






"""simple docstring"""

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase    			=       False
lowercase    			=       True
lowercase    			=       False


if __name__ == "__main__":
			lowercase    			=       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.''')

			lowercase    			=       parser.parse_args()

			lowercase    			=       {
			    '''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''',
			}

			lowercase    			=       {
			    '''time_steps''': '''time_proj''',
			    '''mid''': '''mid_block''',
			    '''downsample_blocks''': '''down_blocks''',
			    '''upsample_blocks''': '''up_blocks''',
			}

			lowercase    			=       '''''' 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:
						lowercase    			=       reader.read()
						lowercase    			=       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'''):
						lowercase    			=       UNetaDModel(**config)
			else:
						lowercase    			=       UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
						lowercase    			=       class_name(**config)

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

			lowercase    			=       dict(model.config)

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

						lowercase    			=       [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
						lowercase    			=       [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

			if do_only_weights:
						lowercase    			=       torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

						lowercase    			=       {}
						for param_key, param_value in state_dict.items():
									if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
												continue
									lowercase    			=       False
									for key, new_key in key_parameters_to_change.items():
												if not has_changed and param_key.split('''.''')[0] == key:
															lowercase    			=       param_value
															lowercase    			=       True
									if not has_changed:
												lowercase    			=       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 Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder

@dataclass
class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				42





class 						A_							(      snake_case_					,							snake_case_  ):
				UpperCAmelCase__        =				True







				@register_to_config
				def __init__(							self							:						Any						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						int = 3						,    __lowerCamelCase							:						Tuple[str] = ("DownEncoderBlock2D",)						,    __lowerCamelCase							:						Tuple[str] = ("UpDecoderBlock2D",)						,    __lowerCamelCase							:						Tuple[int] = (6_4,)						,    __lowerCamelCase							:						int = 1						,    __lowerCamelCase							:						str = "silu"						,    __lowerCamelCase							:						int = 4						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						int = 3_2						,    __lowerCamelCase							:						float = 0.1_8215						,    )				->					Any:
										super().__init__()

										# pass init params to Encoder
										__magic_name__            =     Encoder(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    down_block_types=__lowerCamelCase						,    block_out_channels=__lowerCamelCase						,    layers_per_block=__lowerCamelCase						,    act_fn=__lowerCamelCase						,    norm_num_groups=__lowerCamelCase						,    double_z=__lowerCamelCase						,    )

										# pass init params to Decoder
										__magic_name__            =     Decoder(
										    in_channels=__lowerCamelCase						,    out_channels=__lowerCamelCase						,    up_block_types=__lowerCamelCase						,    block_out_channels=__lowerCamelCase						,    layers_per_block=__lowerCamelCase						,    norm_num_groups=__lowerCamelCase						,    act_fn=__lowerCamelCase						,    )

										__magic_name__            =     nn.Convad(2 * latent_channels						,    2 * latent_channels						,    1	)
										__magic_name__            =     nn.Convad(__lowerCamelCase						,    __lowerCamelCase						,    1	)

										__magic_name__            =     False
										__magic_name__            =     False

										# only relevant if vae tiling is enabled
										__magic_name__            =     self.config.sample_size
										__magic_name__            =     (
										    self.config.sample_size[0]
										    if isinstance(self.config.sample_size						,    (list, tuple)	)
										    else self.config.sample_size
										)
										__magic_name__            =     int(sample_size / (2 ** (len(self.config.block_out_channels	) - 1))	)
										__magic_name__            =     0.25







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict=False	)				->					Optional[int]:
										if isinstance(__lowerCamelCase						,    (Encoder, Decoder)	):
																__magic_name__            =     value







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						bool = True	)				->					int:
										__magic_name__            =     use_tiling







				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										self.enable_tiling(__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					str:
										__magic_name__            =     True







				def     _snake_case					(							self							:						Optional[Any]	)				->					Tuple:
										__magic_name__            =     False







				@property
				# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
				def     _snake_case					(							self							:						Optional[int]	)				->					Dict[str, AttentionProcessor]:
										__magic_name__            =     {}

										def fn_recursive_add_processors(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						Dict[str, AttentionProcessor]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						__magic_name__            =     module.processor

																for sub_name, child in module.named_children():
																						fn_recursive_add_processors(f'''{name}.{sub_name}'''						,    __lowerCamelCase						,    __lowerCamelCase	)

																return processors

										for name, module in self.named_children():
																fn_recursive_add_processors(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)

										return processors







				def     _snake_case					(							self							:						Dict						,    __lowerCamelCase							:						Union[AttentionProcessor, Dict[str, AttentionProcessor]]	)				->					int:
										__magic_name__            =     len(self.attn_processors.keys()	)

										if isinstance(__lowerCamelCase						,    __lowerCamelCase	) and len(__lowerCamelCase	) != count:
																raise ValueError(
																    f'''A dict of processors was passed, but the number of processors {len(__lowerCamelCase	)} does not match the'''
																    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''	)

										def fn_recursive_attn_processor(__lowerCamelCase							:						str						,    __lowerCamelCase							:						torch.nn.Module						,    __lowerCamelCase							:						List[str]	):
																if hasattr(__lowerCamelCase						,    "set_processor"	):
																						if not isinstance(__lowerCamelCase						,    __lowerCamelCase	):
																												module.set_processor(__lowerCamelCase	)
																						else:
																												module.set_processor(processor.pop(f'''{name}.processor'''	)	)

																for sub_name, child in module.named_children():
																						fn_recursive_attn_processor(f'''{name}.{sub_name}'''						,    __lowerCamelCase						,    __lowerCamelCase	)

										for name, module in self.named_children():
																fn_recursive_attn_processor(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										self.set_attn_processor(AttnProcessor()	)







