






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


lowercase    				=   {
    "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 		__UpperCAmelCase     (	a_				,	a_):
							return torch.atana(a_				,	a_) / math.pi * 2





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				torch.sin(t * math.pi / 2) ** 2
							snake_case_           =				(1 - sigma**2) ** 0.5
							return alpha_sigma_to_t(a_				,	a_)




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






class 					UpperCamelCase_ (     nn.Module							):



	'''simple docstring'''




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

								snake_case_           =				DiffusionAttnUnetaD(a			,     n_attn_layers=4   )
								snake_case_           =				deepcopy(self.diffusion   )
								snake_case_           =				torch.quasirandom.SobolEngine(1			,     scramble=a   )






def 		__UpperCAmelCase     (	a_):
							snake_case_           =				MODELS_MAP[model_name]['url']
							os.system(f'''wget {url} ./''')

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


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

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

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





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





def 		__UpperCAmelCase     (	a_				,	a_=13):
							snake_case_           =				input_string

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

							snake_case_           =				0
							if string.startswith('net.3.'):
														depth += 1
														snake_case_           =				string[6:]
							elif string.startswith('net.'):
														snake_case_           =				string[4:]

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

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

							# mid block
							if string[:2].isdigit():
														snake_case_           =				string[:2]
														snake_case_           =				string[2:]
							else:
														snake_case_           =				string[0]
														snake_case_           =				string[1:]

							if depth == max_depth:
														snake_case_           =				MID_NUM_TO_LAYER[layer_num]
														snake_case_           =				'mid_block'
							elif depth > 0 and int(a_) < 7:
														snake_case_           =				DOWN_NUM_TO_LAYER[layer_num]
														snake_case_           =				f'''down_blocks.{depth}'''
							elif depth > 0 and int(a_) > 7:
														snake_case_           =				UP_NUM_TO_LAYER[layer_num]
														snake_case_           =				f'''up_blocks.{max_depth - depth - 1}'''
							elif depth == 0:
														snake_case_           =				DEPTH_0_TO_LAYER[layer_num]
														snake_case_           =				f'''up_blocks.{max_depth - 1}''' if int(a_) > 3 else 'down_blocks.0'

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

							snake_case_           =				string_left[1:]

							if "resnets" in new_layer:
														snake_case_           =				convert_resconv_naming(a_)
							elif "attentions" in new_layer:
														snake_case_           =				convert_attn_naming(a_)
														snake_case_           =				new_string_left

							if not isinstance(a_				,	a_):
														snake_case_           =				prefix + '.' + new_layer + '.' + string_left
							else:
														snake_case_           =				[prefix + '.' + new_layer + '.' + s for s in string_left]
							return new_string





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

														snake_case_           =				rename(a_)

														# check if we need to transform from Conv => Linear for attention
														if isinstance(a_				,	a_):
																					snake_case_           =				transform_conv_attns(a_				,	a_				,	a_)
														else:
																					snake_case_           =				v

							return new_state_dict





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							if len(a_) == 1:
														if len(v.shape) == 3:
																					# weight
																					snake_case_           =				v[:, :, 0]
														else:
																					# bias
																					snake_case_           =				v
							else:
														# qkv matrices
														snake_case_           =				v.shape[0]
														snake_case_           =				trippled_shape // 3
														for i in range(3):
																					if len(v.shape) == 3:
																												snake_case_           =				v[i * single_shape : (i + 1) * single_shape, :, 0]
																					else:
																												snake_case_           =				v[i * single_shape : (i + 1) * single_shape]
							return new_state_dict





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				torch.device('cuda' if torch.cuda.is_available() else 'cpu')

							snake_case_           =				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()}'''
														snake_case_           =				download(a_)

							snake_case_           =				MODELS_MAP[model_name]['sample_rate']
							snake_case_           =				MODELS_MAP[model_name]['sample_size']

							snake_case_           =				Object()
							snake_case_           =				sample_size
							snake_case_           =				sample_rate
							snake_case_           =				0

							snake_case_           =				UNetaDModel(sample_size=a_				,	sample_rate=a_)
							snake_case_           =				diffusers_model.state_dict()

