





'''simple docstring'''


import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase_   :   Optional[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",
}

lowerCAmelCase_   :   Any       =					AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[Any]=False     )   ->    List[Any]:
			a__   ,				a__     = create_model(
			    "HTSAT-tiny"					,  "roberta"					,  __lowerCamelCase					,  precision="fp32"					,  device="cuda:0" if torch.cuda.is_available() else "cpu"					,  enable_fusion=__lowerCamelCase					,  fusion_type="aff_2d" if enable_fusion else None					,  )
			return model, model_cfg







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = {}

			a__     = r".*sequential.(\d+).*"
			a__     = 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:
												a__     = key.replace(__lowerCamelCase					,  __lowerCamelCase     )

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

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

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

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

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

									a__     = query_layer
									a__     = key_layer
									a__     = value_layer
						else:
									a__     = value

			return model_state_dict







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Dict					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     str=False     )   ->    List[str]:
			a__   ,				a__     = init_clap(__lowerCamelCase					,  enable_fusion=__lowerCamelCase     )

			clap_model.eval()
			a__     = clap_model.state_dict()
			a__     = rename_state_dict(__lowerCamelCase     )

			a__     = ClapConfig()
			a__     = enable_fusion
			a__     = ClapModel(__lowerCamelCase     )

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

			model.save_pretrained(__lowerCamelCase     )
			transformers_config.save_pretrained(__lowerCamelCase     )


if __name__ == "__main__":
					lowerCAmelCase_   :   Union[str, 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")
					lowerCAmelCase_   :   str       =					parser.parse_args()

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










'''simple docstring'''


from manim import *






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = Rectangle(height=0.5 , width=0.5		)
				a__     = Rectangle(height=0.25 , width=0.25		)
				a__     = Rectangle(height=0.46 , width=0.46		).set_stroke(width=0		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("CPU" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				cpu.move_to([-2.5, -0.5, 0]		)
				self.add(lowerCamelCase		)

				a__     = [mem.copy() for i in range(4		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("GPU" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				gpu.move_to([-1, -1, 0]		)
				self.add(lowerCamelCase		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)

				a__     = Text("Model" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				model.move_to([3, -1.0, 0]		)
				self.add(lowerCamelCase		)

				a__     = []
				a__     = []
				a__     = []

				for i, rect in enumerate(lowerCamelCase		):
							rect.set_stroke(lowerCamelCase		)

							a__     = Rectangle(height=0.46 / 4 , width=0.46 / 3		).set_stroke(width=0.0		).set_fill(lowerCamelCase , opacity=0.7		)

							if i == 0:
										cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		) , buff=0.02 , direction=lowerCamelCase		)
										cpu_target.set_x(cpu_target.get_x() + 0.1		)
							elif i == 3:
										cpu_target.next_to(model_cpu_arr[0] , direction=lowerCamelCase , buff=0.0		)
							else:
										cpu_target.next_to(model_cpu_arr[i - 1] , direction=lowerCamelCase , buff=0.0		)
							self.add(lowerCamelCase		)
							model_cpu_arr.append(lowerCamelCase		)

				self.add(*lowerCamelCase , *lowerCamelCase , *lowerCamelCase		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)

				a__     = Text("Loaded Checkpoint" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				checkpoint.move_to([3, 0.5, 0]		)

				self.add(lowerCamelCase		)

				a__     = []
				a__     = []

				for i, rect in enumerate(lowerCamelCase		):
							a__     = fill.copy().set_fill(lowerCamelCase , opacity=0.7		)
							target.move_to(lowerCamelCase		)
							ckpt_arr.append(lowerCamelCase		)

							a__     = target.copy()
							if i < 5:
										cpu_target.move_to(cpu_left_col_base[i + 1]		)
							else:
										cpu_target.move_to(cpu_right_col_base[i - 5]		)
							ckpt_cpu_arr.append(lowerCamelCase		)
				self.add(*lowerCamelCase , *lowerCamelCase		)

				a__     = Square(side_length=2.2		)
				key.move_to([-5, 2, 0]		)

				a__     = MarkupText(
				    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=1_8 , )

				key_text.move_to([-5, 2.4, 0]		)

				self.add(lowerCamelCase , lowerCamelCase		)

				a__     = MarkupText(
				    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=1_8 , )

				blue_text.next_to(lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left()		)
				self.add(lowerCamelCase		)

				a__     = MarkupText(
				    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' , font_size=2_4 , )
				step_a.move_to([2, 2, 0]		)

				a__     = [meta_mem.copy() for i in range(6		)]
				a__     = [meta_mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("Disk" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				disk.move_to([-4.0, -1.25, 0]		)
				self.play(Write(lowerCamelCase , run_time=3		) , Write(lowerCamelCase , run_time=1		) , Create(lowerCamelCase , run_time=1		)		)

				a__     = []
				for i, rect in enumerate(lowerCamelCase		):
							a__     = rect.copy()
							target.generate_target()
							target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
							animations.append(MoveToTarget(lowerCamelCase , run_time=1.5		)		)
				self.play(*lowerCamelCase		)

				self.play(FadeOut(lowerCamelCase		)		)

				a__     = MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' , font_size=2_4		)
				step_a.move_to([2, 2, 0]		)

				self.play(Write(lowerCamelCase , run_time=3		)		)

				self.play(
				    FadeOut(lowerCamelCase , lowerCamelCase , *lowerCamelCase , *lowerCamelCase		) , )

				self.wait()



