

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 					lowerCamelCase_     (							UpperCAmelCase_							:     str = "dhaka"							,       UpperCAmelCase_							:     int = 5	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						min(UpperCAmelCase_							,       5_0	)  # Prevent abuse!
	_UpperCamelCase   :		int			=						{
	    'q': query,
	    'tbm': 'isch',
	    'hl': 'en',
	    'ijn': '0',
	}

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

	_UpperCamelCase   :		Optional[Any]			=						json.dumps(UpperCAmelCase_	)
	_UpperCamelCase   :		List[str]			=						json.loads(UpperCAmelCase_	)

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

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

	_UpperCamelCase   :		int			=						re.findall(
	    R'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]'							,       UpperCAmelCase_							,       )
	for index, fixed_full_res_image in enumerate(UpperCAmelCase_	):
		if index >= max_images:
			return index
		_UpperCamelCase   :		Optional[Any]			=						bytes(UpperCAmelCase_							,       'ascii'	).decode(
		    'unicode-escape'	)
		_UpperCamelCase   :		Union[str, Any]			=						bytes(UpperCAmelCase_							,       'ascii'	).decode(
		    'unicode-escape'	)
		_UpperCamelCase   :		List[str]			=						urllib.request.build_opener()
		_UpperCamelCase   :		Tuple			=						[
		    (
		        '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   :		Union[str, Any]			=						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







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		[
    ["""attention""", """attn"""],
    ["""encoder_attention""", """encoder_attn"""],
    ["""q_lin""", """q_proj"""],
    ["""k_lin""", """k_proj"""],
    ["""v_lin""", """v_proj"""],
    ["""out_lin""", """out_proj"""],
    ["""norm_embeddings""", """layernorm_embedding"""],
    ["""position_embeddings""", """embed_positions"""],
    ["""embeddings""", """embed_tokens"""],
    ["""ffn.lin""", """fc"""],
]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Optional[int]:




	'''simple docstring'''




	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		_UpperCamelCase   :		List[Any]			=						k.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

	if k.startswith('encoder'	):
		_UpperCamelCase   :		Optional[Any]			=						k.replace('.attn'							,       '.self_attn'	)
		_UpperCamelCase   :		Optional[int]			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'final_layer_norm'	)
	elif k.startswith('decoder'	):
		_UpperCamelCase   :		Any			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'encoder_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm3'							,       'final_layer_norm'	)
	return k






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		_UpperCamelCase   :		Optional[int]			=						sd.pop(UpperCAmelCase_	)
		_UpperCamelCase   :		str			=						k.replace('layernorm_embedding'							,       'layer_norm'	)
		assert new_k not in sd
		_UpperCamelCase   :		Tuple			=						v


lowerCAmelCase__								=		["""START"""]






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		int			=						model['model']
	_UpperCamelCase   :		List[Any]			=						BlenderbotConfig.from_json_file(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						BlenderbotForConditionalGeneration(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						m.model.state_dict().keys()
	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		int			=						{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		_UpperCamelCase   :		Optional[int]			=						rename_state_dict_key(UpperCAmelCase_	)
		if new_k not in valid_keys:
			failures.append([k, new_k]	)
		else:
			_UpperCamelCase   :		int			=						v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCAmelCase_	)
	m.model.load_state_dict(UpperCAmelCase_							,       strict=UpperCAmelCase_	)
	m.half()
	m.save_pretrained(UpperCAmelCase_	)


if __name__ == "__main__":
							lowerCAmelCase__								=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""--src_path""", type=str, help="""like blenderbot-model.bin""")
							parser.add_argument("""--save_dir""", default="""hf_blenderbot""", type=str, help="""Where to save converted model.""")
							parser.add_argument(
							    """--hf_config_json""", default="""blenderbot-3b-config.json""", type=str, help="""Path to config to use"""
							)
							lowerCAmelCase__								=		parser.parse_args()
							convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