							snake_case_           =				DiffusionUncond(a_)
							orig_model.load_state_dict(torch.load(args.model_path				,	map_location=a_)['state_dict'])
							snake_case_           =				orig_model.diffusion_ema.eval()
							snake_case_           =				orig_model.state_dict()
							snake_case_           =				rename_orig_weights(a_)

							snake_case_           =				set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys())
							snake_case_           =				set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys())

							assert len(a_) == 0, f'''Problem with {renamed_minus_diffusers}'''
							assert all(k.endswith('kernel') for k in list(a_)), 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":
																					snake_case_           =				value.squeeze()

														snake_case_           =				value

							diffusers_model.load_state_dict(a_)

							snake_case_           =				1_00
							snake_case_           =				33

							snake_case_           =				IPNDMScheduler(num_train_timesteps=a_)

							snake_case_           =				torch.manual_seed(a_)
							snake_case_           =				torch.randn([1, 2, config.sample_size]				,	generator=a_).to(a_)

							snake_case_           =				torch.linspace(1				,	0				,	steps + 1				,	device=a_)[:-1]
							snake_case_           =				get_crash_schedule(a_)

							snake_case_           =				DanceDiffusionPipeline(unet=a_				,	scheduler=a_)

							snake_case_           =				torch.manual_seed(33)
							snake_case_           =				pipe(num_inference_steps=a_				,	generator=a_).audios

							snake_case_           =				sampling.iplms_sample(a_				,	a_				,	a_				,	{})
							snake_case_           =				generated.clamp(-1				,	1)

							snake_case_           =				(generated - audio).abs().sum()
							snake_case_           =				(generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
	lowercase    				=   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.")
	lowercase    				=   parser.parse_args()

	main(args)













from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


lowercase    				=   "CompVis/stable-diffusion-v1-1"
lowercase    				=   "CompVis/stable-diffusion-v1-2"
lowercase    				=   "CompVis/stable-diffusion-v1-3"
lowercase    				=   "CompVis/stable-diffusion-v1-4"




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a			,     a			,     a			,     a			,     a			,     a			,     a = True			,     )			->       int:
								super()._init_()

								snake_case_           =				StableDiffusionPipeline.from_pretrained(a   )
								snake_case_           =				StableDiffusionPipeline.from_pretrained(a   )
								snake_case_           =				StableDiffusionPipeline.from_pretrained(a   )
								snake_case_           =				StableDiffusionPipeline(
								    vae=a			,     text_encoder=a			,     tokenizer=a			,     unet=a			,     scheduler=a			,     safety_checker=a			,     feature_extractor=a			,     requires_safety_checker=a			,     )

								self.register_modules(pipelinea=self.pipea			,     pipelinea=self.pipea			,     pipelinea=self.pipea			,     pipelinea=self.pipea   )




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, Any]:
								return {k: getattr(self			,     a   ) for k in self.config.keys() if not k.startswith('_'   )}




	def 				_UpperCamelCase     (			self			,     a = "auto"   )			->       Dict:
								if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															snake_case_           =				self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(a   )




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								self.enable_attention_slicing(a   )




	@torch.no_grad()
	def 				_UpperCamelCase     (			self			,     a			,     a = 5_12			,     a = 5_12			,     a = 50			,     a = 7.5			,     a = None			,     a = 1			,     a = 0.0			,     a = None			,     a = None			,     a = "pil"			,     a = True			,     a = None			,     a = 1			,     **a			,     )			->       Optional[Any]:
								return self.pipea(
								    prompt=a			,     height=a			,     width=a			,     num_inference_steps=a			,     guidance_scale=a			,     negative_prompt=a			,     num_images_per_prompt=a			,     eta=a			,     generator=a			,     latents=a			,     output_type=a			,     return_dict=a			,     callback=a			,     callback_steps=a			,     **a			,     )




	@torch.no_grad()
	def 				_UpperCamelCase     (			self			,     a			,     a = 5_12			,     a = 5_12			,     a = 50			,     a = 7.5			,     a = None			,     a = 1			,     a = 0.0			,     a = None			,     a = None			,     a = "pil"			,     a = True			,     a = None			,     a = 1			,     **a			,     )			->       int:
								return self.pipea(
								    prompt=a			,     height=a			,     width=a			,     num_inference_steps=a			,     guidance_scale=a			,     negative_prompt=a			,     num_images_per_prompt=a			,     eta=a			,     generator=a			,     latents=a			,     output_type=a			,     return_dict=a			,     callback=a			,     callback_steps=a			,     **a			,     )




