


from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def       UpperCAmelCase__						(UpperCamelCase_ = "mumbai"    ):



     """simple docstring"""






     snake_case					=    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'''}    ):
          snake_case					=    job.find('''a'''							,attrs={'''data-tn-element''': '''jobTitle'''}    ).text.strip()
          snake_case					=    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 argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
             F'''stage{idx}.patch_embed.proj.weight''',
         )    )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
             F'''stage{idx}.patch_embed.proj.bias''',
         )    )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
             F'''stage{idx}.patch_embed.norm.weight''',
         )    )
     embed.append(
         (
             F'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
             F'''stage{idx}.patch_embed.norm.bias''',
         )    )
     return embed
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
             F'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
             F'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
         )    )
     attention_weights.append(
         (
             F'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
             F'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
         )    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', F'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', F'''stage{idx}.blocks.{cnt}.norm1.weight''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', F'''stage{idx}.blocks.{cnt}.norm1.bias''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', F'''stage{idx}.blocks.{cnt}.norm2.weight''')    )
     attention_weights.append(
         (F'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', F'''stage{idx}.blocks.{cnt}.norm2.bias''')    )
     return attention_weights
def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    []
     token.append((F'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
     return token
def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    []
     head.append(('''layernorm.weight''', '''norm.weight''')    )
     head.append(('''layernorm.bias''', '''norm.bias''')    )
     head.append(('''classifier.weight''', '''head.weight''')    )
     head.append(('''classifier.bias''', '''head.bias''')    )
     return head
def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    '''imagenet-1k-id2label.json'''
     snake_case					=    10_00

     snake_case					=    '''huggingface/label-files'''
     snake_case					=    num_labels
     snake_case					=    json.load(open(cached_download(hf_hub_url(UpperCamelCase_							,UpperCamelCase_							,repo_type='''dataset'''    )    )							,'''r'''    )    )
     snake_case					=    {int(UpperCamelCase_    ): v for k, v in idalabel.items()}

     snake_case					=    idalabel
     snake_case					=    {v: k for k, v in idalabel.items()}

     snake_case					=    snake_case					=    CvtConfig(num_labels=UpperCamelCase_							,idalabel=UpperCamelCase_							,labelaid=UpperCamelCase_    )

     # For depth size 13 (13 = 1+2+10)
     if cvt_model.rsplit('''/'''							,1    )[-1][4:6] == "13":
          snake_case					=    [1, 2, 10]

     # For depth size 21 (21 = 1+4+16)
     elif cvt_model.rsplit('''/'''							,1    )[-1][4:6] == "21":
          snake_case					=    [1, 4, 16]

     # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
     else:
          snake_case					=    [2, 2, 20]
          snake_case					=    [3, 12, 16]
          snake_case					=    [1_92, 7_68, 10_24]

     snake_case					=    CvtForImageClassification(UpperCamelCase_    )
     snake_case					=    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
     snake_case					=    image_size
     snake_case					=    torch.load(UpperCamelCase_							,map_location=torch.device('''cpu'''    )    )

     snake_case					=    OrderedDict()
     snake_case					=    []

     for idx in range(len(config.depth    )    ):
          if config.cls_token[idx]:
               snake_case					=    list_of_state_dict + cls_token(UpperCamelCase_    )
          snake_case					=    list_of_state_dict + embeddings(UpperCamelCase_    )
          for cnt in range(config.depth[idx]    ):
               snake_case					=    list_of_state_dict + attention(UpperCamelCase_							,UpperCamelCase_    )

     snake_case					=    list_of_state_dict + final()
     for gg in list_of_state_dict:
          print(UpperCamelCase_    )
     for i in range(len(UpperCamelCase_    )    ):
          snake_case					=    original_weights[list_of_state_dict[i][1]]

     model.load_state_dict(UpperCamelCase_    )
     model.save_pretrained(UpperCamelCase_    )
     image_processor.save_pretrained(UpperCamelCase_    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   argparse.ArgumentParser()
 parser.add_argument(
     "--cvt_model",
     default="cvt-w24",
     type=str,
     help="Name of the cvt model you'd like to convert.",
 )
 parser.add_argument(
     "--image_size",
     default=3_84,
     type=int,
     help="Input Image Size",
 )
 parser.add_argument(
     "--cvt_file_name",
     default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
     type=str,
     help="Input Image Size",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )

 _SCREAMING_SNAKE_CASE      :			Optional[int]      				=   parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




