowerCamelCase								=		in_proj_bias[-config.hidden_size :]

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		["head.weight", "head.bias"]
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		dct.pop(UpperCAmelCase__ )
      lowerCamelCase								=		val

def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		"http://images.cocodataset.org/val2017/000000039769.jpg"
      lowerCamelCase								=		Image.open(requests.get(UpperCAmelCase__   ,   stream=UpperCAmelCase__ ).raw )
      return im

@torch.no_grad()
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		ViTConfig()
      lowerCamelCase								=		False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            lowerCamelCase								=		True
            lowerCamelCase								=		int(vit_name[-12:-10] )
            lowerCamelCase								=		int(vit_name[-9:-6] )
      else:
            lowerCamelCase								=		1000
            lowerCamelCase								=		"huggingface/label-files"
            lowerCamelCase								=		"imagenet-1k-id2label.json"
            lowerCamelCase								=		json.load(open(hf_hub_download(UpperCAmelCase__   ,   UpperCAmelCase__   ,   repo_type="dataset" )   ,   "r" ) )
            lowerCamelCase								=		{int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
            lowerCamelCase								=		idalabel
            lowerCamelCase								=		{v: k for k, v in idalabel.items()}
            lowerCamelCase								=		int(vit_name[-6:-4] )
            lowerCamelCase								=		int(vit_name[-3:] )
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith("tiny" ):
                  lowerCamelCase								=		192
                  lowerCamelCase								=		768
                  lowerCamelCase								=		12
                  lowerCamelCase								=		3
            elif vit_name[9:].startswith("small" ):
                  lowerCamelCase								=		384
                  lowerCamelCase								=		1536
                  lowerCamelCase								=		12
                  lowerCamelCase								=		6
            else:
                  pass
      else:
            if vit_name[4:].startswith("small" ):
                  lowerCamelCase								=		768
                  lowerCamelCase								=		2304
                  lowerCamelCase								=		8
                  lowerCamelCase								=		8
            elif vit_name[4:].startswith("base" ):
                  pass
            elif vit_name[4:].startswith("large" ):
                  lowerCamelCase								=		1024
                  lowerCamelCase								=		4096
                  lowerCamelCase								=		24
                  lowerCamelCase								=		16
            elif vit_name[4:].startswith("huge" ):
                  lowerCamelCase								=		1280
                  lowerCamelCase								=		5120
                  lowerCamelCase								=		32
                  lowerCamelCase								=		16

    # load original model from timm
      lowerCamelCase								=		timm.create_model(UpperCAmelCase__   ,   pretrained=UpperCAmelCase__ )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowerCamelCase								=		timm_model.state_dict()
      if base_model:
            remove_classification_head_(UpperCAmelCase__ )
      lowerCamelCase								=		create_rename_keys(UpperCAmelCase__   ,   UpperCAmelCase__ )
      for src, dest in rename_keys:
            rename_key(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )
      read_in_q_k_v(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            lowerCamelCase								=		ViTModel(UpperCAmelCase__ ).eval()
      else:
            lowerCamelCase								=		ViTForImageClassification(UpperCAmelCase__ ).eval()
      model.load_state_dict(UpperCAmelCase__ )

      # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
      if "deit" in vit_name:
            lowerCamelCase								=		DeiTImageProcessor(size=config.image_size )
      else:
            lowerCamelCase								=		ViTImageProcessor(size=config.image_size )
      lowerCamelCase								=		image_processor(images=prepare_img()   ,   return_tensors="pt" )
      lowerCamelCase								=		encoding["pixel_values"]
      lowerCamelCase								=		model(UpperCAmelCase__ )

      if base_model:
            lowerCamelCase								=		timm_model.forward_features(UpperCAmelCase__ )
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(UpperCAmelCase__   ,   outputs.pooler_output   ,   atol=1E-3 )
      else:
            lowerCamelCase								=		timm_model(UpperCAmelCase__ )
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(UpperCAmelCase__   ,   outputs.logits   ,   atol=1E-3 )

      Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
      print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}""" )
      model.save_pretrained(UpperCAmelCase__ )
      print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
      image_processor.save_pretrained(UpperCAmelCase__ )


if __name__ == "__main__":
     a_						:     Tuple	     =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--vit_name',
         default='vit_base_patch16_224',
         type=str,
         help='Name of the ViT timm model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     a_						:     Any	     =		parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




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]}""")