


'''simple docstring'''





import json
import os
import re
import sys
import urllib.request

import requests
from bsa import BeautifulSoup

__lowerCamelCase												=						{
    '''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
    ''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
}


def        a__     (					UpperCamelCase_						:      str = "dhaka",   UpperCamelCase_						:      int = 5						):
   UpperCAmelCase__	:Optional[Any]      =      min(UpperCamelCase_,   50						)  # Prevent abuse!
   UpperCAmelCase__	:Tuple      =      {
       '''q''': query,
       '''tbm''': '''isch''',
       '''hl''': '''en''',
       '''ijn''': '''0''',
   }

   UpperCAmelCase__	:List[str]      =      requests.get('''https://www.google.com/search''',   params=UpperCamelCase_,   headers=UpperCamelCase_						)
   UpperCAmelCase__	:List[str]      =      BeautifulSoup(html.text,   '''html.parser'''						)
   UpperCAmelCase__	:Union[str, Any]      =      ''''''.join(
       re.findall(r'''AF_initDataCallback\(([^<]+)\);''',   str(soup.select('''script'''						)						)						)						)

   UpperCAmelCase__	:List[Any]      =      json.dumps(UpperCamelCase_						)
   UpperCAmelCase__	:Dict      =      json.loads(UpperCamelCase_						)

   UpperCAmelCase__	:Any      =      re.findall(
       r'''\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",''',   UpperCamelCase_,   )
   if not matched_google_image_data:
      return 0

   UpperCAmelCase__	:Any      =      re.sub(
       r'''\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]''',   '''''',   str(UpperCamelCase_						),   )

   UpperCAmelCase__	:Union[str, Any]      =      re.findall(
       r'''(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]''',   UpperCamelCase_,   )
   for index, fixed_full_res_image in enumerate(UpperCamelCase_						):
      if index >= max_images:
         return index
      UpperCAmelCase__	:int      =      bytes(UpperCamelCase_,   '''ascii'''						).decode(
          '''unicode-escape'''						)
      UpperCAmelCase__	:Any      =      bytes(UpperCamelCase_,   '''ascii'''						).decode(
          '''unicode-escape'''						)
      UpperCAmelCase__	:Tuple      =      urllib.request.build_opener()
      UpperCAmelCase__	:int      =      [
          (
              '''User-Agent''',
              '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
              ''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582''',
          )
      ]
      urllib.request.install_opener(UpperCamelCase_						)
      UpperCAmelCase__	:Optional[int]      =      F'''query_{query.replace(" ",   "_"						)}'''
      if not os.path.exists(UpperCamelCase_						):
         os.makedirs(UpperCamelCase_						)
      urllib.request.urlretrieve(  # noqa: S310
          UpperCamelCase_,   F'''{path_name}/original_size_img_{index}.jpg'''						)
   return index


if __name__ == "__main__":
    try:
        __lowerCamelCase												=						download_images_from_google_query(sys.argv[1])
        print(F'''{image_count} images were downloaded to disk.''')
    except IndexError:
        print('''Please provide a search term.''')
        raise







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    __lowerCamelCase												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



