
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


lowerCamelCase_						: List[str]         =  {
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 6_55_36,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 6_55_36,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 13_10_72,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
}







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Any:
				return torch.atana(lowerCamelCase     ,    lowerCamelCase						) / math.pi * 2







def        A__					(						lowerCamelCase						)  ->	List[str]:
				UpperCamelCase_:							int	=						torch.sin(t * math.pi / 2						) ** 2
				UpperCamelCase_:							Optional[Any]	=						(1 - sigma**2) ** 0.5
				return alpha_sigma_to_t(lowerCamelCase     ,    lowerCamelCase						)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			pass




class        _UpperCamelCase				(					nn.Module  ):






			'''simple docstring'''

			def __init__(       self :       Any						,	snake_case_ :       Any      ):
							super().__init__()

							UpperCamelCase_:							Dict	=						DiffusionAttnUnetaD(snake_case_						,	n_attn_layers=4      )
							UpperCamelCase_:							List[str]	=						deepcopy(self.diffusion      )
							UpperCamelCase_:							int	=						torch.quasirandom.SobolEngine(1						,	scramble=snake_case_      )







def        A__					(						lowerCamelCase						)  ->	int:
				UpperCamelCase_:							Union[str, Any]	=						MODELS_MAP[model_name]["""url"""]
				os.system(F'''wget {url} ./'''						)

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


lowerCamelCase_						: str         =  {
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
lowerCamelCase_						: List[Any]         =  {
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
lowerCamelCase_						: 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""",
}
lowerCamelCase_						: List[Any]         =  {
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

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

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







def        A__					(						lowerCamelCase						)  ->	str:
				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						)  ->	Dict:
				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						)  ->	Tuple:
				UpperCamelCase_:							Optional[Any]	=						input_string

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

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

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

				if string.startswith("""main."""						):
								UpperCamelCase_:							str	=						string[5:]

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

				if depth == max_depth:
								UpperCamelCase_:							Tuple	=						MID_NUM_TO_LAYER[layer_num]
								UpperCamelCase_:							Optional[int]	=						"""mid_block"""
				elif depth > 0 and int(lowerCamelCase						) < 7:
								UpperCamelCase_:							str	=						DOWN_NUM_TO_LAYER[layer_num]
								UpperCamelCase_:							Tuple	=						F'''down_blocks.{depth}'''
				elif depth > 0 and int(lowerCamelCase						) > 7:
								UpperCamelCase_:							Dict	=						UP_NUM_TO_LAYER[layer_num]
								UpperCamelCase_:							List[Any]	=						F'''up_blocks.{max_depth - depth - 1}'''
				elif depth == 0:
								UpperCamelCase_:							List[Any]	=						DEPTH_0_TO_LAYER[layer_num]
								UpperCamelCase_:							int	=						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}.'''						)

				UpperCamelCase_:							int	=						string_left[1:]

				if "resnets" in new_layer:
								UpperCamelCase_:							int	=						convert_resconv_naming(lowerCamelCase						)
				elif "attentions" in new_layer:
								UpperCamelCase_:							str	=						convert_attn_naming(lowerCamelCase						)
								UpperCamelCase_:							List[Any]	=						new_string_left

				if not isinstance(lowerCamelCase     ,    lowerCamelCase						):
								UpperCamelCase_:							int	=						prefix + """.""" + new_layer + """.""" + string_left
				else:
								UpperCamelCase_:							Tuple	=						[prefix + """.""" + new_layer + """.""" + s for s in string_left]
				return new_string







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

								UpperCamelCase_:							int	=						rename(lowerCamelCase						)

								# check if we need to transform from Conv => Linear for attention
								if isinstance(lowerCamelCase     ,    lowerCamelCase						):
												UpperCamelCase_:							int	=						transform_conv_attns(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)
								else:
												UpperCamelCase_:							Dict	=						v

				return new_state_dict







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				if len(lowerCamelCase						) == 1:
								if len(v.shape						) == 3:
												# weight
												UpperCamelCase_:							Any	=						v[:, :, 0]
								else:
												# bias
												UpperCamelCase_:							Any	=						v
				else:
								# qkv matrices
								UpperCamelCase_:							int	=						v.shape[0]
								UpperCamelCase_:							Optional[Any]	=						trippled_shape // 3
								for i in range(3						):
												if len(v.shape						) == 3:
																UpperCamelCase_:							List[Any]	=						v[i * single_shape : (i + 1) * single_shape, :, 0]
												else:
																UpperCamelCase_:							List[str]	=						v[i * single_shape : (i + 1) * single_shape]
				return new_state_dict







def        A__					(						lowerCamelCase						)  ->	Tuple:
				UpperCamelCase_:							Union[str, Any]	=						torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)

				UpperCamelCase_:							str	=						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()}'''
								UpperCamelCase_:							Any	=						download(lowerCamelCase						)

				UpperCamelCase_:							List[Any]	=						MODELS_MAP[model_name]["""sample_rate"""]
				UpperCamelCase_:							Union[str, Any]	=						MODELS_MAP[model_name]["""sample_size"""]

				UpperCamelCase_:							Union[str, Any]	=						Object()
				UpperCamelCase_:							str	=						sample_size
				UpperCamelCase_:							Optional[int]	=						sample_rate
				UpperCamelCase_:							int	=						0

