


import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_UpperCAmelCase			:  int     ={
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 6_5536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 6_5536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 13_1072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
}



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ):
				return torch.atana(lowerCAmelCase_ ,   lowerCAmelCase_ ) / math.pi * 2



def 			lowerCAmelCase (		lowerCAmelCase_ ):
				lowerCAmelCase_ :      List[Any]    				=				torch.sin(t * math.pi / 2 ) ** 2
				lowerCAmelCase_ :      str    				=				(1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(lowerCAmelCase_ ,   lowerCAmelCase_ )







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						pass







class 				snake_case__(							nn.Module							):







						'''simple docstring'''




						def __init__(				self   ,						__lowercase							)					->    Union[str, Any]:
										super().__init__()

										lowerCAmelCase_ :      Tuple    				=				DiffusionAttnUnetaD(__lowercase   ,						n_attn_layers=4							)
										lowerCAmelCase_ :      Tuple    				=				deepcopy(self.diffusion							)
										lowerCAmelCase_ :      List[str]    				=				torch.quasirandom.SobolEngine(1   ,						scramble=__lowercase							)






def 			lowerCAmelCase (		lowerCAmelCase_ ):
				lowerCAmelCase_ :      Dict    				=				MODELS_MAP[model_name]['''url''']
				os.system(f"""wget {url} ./""" )

				return f"""./{model_name}.ckpt"""


_UpperCAmelCase			:  Dict     ={
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
_UpperCAmelCase			:  int     ={
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
_UpperCAmelCase			:  Tuple     ={
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
    """8""": """resnets.3""",
    """9""": """attentions.3""",
    """10""": """resnets.4""",
    """11""": """attentions.4""",
    """12""": """resnets.5""",
    """13""": """attentions.5""",
}
_UpperCAmelCase			:  Dict     ={
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

_UpperCAmelCase			:  Tuple     ={
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

_UpperCAmelCase			:  Optional[int]     ={
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}



def 			lowerCAmelCase (		lowerCAmelCase_ ):
				if name.startswith('''skip''' ):
								return name.replace('''skip''' ,   RES_CONV_MAP['''skip'''] )

				# name has to be of format main.{digit}
				if not name.startswith('''main.''' ):
								raise ValueError(f"""ResConvBlock error with {name}""" )

				return name.replace(name[:6] ,   RES_CONV_MAP[name[:6]] )



def 			lowerCAmelCase (		lowerCAmelCase_ ):
				for key, value in ATTN_MAP.items():
								if name.startswith(lowerCAmelCase_ ) and not isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ ):
												return name.replace(lowerCAmelCase_ ,   lowerCAmelCase_ )
								elif name.startswith(lowerCAmelCase_ ):
												return [name.replace(lowerCAmelCase_ ,   lowerCAmelCase_ ) for v in value]
				raise ValueError(f"""Attn error with {name}""" )



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_=13 ):
				lowerCAmelCase_ :      Dict    				=				input_string

				if string.split('''.''' )[0] == "timestep_embed":
								return string.replace('''timestep_embed''' ,   '''time_proj''' )

				lowerCAmelCase_ :      List[Any]    				=				0
				if string.startswith('''net.3.''' ):
								depth += 1
								lowerCAmelCase_ :      Optional[Any]    				=				string[6:]
				elif string.startswith('''net.''' ):
								lowerCAmelCase_ :      str    				=				string[4:]

				while string.startswith('''main.7.''' ):
								depth += 1
								lowerCAmelCase_ :      Dict    				=				string[7:]

				if string.startswith('''main.''' ):
								lowerCAmelCase_ :      Any    				=				string[5:]

				# mid block
				if string[:2].isdigit():
								lowerCAmelCase_ :      Optional[int]    				=				string[:2]
								lowerCAmelCase_ :      Union[str, Any]    				=				string[2:]
				else:
								lowerCAmelCase_ :      Any    				=				string[0]
								lowerCAmelCase_ :      List[str]    				=				string[1:]

