


"""simple docstring"""


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(_UpperCamelCase					,				_UpperCamelCase   )


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase					,			__lowerCAmelCase       					=    emb.weight.shape
			__lowerCAmelCase       					=    nn.Linear(_UpperCamelCase					,				_UpperCamelCase					,				bias=_UpperCamelCase   )
			__lowerCAmelCase       					=    emb.weight.data
			return lin_layer


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    torch.load(_UpperCamelCase					,				map_location="cpu"   )
			__lowerCAmelCase       					=    Namespace(**checkpoint["cfg"]["model"]   )
			__lowerCAmelCase       					=    checkpoint["model"]
			remove_ignore_keys_(_UpperCamelCase   )
			__lowerCAmelCase       					=    state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase       					=    {key.replace("decoder"					,				"model"   ): val for key, val in state_dict.items()}

			__lowerCAmelCase       					=    XGLMConfig(
			    vocab_size=_UpperCamelCase					,				max_position_embeddings=args.max_target_positions					,				num_layers=args.decoder_layers					,				attention_heads=args.decoder_attention_heads					,				ffn_dim=args.decoder_ffn_embed_dim					,				d_model=args.decoder_embed_dim					,				layerdrop=args.decoder_layerdrop					,				dropout=args.dropout					,				attention_dropout=args.attention_dropout					,				activation_dropout=args.activation_dropout					,				activation_function="gelu"					,				scale_embedding=not args.no_scale_embedding					,				tie_word_embeddings=args.share_decoder_input_output_embed					,				)

			__lowerCAmelCase       					=    XGLMForCausalLM(_UpperCamelCase   )
			__lowerCAmelCase       					=    model.load_state_dict(_UpperCamelCase					,				strict=_UpperCamelCase   )
			print(_UpperCamelCase   )
			__lowerCAmelCase       					=    make_linear_from_emb(model.model.embed_tokens   )

			return model


if __name__ == "__main__":
	A  :     Tuple    					=    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.")
	A  :     Dict    					=    parser.parse_args()
	A  :     int    					=    convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)








"""simple docstring"""


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

import requests
from bsa import BeautifulSoup

A  :     Union[str, 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			(	_UpperCamelCase = "dhaka"					,				_UpperCamelCase = 5   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    min(_UpperCamelCase					,				50   )  # Prevent abuse!
			__lowerCAmelCase       					=    {
			    "q": query,
			    "tbm": "isch",
			    "hl": "en",
			    "ijn": "0",
			}

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

			__lowerCAmelCase       					=    json.dumps(_UpperCamelCase   )
			__lowerCAmelCase       					=    json.loads(_UpperCamelCase   )

			__lowerCAmelCase       					=    re.findall(
			    R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\","					,				_UpperCamelCase					,				)
			if not matched_google_image_data:
						return 0

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

			__lowerCAmelCase       					=    re.findall(
			    R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]"					,				_UpperCamelCase					,				)
			for index, fixed_full_res_image in enumerate(_UpperCamelCase   ):
						if index >= max_images:
									return index
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    bytes(_UpperCamelCase					,				"ascii"   ).decode(
						    "unicode-escape"   )
						__lowerCAmelCase       					=    urllib.request.build_opener()
						__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",
						    )
						]
						urllib.request.install_opener(_UpperCamelCase   )
						__lowerCAmelCase       					=    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:
		A  :     Any    					=    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




