
'''simple docstring'''



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

import requests
from bsa import BeautifulSoup

UpperCamelCase__      :		Optional[Any]       							=  {
    "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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = "dhaka"    ,      SCREAMING_SNAKE_CASE_ = 5						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				min(SCREAMING_SNAKE_CASE_    ,      50						)  # Prevent abuse!
   _SCREAMING_SNAKE_CASE      =				{
       """q""": query,
       """tbm""": """isch""",
       """hl""": """en""",
       """ijn""": """0""",
   }

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

   _SCREAMING_SNAKE_CASE      =				json.dumps(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				json.loads(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				re.findall(
       r"""\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","""    ,      SCREAMING_SNAKE_CASE_    ,      )
   if not matched_google_image_data:
      return 0

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

   _SCREAMING_SNAKE_CASE      =				re.findall(
       r"""(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"""    ,      SCREAMING_SNAKE_CASE_    ,      )
   for index, fixed_full_res_image in enumerate(SCREAMING_SNAKE_CASE_						):
      if index >= max_images:
         return index
      _SCREAMING_SNAKE_CASE      =				bytes(SCREAMING_SNAKE_CASE_    ,      """ascii"""						).decode(
          """unicode-escape"""						)
      _SCREAMING_SNAKE_CASE      =				bytes(SCREAMING_SNAKE_CASE_    ,      """ascii"""						).decode(
          """unicode-escape"""						)
      _SCREAMING_SNAKE_CASE      =				urllib.request.build_opener()
      _SCREAMING_SNAKE_CASE      =				[
          (
              """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(SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				F"query_{query.replace(' '    ,      '_'						)}"
      if not os.path.exists(SCREAMING_SNAKE_CASE_						):
         os.makedirs(SCREAMING_SNAKE_CASE_						)
      urllib.request.urlretrieve(  # noqa: S310
          SCREAMING_SNAKE_CASE_    ,      F"{path_name}/original_size_img_{index}.jpg"						)
   return index


if __name__ == "__main__":
     try:
          UpperCamelCase__      :		List[str]       							=  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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[
       """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(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				emb.weight.shape
   _SCREAMING_SNAKE_CASE      =				nn.Linear(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      bias=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				emb.weight.data
   return lin_layer
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
   _SCREAMING_SNAKE_CASE      =				mam_aaa["""model"""]
   remove_ignore_keys_(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				state_dict["""encoder.embed_tokens.weight"""].shape[0]

   _SCREAMING_SNAKE_CASE      =				MaMaaaConfig(
       vocab_size=SCREAMING_SNAKE_CASE_    ,      max_position_embeddings=10_24    ,      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"""    ,      )

   _SCREAMING_SNAKE_CASE      =				state_dict["""decoder.embed_tokens.weight"""]
   _SCREAMING_SNAKE_CASE      =				MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_						)
   model.model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
     UpperCamelCase__      :		Dict       							=  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.")
     UpperCamelCase__      :		List[str]       							=  parser.parse_args()
     UpperCamelCase__      :		Any       							=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)






