





'''simple docstring'''




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            =       {
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 65536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 65536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 131072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
}



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		return torch.atana(lowerCamelCase_			,			lowerCamelCase_	) / math.pi * 2



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



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




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					pass







class      UpperCamelCase__					(					nn.Module			):




					"""simple docstring"""


					def __init__(						self				:			int      ,						lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							super().__init__()

							SCREAMING_SNAKE_CASE :				int		=			DiffusionAttnUnetaD(lowerCamelCase_      ,						n_attn_layers=4						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			deepcopy(self.diffusion						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.quasirandom.SobolEngine(1      ,						scramble=lowerCamelCase_						)







def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



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

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


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

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

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



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		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 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		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 		__A		(  lowerCamelCase_			,			lowerCamelCase_=13	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				int		=			input_string

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

		SCREAMING_SNAKE_CASE :				List[Any]		=			0
		if string.startswith("""net.3."""	):
				depth += 1
				SCREAMING_SNAKE_CASE :				int		=			string[6:]
		elif string.startswith("""net."""	):
				SCREAMING_SNAKE_CASE :				List[str]		=			string[4:]

		while string.startswith("""main.7."""	):
				depth += 1
				SCREAMING_SNAKE_CASE :				Any		=			string[7:]

		if string.startswith("""main."""	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			string[5:]

		# mid block
		if string[:2].isdigit():
				SCREAMING_SNAKE_CASE :				str		=			string[:2]
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			string[2:]
		else:
				SCREAMING_SNAKE_CASE :				Dict		=			string[0]
				SCREAMING_SNAKE_CASE :				Any		=			string[1:]

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

		SCREAMING_SNAKE_CASE :				Dict		=			string_left[1:]

		if "resnets" in new_layer:
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			convert_resconv_naming(lowerCamelCase_	)
		elif "attentions" in new_layer:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			convert_attn_naming(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				List[str]		=			new_string_left

		if not isinstance(lowerCamelCase_			,			lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				str		=			prefix + """.""" + new_layer + """.""" + string_left
		else:
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			[prefix + """.""" + new_layer + """.""" + s for s in string_left]
		return new_string



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			{}
		for k, v in state_dict.items():
				if k.endswith("""kernel"""	):
						# up- and downsample layers, don't have trainable weights
						continue

				SCREAMING_SNAKE_CASE :				Optional[int]		=			rename(lowerCamelCase_	)

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

		return new_state_dict



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



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



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Tuple		=			torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""	)

		SCREAMING_SNAKE_CASE :				Union[str, 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()}'''
				SCREAMING_SNAKE_CASE :				Optional[int]		=			download(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				List[str]		=			MODELS_MAP[model_name]["""sample_rate"""]
		SCREAMING_SNAKE_CASE :				str		=			MODELS_MAP[model_name]["""sample_size"""]

		SCREAMING_SNAKE_CASE :				Tuple		=			Object()
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			sample_size
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			sample_rate
		SCREAMING_SNAKE_CASE :				Dict		=			0

		SCREAMING_SNAKE_CASE :				Tuple		=			UNetaDModel(sample_size=lowerCamelCase_			,			sample_rate=lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[int]		=			diffusers_model.state_dict()

		SCREAMING_SNAKE_CASE :				int		=			DiffusionUncond(lowerCamelCase_	)
		orig_model.load_state_dict(torch.load(args.model_path			,			map_location=lowerCamelCase_	)["""state_dict"""]	)
		SCREAMING_SNAKE_CASE :				List[str]		=			orig_model.diffusion_ema.eval()
		SCREAMING_SNAKE_CASE :				List[str]		=			orig_model.state_dict()
		SCREAMING_SNAKE_CASE :				Any		=			rename_orig_weights(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				List[str]		=			set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
		SCREAMING_SNAKE_CASE :				List[Any]		=			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":
						SCREAMING_SNAKE_CASE :				List[str]		=			value.squeeze()

				SCREAMING_SNAKE_CASE :				List[Any]		=			value

		diffusers_model.load_state_dict(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				List[Any]		=			1_00
		SCREAMING_SNAKE_CASE :				Any		=			33

		SCREAMING_SNAKE_CASE :				Any		=			IPNDMScheduler(num_train_timesteps=lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.randn([1, 2, config.sample_size]			,			generator=lowerCamelCase_	).to(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.linspace(1			,			0			,			steps + 1			,			device=lowerCamelCase_	)[:-1]
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			get_crash_schedule(lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				str		=			DanceDiffusionPipeline(unet=lowerCamelCase_			,			scheduler=lowerCamelCase_	)

		SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.manual_seed(33	)
		SCREAMING_SNAKE_CASE :				int		=			pipe(num_inference_steps=lowerCamelCase_			,			generator=lowerCamelCase_	).audios