	@torch.no_grad()
	def 				_UpperCamelCase     (			self			,     a			,     a = 5_12			,     a = 5_12			,     a = 50			,     a = 7.5			,     a = None			,     a = 1			,     a = 0.0			,     a = None			,     a = None			,     a = "pil"			,     a = True			,     a = None			,     a = 1			,     **a			,     )			->       Optional[int]:
								return self.pipea(
								    prompt=a			,     height=a			,     width=a			,     num_inference_steps=a			,     guidance_scale=a			,     negative_prompt=a			,     num_images_per_prompt=a			,     eta=a			,     generator=a			,     latents=a			,     output_type=a			,     return_dict=a			,     callback=a			,     callback_steps=a			,     **a			,     )




	@torch.no_grad()
	def 				_UpperCamelCase     (			self			,     a			,     a = 5_12			,     a = 5_12			,     a = 50			,     a = 7.5			,     a = None			,     a = 1			,     a = 0.0			,     a = None			,     a = None			,     a = "pil"			,     a = True			,     a = None			,     a = 1			,     **a			,     )			->       Any:
								return self.pipea(
								    prompt=a			,     height=a			,     width=a			,     num_inference_steps=a			,     guidance_scale=a			,     negative_prompt=a			,     num_images_per_prompt=a			,     eta=a			,     generator=a			,     latents=a			,     output_type=a			,     return_dict=a			,     callback=a			,     callback_steps=a			,     **a			,     )






	@torch.no_grad()
	def 				_UpperCamelCase     (			self			,     a			,     a = 5_12			,     a = 5_12			,     a = 50			,     a = 7.5			,     a = None			,     a = 1			,     a = 0.0			,     a = None			,     a = None			,     a = "pil"			,     a = True			,     a = None			,     a = 1			,     **a			,     )			->       Optional[Any]:
								snake_case_           =				'cuda' if torch.cuda.is_available() else 'cpu'
								self.to(a   )

								# Checks if the height and width are divisible by 8 or not
								if height % 8 != 0 or width % 8 != 0:
															raise ValueError(F'''`height` and `width` must be divisible by 8 but are {height} and {width}.'''   )

								# Get first result from Stable Diffusion Checkpoint v1.1
								snake_case_           =				self.textaimg_sda_a(
								    prompt=a			,     height=a			,     width=a			,     num_inference_steps=a			,     guidance_scale=a			,     negative_prompt=a			,     num_images_per_prompt=a			,     eta=a			,     generator=a			,     latents=a			,     output_type=a			,     return_dict=a			,     callback=a			,     callback_steps=a			,     **a			,     )

								# Get first result from Stable Diffusion Checkpoint v1.2
								snake_case_           =				self.textaimg_sda_a(
								    prompt=a			,     height=a			,     width=a			,     num_inference_steps=a			,     guidance_scale=a			,     negative_prompt=a			,     num_images_per_prompt=a			,     eta=a			,     generator=a			,     latents=a			,     output_type=a			,     return_dict=a			,     callback=a			,     callback_steps=a			,     **a			,     )

								# Get first result from Stable Diffusion Checkpoint v1.3
								snake_case_           =				self.textaimg_sda_a(
								    prompt=a			,     height=a			,     width=a			,     num_inference_steps=a			,     guidance_scale=a			,     negative_prompt=a			,     num_images_per_prompt=a			,     eta=a			,     generator=a			,     latents=a			,     output_type=a			,     return_dict=a			,     callback=a			,     callback_steps=a			,     **a			,     )

								# Get first result from Stable Diffusion Checkpoint v1.4
								snake_case_           =				self.textaimg_sda_a(
								    prompt=a			,     height=a			,     width=a			,     num_inference_steps=a			,     guidance_scale=a			,     negative_prompt=a			,     num_images_per_prompt=a			,     eta=a			,     generator=a			,     latents=a			,     output_type=a			,     return_dict=a			,     callback=a			,     callback_steps=a			,     **a			,     )

								# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
								return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]   )