				if depth == max_depth:
								lowerCAmelCase_ :      List[Any]    				=				MID_NUM_TO_LAYER[layer_num]
								lowerCAmelCase_ :      int    				=				'''mid_block'''
				elif depth > 0 and int(lowerCAmelCase_ ) < 7:
								lowerCAmelCase_ :      Dict    				=				DOWN_NUM_TO_LAYER[layer_num]
								lowerCAmelCase_ :      int    				=				f"""down_blocks.{depth}"""
				elif depth > 0 and int(lowerCAmelCase_ ) > 7:
								lowerCAmelCase_ :      List[str]    				=				UP_NUM_TO_LAYER[layer_num]
								lowerCAmelCase_ :      Union[str, Any]    				=				f"""up_blocks.{max_depth - depth - 1}"""
				elif depth == 0:
								lowerCAmelCase_ :      str    				=				DEPTH_0_TO_LAYER[layer_num]
								lowerCAmelCase_ :      str    				=				f"""up_blocks.{max_depth - 1}""" if int(lowerCAmelCase_ ) > 3 else '''down_blocks.0'''

				if not string_left.startswith('''.''' ):
								raise ValueError(f"""Naming error with {input_string} and string_left: {string_left}.""" )

				lowerCAmelCase_ :      Tuple    				=				string_left[1:]

				if "resnets" in new_layer:
								lowerCAmelCase_ :      Tuple    				=				convert_resconv_naming(lowerCAmelCase_ )
				elif "attentions" in new_layer:
								lowerCAmelCase_ :      int    				=				convert_attn_naming(lowerCAmelCase_ )
								lowerCAmelCase_ :      int    				=				new_string_left

				if not isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								lowerCAmelCase_ :      Optional[int]    				=				prefix + '''.''' + new_layer + '''.''' + string_left
				else:
								lowerCAmelCase_ :      str    				=				[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
				return new_string



def 			lowerCAmelCase (		lowerCAmelCase_ ):
				lowerCAmelCase_ :      Optional[int]    				=				{}
				for k, v in state_dict.items():
								if k.endswith('''kernel''' ):
												# up- and downsample layers, don't have trainable weights
												continue

								lowerCAmelCase_ :      Any    				=				rename(lowerCAmelCase_ )

								# check if we need to transform from Conv => Linear for attention
								if isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ ):
												lowerCAmelCase_ :      Optional[Any]    				=				transform_conv_attns(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								else:
												lowerCAmelCase_ :      List[str]    				=				v

				return new_state_dict



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ):
				if len(lowerCAmelCase_ ) == 1:
								if len(v.shape ) == 3:
												# weight
												lowerCAmelCase_ :      Any    				=				v[:, :, 0]
								else:
												# bias
												lowerCAmelCase_ :      Union[str, Any]    				=				v
				else:
								# qkv matrices
								lowerCAmelCase_ :      Dict    				=				v.shape[0]
								lowerCAmelCase_ :      List[str]    				=				trippled_shape // 3
								for i in range(3 ):
												if len(v.shape ) == 3:
																lowerCAmelCase_ :      Any    				=				v[i * single_shape : (i + 1) * single_shape, :, 0]
												else:
																lowerCAmelCase_ :      List[str]    				=				v[i * single_shape : (i + 1) * single_shape]
				return new_state_dict



def 			lowerCAmelCase (		lowerCAmelCase_ ):
				lowerCAmelCase_ :      Dict    				=				torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )

				lowerCAmelCase_ :      List[Any]    				=				args.model_path.split('''/''' )[-1].split('''.''' )[0]
				if not os.path.isfile(args.model_path ):
								assert (
								    model_name == args.model_path
								), f"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
								lowerCAmelCase_ :      Optional[int]    				=				download(lowerCAmelCase_ )

				lowerCAmelCase_ :      Any    				=				MODELS_MAP[model_name]['''sample_rate''']
				lowerCAmelCase_ :      Union[str, Any]    				=				MODELS_MAP[model_name]['''sample_size''']

				lowerCAmelCase_ :      Dict    				=				Object()
				lowerCAmelCase_ :      List[Any]    				=				sample_size
				lowerCAmelCase_ :      Any    				=				sample_rate
				lowerCAmelCase_ :      str    				=				0

				lowerCAmelCase_ :      Optional[Any]    				=				UNetaDModel(sample_size=lowerCAmelCase_ ,   sample_rate=lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[int]    				=				diffusers_model.state_dict()

				lowerCAmelCase_ :      Optional[Any]    				=				DiffusionUncond(lowerCAmelCase_ )
				orig_model.load_state_dict(torch.load(args.model_path ,   map_location=lowerCAmelCase_ )['''state_dict'''] )
				lowerCAmelCase_ :      Optional[Any]    				=				orig_model.diffusion_ema.eval()
				lowerCAmelCase_ :      Tuple    				=				orig_model.state_dict()
				lowerCAmelCase_ :      Tuple    				=				rename_orig_weights(lowerCAmelCase_ )