		SCREAMING_SNAKE_CASE :				List[str]		=			sampling.iplms_sample(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_			,			{}	)
		SCREAMING_SNAKE_CASE :				Any		=			generated.clamp(-1			,			1	)

		SCREAMING_SNAKE_CASE :				str		=			(generated - audio).abs().sum()
		SCREAMING_SNAKE_CASE :				Tuple		=			(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            =       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            =       parser.parse_args()

							main(args)










'''simple docstring'''




import random



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			num - 1
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			0

		while s % 2 == 0:
				SCREAMING_SNAKE_CASE :				List[str]		=			s // 2
				t += 1

		for _ in range(5	):
				SCREAMING_SNAKE_CASE :				List[Any]		=			random.randrange(2			,			num - 1	)
				SCREAMING_SNAKE_CASE :				List[Any]		=			pow(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)
				if v != 1:
						SCREAMING_SNAKE_CASE :				Tuple		=			0
						while v != (num - 1):
								if i == t - 1:
										return False
								else:
										SCREAMING_SNAKE_CASE :				Any		=			i + 1
										SCREAMING_SNAKE_CASE :				str		=			(v**2) % num
		return True



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if num < 2:
				return False

		SCREAMING_SNAKE_CASE :				List[str]		=			[
		    2,
		    3,
		    5,
		    7,
		    11,
		    13,
		    17,
		    19,
		    23,
		    29,
		    31,
		    37,
		    41,
		    43,
		    47,
		    53,
		    59,
		    61,
		    67,
		    71,
		    73,
		    79,
		    83,
		    89,
		    97,
		    1_01,
		    1_03,
		    1_07,
		    1_09,
		    1_13,
		    1_27,
		    1_31,
		    1_37,
		    1_39,
		    1_49,
		    1_51,
		    1_57,
		    1_63,
		    1_67,
		    1_73,
		    1_79,
		    1_81,
		    1_91,
		    1_93,
		    1_97,
		    1_99,
		    2_11,
		    2_23,
		    2_27,
		    2_29,
		    2_33,
		    2_39,
		    2_41,
		    2_51,
		    2_57,
		    2_63,
		    2_69,
		    2_71,
		    2_77,
		    2_81,
		    2_83,
		    2_93,
		    3_07,
		    3_11,
		    3_13,
		    3_17,
		    3_31,
		    3_37,
		    3_47,
		    3_49,
		    3_53,
		    3_59,
		    3_67,
		    3_73,
		    3_79,
		    3_83,
		    3_89,
		    3_97,
		    4_01,
		    4_09,
		    4_19,
		    4_21,
		    4_31,
		    4_33,
		    4_39,
		    4_43,
		    4_49,
		    4_57,
		    4_61,
		    4_63,
		    4_67,
		    4_79,
		    4_87,
		    4_91,
		    4_99,
		    5_03,
		    5_09,
		    5_21,
		    5_23,
		    5_41,
		    5_47,
		    5_57,
		    5_63,
		    5_69,
		    5_71,
		    5_77,
		    5_87,
		    5_93,
		    5_99,
		    6_01,
		    6_07,
		    6_13,
		    6_17,
		    6_19,
		    6_31,
		    6_41,
		    6_43,
		    6_47,
		    6_53,
		    6_59,
		    6_61,
		    6_73,
		    6_77,
		    6_83,
		    6_91,
		    7_01,
		    7_09,
		    7_19,
		    7_27,
		    7_33,
		    7_39,
		    7_43,
		    7_51,
		    7_57,
		    7_61,
		    7_69,
		    7_73,
		    7_87,
		    7_97,
		    8_09,
		    8_11,
		    8_21,
		    8_23,
		    8_27,
		    8_29,
		    8_39,
		    8_53,
		    8_57,
		    8_59,
		    8_63,
		    8_77,
		    8_81,
		    8_83,
		    8_87,
		    9_07,
		    9_11,
		    9_19,
		    9_29,
		    9_37,
		    9_41,
		    9_47,
		    9_53,
		    9_67,
		    9_71,
		    9_77,
		    9_83,
		    9_91,
		    9_97,
		]

		if num in low_primes:
				return True

		for prime in low_primes:
				if (num % prime) == 0:
						return False

		return rabin_miller(lowerCamelCase_	)



def 		__A		(  lowerCamelCase_ = 10_24	):


		"""simple docstring"""



		while True:
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			random.randrange(2 ** (keysize - 1)			,			2 ** (keysize)	)
				if is_prime_low_num(lowerCamelCase_	):
						return num


if __name__ == "__main__":
							__UpperCAmelCase            =       generate_large_prime()
							print(("""Prime number:""", num))
							print(("""is_prime_low_num:""", is_prime_low_num(num)))



