




import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__A						:							int							=  {
    """text_branch""": """text_model""",
    """audio_branch""": """audio_model.audio_encoder""",
    """attn""": """attention.self""",
    """self.proj""": """output.dense""",
    """attention.self_mask""": """attn_mask""",
    """mlp.fc1""": """intermediate.dense""",
    """mlp.fc2""": """output.dense""",
    """norm1""": """layernorm_before""",
    """norm2""": """layernorm_after""",
    """bn0""": """batch_norm""",
}

__A						:							int							=  AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					create_model(
			    """HTSAT-tiny"""					,	"""roberta"""					,	SCREAMING_SNAKE_CASE					,	precision="""fp32"""					,	device="""cuda:0""" if torch.cuda.is_available() else """cpu"""					,	enable_fusion=SCREAMING_SNAKE_CASE					,	fusion_type="""aff_2d""" if enable_fusion else None					,	)
			return model, model_cfg
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					{}

			SCREAMING_SNAKE_CASE           =					r""".*sequential.(\d+).*"""
			SCREAMING_SNAKE_CASE           =					r""".*_projection.(\d+).*"""

			for key, value in state_dict.items():
						# check if any key needs to be modified
						for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
									if key_to_modify in key:
												SCREAMING_SNAKE_CASE           =					key.replace(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

						if re.match(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
									# replace sequential layers with list
									SCREAMING_SNAKE_CASE           =					re.match(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ).group(1   )

									SCREAMING_SNAKE_CASE           =					key.replace(f"""sequential.{sequential_layer}."""					,	f"""layers.{int(SCREAMING_SNAKE_CASE   )//3}.linear."""   )
						elif re.match(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
									SCREAMING_SNAKE_CASE           =					int(re.match(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ).group(1   )   )

									# Because in CLAP they use `nn.Sequential`...
									SCREAMING_SNAKE_CASE           =					1 if projecton_layer == 0 else 2

									SCREAMING_SNAKE_CASE           =					key.replace(f"""_projection.{projecton_layer}."""					,	f"""_projection.linear{transformers_projection_layer}."""   )

						if "audio" and "qkv" in key:
									# split qkv into query key and value
									SCREAMING_SNAKE_CASE           =					value
									SCREAMING_SNAKE_CASE           =					mixed_qkv.size(0   ) // 3

									SCREAMING_SNAKE_CASE           =					mixed_qkv[:qkv_dim]
									SCREAMING_SNAKE_CASE           =					mixed_qkv[qkv_dim : qkv_dim * 2]
									SCREAMING_SNAKE_CASE           =					mixed_qkv[qkv_dim * 2 :]

									SCREAMING_SNAKE_CASE           =					query_layer
									SCREAMING_SNAKE_CASE           =					key_layer
									SCREAMING_SNAKE_CASE           =					value_layer
						else:
									SCREAMING_SNAKE_CASE           =					value

			return model_state_dict
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					init_clap(SCREAMING_SNAKE_CASE					,	enable_fusion=SCREAMING_SNAKE_CASE   )

			clap_model.eval()
			SCREAMING_SNAKE_CASE           =					clap_model.state_dict()
			SCREAMING_SNAKE_CASE           =					rename_state_dict(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					ClapConfig()
			SCREAMING_SNAKE_CASE           =					enable_fusion
			SCREAMING_SNAKE_CASE           =					ClapModel(SCREAMING_SNAKE_CASE   )

			# ignore the spectrogram embedding layer
			model.load_state_dict(SCREAMING_SNAKE_CASE					,	strict=SCREAMING_SNAKE_CASE   )

			model.save_pretrained(SCREAMING_SNAKE_CASE   )
			transformers_config.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		__A						:							List[Any]							=  argparse.ArgumentParser()
		parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
		parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
		parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
		__A						:							Union[str, Any]							=  parser.parse_args()

		convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)












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

import requests
from bsa import BeautifulSoup

__A						:							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"""
}
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE = "dhaka"					,	SCREAMING_SNAKE_CASE = 5   ):
			'''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:
				__A						:							List[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