				lowerCAmelCase_ :      List[Any]    				=				set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
				lowerCAmelCase_ :      str    				=				set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )

				assert len(lowerCAmelCase_ ) == 0, f"""Problem with {renamed_minus_diffusers}"""
				assert all(k.endswith('''kernel''' ) for k in list(lowerCAmelCase_ ) ), f"""Problem with {diffusers_minus_renamed}"""

				for key, value in renamed_state_dict.items():
								assert (
								    diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
								), f"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
								if key == "time_proj.weight":
												lowerCAmelCase_ :      Tuple    				=				value.squeeze()

								lowerCAmelCase_ :      int    				=				value

				diffusers_model.load_state_dict(lowerCAmelCase_ )

				lowerCAmelCase_ :      str    				=				100
				lowerCAmelCase_ :      Optional[Any]    				=				33

				lowerCAmelCase_ :      List[str]    				=				IPNDMScheduler(num_train_timesteps=lowerCAmelCase_ )

				lowerCAmelCase_ :      Optional[int]    				=				torch.manual_seed(lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[Any]    				=				torch.randn([1, 2, config.sample_size] ,   generator=lowerCAmelCase_ ).to(lowerCAmelCase_ )

				lowerCAmelCase_ :      str    				=				torch.linspace(1 ,   0 ,   steps + 1 ,   device=lowerCAmelCase_ )[:-1]
				lowerCAmelCase_ :      List[Any]    				=				get_crash_schedule(lowerCAmelCase_ )

				lowerCAmelCase_ :      Optional[int]    				=				DanceDiffusionPipeline(unet=lowerCAmelCase_ ,   scheduler=lowerCAmelCase_ )

				lowerCAmelCase_ :      Tuple    				=				torch.manual_seed(33 )
				lowerCAmelCase_ :      List[Any]    				=				pipe(num_inference_steps=lowerCAmelCase_ ,   generator=lowerCAmelCase_ ).audios

				lowerCAmelCase_ :      List[str]    				=				sampling.iplms_sample(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   {} )
				lowerCAmelCase_ :      Optional[int]    				=				generated.clamp(-1 ,   1 )

				lowerCAmelCase_ :      Tuple    				=				(generated - audio).abs().sum()
				lowerCAmelCase_ :      Any    				=				(generated - audio).abs().max()

				if args.save:
								pipe.save_pretrained(args.checkpoint_path )

				print('''Diff sum''' ,   lowerCAmelCase_ )
				print('''Diff max''' ,   lowerCAmelCase_ )

				assert diff_max < 1e-3, f"""Diff max: {diff_max} is too much :-/"""

				print(f"""Conversion for {model_name} successful!""" )


if __name__ == "__main__":
						_UpperCAmelCase			:  List[str]     =argparse.ArgumentParser()

						parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
						parser.add_argument(
						    """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
						)
						parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
						_UpperCAmelCase			:  Dict     =parser.parse_args()

						main(args)



from manim import *







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      Dict    				=				Rectangle(height=0.5   ,						width=0.5							)
										lowerCAmelCase_ :      Tuple    				=				Rectangle(height=0.25   ,						width=0.25							)
										lowerCAmelCase_ :      Tuple    				=				Rectangle(height=0.46   ,						width=0.46							).set_stroke(width=0							)

										lowerCAmelCase_ :      Optional[int]    				=				[mem.copy() for i in range(6							)]
										lowerCAmelCase_ :      int    				=				[mem.copy() for i in range(6							)]
										lowerCAmelCase_ :      Optional[int]    				=				VGroup(*__lowercase							).arrange(__lowercase   ,						buff=0							)
										lowerCAmelCase_ :      List[str]    				=				VGroup(*__lowercase							).arrange(__lowercase   ,						buff=0							)
										lowerCAmelCase_ :      int    				=				VGroup(__lowercase   ,						__lowercase							).arrange(__lowercase   ,						buff=0							)
										lowerCAmelCase_ :      Tuple    				=				Text('''CPU'''   ,						font_size=2_4							)
										lowerCAmelCase_ :      Union[str, Any]    				=				Group(__lowercase   ,						__lowercase							).arrange(__lowercase   ,						buff=0.5   ,						aligned_edge=__lowercase							)
										cpu.move_to([-2.5, -0.5, 0]							)
										self.add(__lowercase							)

