






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 List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowercase  							=					logging.get_logger(__name__)  # pylint: disable=invalid-name

lowercase  							=					"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"





def 						__UpperCAmelCase			(     a_							,     a_							,     a_=8):
							snake_case_				=							height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							snake_case_				=							width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a       ,			a       ,			)       ->    Tuple:
								super().__init__()

								self.register_modules(
								    unet=a       ,			scheduler=a       ,			movq=a       ,			)
								snake_case_				=							2 ** (len(self.movq.config.block_out_channels					) - 1)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Any:
								if latents is None:
															snake_case_				=							randn_tensor(a       ,			generator=a       ,			device=a       ,			dtype=a					)
								else:
															if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
															snake_case_				=							latents.to(a					)

								snake_case_				=							latents * scheduler.init_noise_sigma
								return latents




	def 							_UpperCamelCase			(       self       ,			a=0					)       ->    str:
								if is_accelerate_available():
															from accelerate import cpu_offload
								else:
															raise ImportError('Please install accelerate via `pip install accelerate`'					)

								snake_case_				=							torch.device(F'''cuda:{gpu_id}'''					)

								snake_case_				=							[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																						cpu_offload(a       ,			a					)




	def 							_UpperCamelCase			(       self       ,			a=0					)       ->    List[str]:
								if is_accelerate_available() and is_accelerate_version('>='       ,			'0.17.0.dev0'					):
															from accelerate import cpu_offload_with_hook
								else:
															raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'					)

								snake_case_				=							torch.device(F'''cuda:{gpu_id}'''					)

								if self.device.type != "cpu":
															self.to('cpu'       ,			silence_dtype_warnings=a					)
															torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

								snake_case_				=							None
								for cpu_offloaded_model in [self.unet, self.movq]:
															snake_case_			,		snake_case_				=							cpu_offload_with_hook(a       ,			a       ,			prev_module_hook=a					)

								# We'll offload the last model manually.
								snake_case_				=							hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 							_UpperCamelCase			(       self					)       ->    Any:
								if not hasattr(self.unet       ,			'_hf_hook'					):
															return self.device
								for module in self.unet.modules():
															if (
															    hasattr(a       ,			'_hf_hook'					)
															    and hasattr(module._hf_hook       ,			'execution_device'					)
															    and module._hf_hook.execution_device is not None
															):
																						return torch.device(module._hf_hook.execution_device					)
								return self.device






	@torch.no_grad()
	@replace_example_docstring(a					)
	def __call__(       self       ,			a       ,			a       ,			a       ,			a = 5_12       ,			a = 5_12       ,			a = 1_00       ,			a = 4.0       ,			a = 1       ,			a = None       ,			a = None       ,			a = "pil"       ,			a = True       ,			)       ->    List[str]:
								snake_case_				=							self._execution_device

								snake_case_				=							guidance_scale > 1.0

								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)
								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)
								if isinstance(a       ,			a					):
															snake_case_				=							torch.cat(a       ,			dim=0					)

								snake_case_				=							image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
															snake_case_				=							image_embeds.repeat_interleave(a       ,			dim=0					)
															snake_case_				=							negative_image_embeds.repeat_interleave(a       ,			dim=0					)
															snake_case_				=							hint.repeat_interleave(a       ,			dim=0					)

								snake_case_				=							torch.cat([negative_image_embeds, image_embeds]       ,			dim=0					).to(dtype=self.unet.dtype       ,			device=a					)
								snake_case_				=							torch.cat([hint, hint]       ,			dim=0					).to(dtype=self.unet.dtype       ,			device=a					)
								self.scheduler.set_timesteps(a       ,			device=a					)
								snake_case_				=							self.scheduler.timesteps

								snake_case_				=							self.movq.config.latent_channels

								snake_case_			,		snake_case_				=							downscale_height_and_width(a       ,			a       ,			self.movq_scale_factor					)

								# create initial latent
								snake_case_				=							self.prepare_latents(
								    (batch_size, num_channels_latents, height, width)       ,			image_embeds.dtype       ,			a       ,			a       ,			a       ,			self.scheduler       ,			)

								for i, t in enumerate(self.progress_bar(a					)					):
															# expand the latents if we are doing classifier free guidance
															snake_case_				=							torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

															snake_case_				=							{'image_embeds': image_embeds, 'hint': hint}
															snake_case_				=							self.unet(
															    sample=a       ,			timestep=a       ,			encoder_hidden_states=a       ,			added_cond_kwargs=a       ,			return_dict=a       ,			)[0]

															if do_classifier_free_guidance:
																						snake_case_			,		snake_case_				=							noise_pred.split(latents.shape[1]       ,			dim=1					)
																						snake_case_			,		snake_case_				=							noise_pred.chunk(2					)
																						snake_case_			,		snake_case_				=							variance_pred.chunk(2					)
																						snake_case_				=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						snake_case_				=							torch.cat([noise_pred, variance_pred_text]       ,			dim=1					)

															if not (
															    hasattr(self.scheduler.config       ,			'variance_type'					)
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																						snake_case_			,		snake_case_				=							noise_pred.split(latents.shape[1]       ,			dim=1					)

															# compute the previous noisy sample x_t -> x_t-1
															snake_case_				=							self.scheduler.step(
															    a       ,			a       ,			a       ,			generator=a       ,			)[0]
								# post-processing
								snake_case_				=							self.movq.decode(a       ,			force_not_quantize=a					)['sample']

								if output_type not in ["pt", "np", "pil"]:
															raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''					)

								if output_type in ["np", "pil"]:
															snake_case_				=							image * 0.5 + 0.5
															snake_case_				=							image.clamp(0       ,			1					)
															snake_case_				=							image.cpu().permute(0       ,			2       ,			3       ,			1					).float().numpy()

								if output_type == "pil":
															snake_case_				=							self.numpy_to_pil(a					)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a					)





