
import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_A   =			{
    "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   =			AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")



def 						lowercase_      (       A__			,		A__=False		)							->							List[str]:
				"""simple docstring"""

				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 						lowercase_      (       A__		)							->							Any:
				"""simple docstring"""

				snake_case								= {}

				snake_case								= R""".*sequential.(\d+).*"""
				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:
																snake_case								= key.replace(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE		)

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

												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		):
												snake_case								= int(re.match(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE		).group(1		)		)

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

												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
												snake_case								= value
												snake_case								= mixed_qkv.size(0		) // 3

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

												snake_case								= query_layer
												snake_case								= key_layer
												snake_case								= value_layer
								else:
												snake_case								= value

				return model_state_dict



def 						lowercase_      (       A__			,		A__			,		A__			,		A__=False		)							->							Tuple:
				"""simple docstring"""

				snake_case								= init_clap(__SCREAMING_SNAKE_CASE			,		enable_fusion=__SCREAMING_SNAKE_CASE		)

				clap_model.eval()
				snake_case								= clap_model.state_dict()
				snake_case								= rename_state_dict(__SCREAMING_SNAKE_CASE		)

				snake_case								= ClapConfig()
				snake_case								= enable_fusion
				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   =			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   =			parser.parse_args()

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



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def 						lowercase_      (       A__		)							->							bool:
    """simple docstring"""

    snake_case								= int(number**0.5		)
    return number == sq * sq



def 						lowercase_      (       A__			,		A__			,		A__			,		A__			,		A__			,		A__		)							->							tuple[int, int]:
    """simple docstring"""

    snake_case								= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    snake_case								= x_den * y_den * z_den
    snake_case								= gcd(A__			,		A__		)
    top //= hcf
    bottom //= hcf
    return top, bottom



def 						lowercase_      (       A__ = 35		)							->							int:
    """simple docstring"""

    snake_case								= set()
    snake_case								= 42
    snake_case								= Fraction(0		)
    snake_case								= 42

    for x_num in range(1			,		order + 1		):
        for x_den in range(x_num + 1			,		order + 1		):
            for y_num in range(1			,		order + 1		):
                for y_den in range(y_num + 1			,		order + 1		):
                    # n=1
                    snake_case								= x_num * y_den + x_den * y_num
                    snake_case								= x_den * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= (
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    snake_case								= x_den * x_den * y_den * y_den
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

                    # n=-1
                    snake_case								= x_num * y_num
                    snake_case								= x_den * y_num + x_num * y_den
                    snake_case								= gcd(A__			,		A__		)
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        snake_case								= add_three(
                            A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                        unique_s.add(A__		)

                    # n=2
                    snake_case								= x_num * x_num * y_num * y_num
                    snake_case								= (
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(A__		) and is_sq(A__		):
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= int(sqrt(A__		)		)
                        snake_case								= gcd(A__			,		A__		)
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            snake_case								= add_three(
                                A__			,		A__			,		A__			,		A__			,		A__			,		A__		)
                            unique_s.add(A__		)

    for num, den in unique_s:
        total += Fraction(A__			,		A__		)

    return total.denominator + total.numerator


if __name__ == "__main__":
   print(f"{solution() = }")