										lowerCAmelCase_ :      List[str]    				=				[mem.copy() for i in range(4							)]
										lowerCAmelCase_ :      Any    				=				VGroup(*__lowercase							).arrange(__lowercase   ,						buff=0							)
										lowerCAmelCase_ :      List[Any]    				=				Text('''GPU'''   ,						font_size=2_4							)
										lowerCAmelCase_ :      int    				=				Group(__lowercase   ,						__lowercase							).arrange(__lowercase   ,						buff=0.5   ,						aligned_edge=__lowercase							)
										gpu.move_to([-1, -1, 0]							)
										self.add(__lowercase							)

										lowerCAmelCase_ :      str    				=				[mem.copy() for i in range(6							)]
										lowerCAmelCase_ :      Dict    				=				VGroup(*__lowercase							).arrange(__lowercase   ,						buff=0							)

										lowerCAmelCase_ :      Dict    				=				Text('''Model'''   ,						font_size=2_4							)
										lowerCAmelCase_ :      str    				=				Group(__lowercase   ,						__lowercase							).arrange(__lowercase   ,						buff=0.5   ,						aligned_edge=__lowercase							)
										model.move_to([3, -1.0, 0]							)
										self.add(__lowercase							)

										lowerCAmelCase_ :      int    				=				[]
										lowerCAmelCase_ :      int    				=				[]
										lowerCAmelCase_ :      Dict    				=				[]

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

														lowerCAmelCase_ :      Any    				=				Rectangle(height=0.46 / 4   ,						width=0.46 / 3							).set_stroke(width=0.0							).set_fill(__lowercase   ,						opacity=0.7							)

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

										self.add(*__lowercase   ,						*__lowercase   ,						*__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				[mem.copy() for i in range(6							)]
										lowerCAmelCase_ :      List[str]    				=				VGroup(*__lowercase							).arrange(__lowercase   ,						buff=0							)

										lowerCAmelCase_ :      Union[str, Any]    				=				Text('''Loaded Checkpoint'''   ,						font_size=2_4							)
										lowerCAmelCase_ :      int    				=				Group(__lowercase   ,						__lowercase							).arrange(__lowercase   ,						buff=0.5   ,						aligned_edge=__lowercase							)
										checkpoint.move_to([3, 0.5, 0]							)

										self.add(__lowercase							)

										lowerCAmelCase_ :      Optional[Any]    				=				[]
										lowerCAmelCase_ :      Dict    				=				[]

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

														lowerCAmelCase_ :      Union[str, Any]    				=				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(__lowercase							)
										self.add(*__lowercase   ,						*__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				Square(side_length=2.2							)
										key.move_to([-5, 2, 0]							)

										lowerCAmelCase_ :      str    				=				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(__lowercase   ,						__lowercase							)

										lowerCAmelCase_ :      str    				=				MarkupText(
										    f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""   ,						font_size=1_8   ,						)

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

										lowerCAmelCase_ :      str    				=				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]							)

										lowerCAmelCase_ :      List[Any]    				=				[meta_mem.copy() for i in range(6							)]
										lowerCAmelCase_ :      Any    				=				[meta_mem.copy() for i in range(6							)]
										lowerCAmelCase_ :      Any    				=				VGroup(*__lowercase							).arrange(__lowercase   ,						buff=0							)
										lowerCAmelCase_ :      Union[str, Any]    				=				VGroup(*__lowercase							).arrange(__lowercase   ,						buff=0							)
										lowerCAmelCase_ :      int    				=				VGroup(__lowercase   ,						__lowercase							).arrange(__lowercase   ,						buff=0							)
										lowerCAmelCase_ :      List[str]    				=				Text('''Disk'''   ,						font_size=2_4							)
										lowerCAmelCase_ :      Optional[int]    				=				Group(__lowercase   ,						__lowercase							).arrange(__lowercase   ,						buff=0.5   ,						aligned_edge=__lowercase							)
										disk.move_to([-4.0, -1.25, 0]							)
										self.play(Write(__lowercase   ,						run_time=3							)   ,						Write(__lowercase   ,						run_time=1							)   ,						Create(__lowercase   ,						run_time=1							)							)

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

										self.play(FadeOut(__lowercase							)							)

										lowerCAmelCase_ :      Union[str, Any]    				=				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(__lowercase   ,						run_time=3							)							)

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

										self.wait()