				UpperCamelCase_:							int	=						UNetaDModel(sample_size=lowerCamelCase     ,    sample_rate=lowerCamelCase						)
				UpperCamelCase_:							Tuple	=						diffusers_model.state_dict()

				UpperCamelCase_:							Any	=						DiffusionUncond(lowerCamelCase						)
				orig_model.load_state_dict(torch.load(args.model_path     ,    map_location=lowerCamelCase						)["""state_dict"""]						)
				UpperCamelCase_:							Union[str, Any]	=						orig_model.diffusion_ema.eval()
				UpperCamelCase_:							Dict	=						orig_model.state_dict()
				UpperCamelCase_:							Union[str, Any]	=						rename_orig_weights(lowerCamelCase						)

				UpperCamelCase_:							int	=						set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
				UpperCamelCase_:							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":
												UpperCamelCase_:							Tuple	=						value.squeeze()

								UpperCamelCase_:							Optional[int]	=						value

				diffusers_model.load_state_dict(lowerCamelCase						)

				UpperCamelCase_:							str	=						1_00
				UpperCamelCase_:							Optional[Any]	=						33

				UpperCamelCase_:							List[str]	=						IPNDMScheduler(num_train_timesteps=lowerCamelCase						)

				UpperCamelCase_:							Union[str, Any]	=						torch.manual_seed(lowerCamelCase						)
				UpperCamelCase_:							List[str]	=						torch.randn([1, 2, config.sample_size]     ,    generator=lowerCamelCase						).to(lowerCamelCase						)

				UpperCamelCase_:							int	=						torch.linspace(1     ,    0     ,    steps + 1     ,    device=lowerCamelCase						)[:-1]
				UpperCamelCase_:							Dict	=						get_crash_schedule(lowerCamelCase						)

				UpperCamelCase_:							List[str]	=						DanceDiffusionPipeline(unet=lowerCamelCase     ,    scheduler=lowerCamelCase						)

				UpperCamelCase_:							str	=						torch.manual_seed(33						)
				UpperCamelCase_:							Any	=						pipe(num_inference_steps=lowerCamelCase     ,    generator=lowerCamelCase						).audios

				UpperCamelCase_:							str	=						sampling.iplms_sample(lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    {}						)
				UpperCamelCase_:							int	=						generated.clamp(-1     ,    1						)

				UpperCamelCase_:							str	=						(generated - audio).abs().sum()
				UpperCamelCase_:							Dict	=						(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__":
							lowerCamelCase_						: int         =  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.""")
							lowerCamelCase_						: Dict         =  parser.parse_args()

							main(args)








import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_						: Optional[int]         =  logging.get_logger("""transformers.models.speecht5""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				hf_model.apply_weight_norm()

				UpperCamelCase_:							Union[str, Any]	=						checkpoint["""input_conv.weight_g"""]
				UpperCamelCase_:							Optional[int]	=						checkpoint["""input_conv.weight_v"""]
				UpperCamelCase_:							List[Any]	=						checkpoint["""input_conv.bias"""]

				for i in range(len(config.upsample_rates						)						):
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.weight_g''']
								UpperCamelCase_:							Dict	=						checkpoint[F'''upsamples.{i}.1.weight_v''']
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.bias''']

				for i in range(len(config.upsample_rates						) * len(config.resblock_kernel_sizes						)						):
								for j in range(len(config.resblock_dilation_sizes						)						):
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

												UpperCamelCase_:							Union[str, Any]	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
												UpperCamelCase_:							int	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

				UpperCamelCase_:							int	=						checkpoint["""output_conv.1.weight_g"""]
				UpperCamelCase_:							Tuple	=						checkpoint["""output_conv.1.weight_v"""]
				UpperCamelCase_:							List[str]	=						checkpoint["""output_conv.1.bias"""]

				hf_model.remove_weight_norm()







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    )  ->	Optional[int]:
				if config_path is not None:
								UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase						)
				else:
								UpperCamelCase_:							str	=						SpeechTaHifiGanConfig()

				UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGan(lowerCamelCase						)

				UpperCamelCase_:							str	=						torch.load(lowerCamelCase						)
				load_weights(orig_checkpoint["""model"""]["""generator"""]     ,    lowerCamelCase     ,    lowerCamelCase						)

				UpperCamelCase_:							Union[str, Any]	=						np.load(lowerCamelCase						)
				UpperCamelCase_:							int	=						stats[0].reshape(-1						)
				UpperCamelCase_:							Union[str, Any]	=						stats[1].reshape(-1						)
				UpperCamelCase_:							Dict	=						torch.from_numpy(lowerCamelCase						).float()
				UpperCamelCase_:							Optional[Any]	=						torch.from_numpy(lowerCamelCase						).float()

				model.save_pretrained(lowerCamelCase						)

				if repo_id:
								print("""Pushing to the hub..."""						)
								model.push_to_hub(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Union[str, Any]         =  argparse.ArgumentParser()
							parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
							parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
							)

							lowerCamelCase_						: Optional[int]         =  parser.parse_args()
							convert_hifigan_checkpoint(
							    args.checkpoint_path,
							    args.stats_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)






