






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__lowerCamelCase      :    str   		=   {
    """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""",
}

__lowerCamelCase      :    Optional[int]   		=   AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Tuple=False     ):
	snake_case__,		snake_case__				:   Any     =						create_model(
	    "HTSAT-tiny"  ,						"roberta"  ,						snake_case_  ,						precision="fp32"  ,						device="cuda:0" if torch.cuda.is_available() else "cpu"  ,						enable_fusion=snake_case_  ,						fusion_type="aff_2d" if enable_fusion else None  ,						)
	return model, model_cfg







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dict     ):
	snake_case__				:   Any     =						{}

	snake_case__				:   Optional[Any]     =						R".*sequential.(\d+).*"
	snake_case__				:   Optional[Any]     =						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:
				snake_case__				:   Dict     =						key.replace(snake_case_  ,						snake_case_     )

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

			snake_case__				:   Union[str, Any]     =						key.replace(F'''sequential.{sequential_layer}.'''  ,						F'''layers.{int(snake_case_     )//3}.linear.'''     )
		elif re.match(snake_case_  ,						snake_case_     ):
			snake_case__				:   str     =						int(re.match(snake_case_  ,						snake_case_     ).group(1     )     )

			# Because in CLAP they use `nn.Sequential`...
			snake_case__				:   Union[str, Any]     =						1 if projecton_layer == 0 else 2

			snake_case__				:   Tuple     =						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
			snake_case__				:   int     =						value
			snake_case__				:   Optional[Any]     =						mixed_qkv.size(0     ) // 3

			snake_case__				:   List[Any]     =						mixed_qkv[:qkv_dim]
			snake_case__				:   Union[str, Any]     =						mixed_qkv[qkv_dim : qkv_dim * 2]
			snake_case__				:   Optional[Any]     =						mixed_qkv[qkv_dim * 2 :]

			snake_case__				:   List[str]     =						query_layer
			snake_case__				:   Optional[int]     =						key_layer
			snake_case__				:   List[Any]     =						value_layer
		else:
			snake_case__				:   Tuple     =						value

	return model_state_dict







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Union[str, Any]=False     ):
	snake_case__,		snake_case__				:   List[Any]     =						init_clap(snake_case_  ,						enable_fusion=snake_case_     )

	clap_model.eval()
	snake_case__				:   List[Any]     =						clap_model.state_dict()
	snake_case__				:   List[Any]     =						rename_state_dict(snake_case_     )

	snake_case__				:   str     =						ClapConfig()
	snake_case__				:   Optional[Any]     =						enable_fusion
	snake_case__				:   str     =						ClapModel(snake_case_     )

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

	model.save_pretrained(snake_case_     )
	transformers_config.save_pretrained(snake_case_     )


if __name__ == "__main__":
	__lowerCamelCase      :    str   		=   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""")
	__lowerCamelCase      :    Optional[int]   		=   parser.parse_args()

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











# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCamelCase      :    Dict   		=   abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							str     ):
	from diffusers.utils.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(snake_case_     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any     ):
	from diffusers.utils.testing_utils import pytest_terminal_summary_main

	snake_case__				:   Optional[int]     =						terminalreporter.config.getoption("--make-reports"     )
	if make_reports:
		pytest_terminal_summary_main(snake_case_  ,						id=snake_case_     )



