





"""simple docstring"""




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


a		:				Optional[Any]  			=    False
a		:				List[Any]  			=    True
a		:				List[Any]  			=    False


if __name__ == "__main__":
		a		:				Tuple  			=    argparse.ArgumentParser()

		parser.add_argument(
		    '''--repo_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the architecture.''',
		)

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		a		:				Tuple  			=    parser.parse_args()

		a		:				List[str]  			=    {
		    '''image_size''': '''sample_size''',
		    '''num_res_blocks''': '''layers_per_block''',
		    '''block_channels''': '''block_out_channels''',
		    '''down_blocks''': '''down_block_types''',
		    '''up_blocks''': '''up_block_types''',
		    '''downscale_freq_shift''': '''freq_shift''',
		    '''resnet_num_groups''': '''norm_num_groups''',
		    '''resnet_act_fn''': '''act_fn''',
		    '''resnet_eps''': '''norm_eps''',
		    '''num_head_channels''': '''attention_head_dim''',
		}

		a		:				Optional[int]  			=    {
		    '''time_steps''': '''time_proj''',
		    '''mid''': '''mid_block''',
		    '''downsample_blocks''': '''down_blocks''',
		    '''upsample_blocks''': '''up_blocks''',
		}

		a		:				Union[str, Any]  			=    '''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

		with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
				a		:				int  			=    reader.read()
				a		:				List[Any]  			=    json.loads(text)

		if do_only_config:
				for key in config_parameters_to_change.keys():
						config.pop(key, None)

		if has_file(args.repo_path, '''config.json'''):
				a		:				List[Any]  			=    UNetaDModel(**config)
		else:
				a		:				Dict  			=    UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
				a		:				List[str]  			=    class_name(**config)

		if do_only_config:
				model.save_config(os.path.join(args.repo_path, subfolder))

		a		:				str  			=    dict(model.config)

		if do_only_renaming:
				for key, value in config_parameters_to_change.items():
						if key in config:
								a		:				str  			=    config[key]
								del config[key]

				a		:				int  			=    [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
				a		:				Tuple  			=    [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

		if do_only_weights:
				a		:				List[Any]  			=    torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

				a		:				int  			=    {}
				for param_key, param_value in state_dict.items():
						if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
								continue
						a		:				List[Any]  			=    False
						for key, new_key in key_parameters_to_change.items():
								if not has_changed and param_key.split('''.''')[0] == key:
										a		:				Union[str, Any]  			=    param_value
										a		:				str  			=    True
						if not has_changed:
								a		:				List[Any]  			=    param_value

				model.load_state_dict(new_state_dict)
				model.save_pretrained(os.path.join(args.repo_path, subfolder))









"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      Optional[torch.FloatTensor]         =None


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         =2






							@register_to_config
							def __init__(     self   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 1.007   ,      lowerCAmelCase__ = 80   ,      lowerCAmelCase__ = 0.05   ,      lowerCAmelCase__ = 50   ,      )			->				Union[str, Any]:
								# standard deviation of the initial noise distribution
								a   :	Tuple			=       sigma_max

								# setable values
								a   :	int			=       None
								a   :	np.IntTensor			=       None
								a   :	torch.FloatTensor			=       None  # sigma(t_i)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				torch.FloatTensor:
								return sample






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
								a   :	List[Any]			=       num_inference_steps
								a   :	List[str]			=       np.arange(0   ,      self.num_inference_steps   )[::-1].copy()
								a   :	int			=       torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[str]			=       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								a   :	Any			=       torch.tensor(lowerCAmelCase__   ,      dtype=torch.floataa   ,      device=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
									a   :	str			=       min(self.config.s_churn / self.num_inference_steps   ,      2**0.5 - 1   )
								else:
									a   :	Dict			=       0

								# sample eps ~ N(0, S_noise^2 * I)
								a   :	Union[str, Any]			=       self.config.s_noise * randn_tensor(sample.shape   ,      generator=lowerCAmelCase__   ).to(sample.device   )
								a   :	Any			=       sigma + gamma * sigma
								a   :	Tuple			=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Union[str, Any]			=       sample_hat + sigma_hat * model_output
								a   :	Tuple			=       (sample_hat - pred_original_sample) / sigma_hat
								a   :	List[Any]			=       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Optional[int]			=       sample_prev + sigma_prev * model_output
								a   :	str			=       (sample_prev - pred_original_sample) / sigma_prev
								a   :	Dict			=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								raise NotImplementedError()