				@apply_forward_hook
				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
																return self.tiled_encode(__lowerCamelCase						,    return_dict=__lowerCamelCase	)

										if self.use_slicing and x.shape[0] > 1:
																__magic_name__            =     [self.encoder(__lowerCamelCase	) for x_slice in x.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self.encoder(__lowerCamelCase	)

										__magic_name__            =     self.quant_conv(__lowerCamelCase	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
																return self.tiled_decode(__lowerCamelCase						,    return_dict=__lowerCamelCase	)

										__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
										__magic_name__            =     self.decoder(__lowerCamelCase	)

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				@apply_forward_hook
				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										if self.use_slicing and z.shape[0] > 1:
																__magic_name__            =     [self._decode(__lowerCamelCase	).sample for z_slice in z.split(1	)]
																__magic_name__            =     torch.cat(__lowerCamelCase	)
										else:
																__magic_name__            =     self._decode(__lowerCamelCase	).sample

										if not return_dict:
																return (decoded,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[str]						,    __lowerCamelCase							:						Dict	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[2]						,    b.shape[2]						,    __lowerCamelCase	)
										for y in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
										return b







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Any						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Tuple	)				->					Optional[int]:
										__magic_name__            =     min(a.shape[3]						,    b.shape[3]						,    __lowerCamelCase	)
										for x in range(__lowerCamelCase	):
																__magic_name__            =     a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
										return b







				def     _snake_case					(							self							:						Optional[int]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					AutoencoderKLOutput:
										__magic_name__            =     int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_latent_min_size * self.tile_overlap_factor	)
										__magic_name__            =     self.tile_latent_min_size - blend_extent

										# Split the image into 512x512 tiles and encode them separately.
										__magic_name__            =     []
										for i in range(0						,    x.shape[2]						,    __lowerCamelCase	):
																__magic_name__            =     []
																for j in range(0						,    x.shape[3]						,    __lowerCamelCase	):
																						__magic_name__            =     x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
																						__magic_name__            =     self.encoder(__lowerCamelCase	)
																						__magic_name__            =     self.quant_conv(__lowerCamelCase	)
																						row.append(__lowerCamelCase	)
																rows.append(__lowerCamelCase	)
										__magic_name__            =     []
										for i, row in enumerate(__lowerCamelCase	):
																__magic_name__            =     []
																for j, tile in enumerate(__lowerCamelCase	):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												__magic_name__            =     self.blend_v(rows[i - 1][j]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						if j > 0:
																												__magic_name__            =     self.blend_h(row[j - 1]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						result_row.append(tile[:, :, :row_limit, :row_limit]	)
																result_rows.append(torch.cat(__lowerCamelCase						,    dim=3	)	)

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										__magic_name__            =     DiagonalGaussianDistribution(__lowerCamelCase	)

										if not return_dict:
																return (posterior,)

										return AutoencoderKLOutput(latent_dist=__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = True	)				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
										__magic_name__            =     int(self.tile_sample_min_size * self.tile_overlap_factor	)
										__magic_name__            =     self.tile_sample_min_size - blend_extent

										# Split z into overlapping 64x64 tiles and decode them separately.
										# The tiles have an overlap to avoid seams between tiles.
										__magic_name__            =     []
										for i in range(0						,    z.shape[2]						,    __lowerCamelCase	):
																__magic_name__            =     []
																for j in range(0						,    z.shape[3]						,    __lowerCamelCase	):
																						__magic_name__            =     z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
																						__magic_name__            =     self.post_quant_conv(__lowerCamelCase	)
																						__magic_name__            =     self.decoder(__lowerCamelCase	)
																						row.append(__lowerCamelCase	)
																rows.append(__lowerCamelCase	)
										__magic_name__            =     []
										for i, row in enumerate(__lowerCamelCase	):
																__magic_name__            =     []
																for j, tile in enumerate(__lowerCamelCase	):
																						# blend the above tile and the left tile
																						# to the current tile and add the current tile to the result row
																						if i > 0:
																												__magic_name__            =     self.blend_v(rows[i - 1][j]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						if j > 0:
																												__magic_name__            =     self.blend_h(row[j - 1]						,    __lowerCamelCase						,    __lowerCamelCase	)
																						result_row.append(tile[:, :, :row_limit, :row_limit]	)
																result_rows.append(torch.cat(__lowerCamelCase						,    dim=3	)	)

										__magic_name__            =     torch.cat(__lowerCamelCase						,    dim=2	)
										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						torch.FloatTensor						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[torch.Generator] = None						,    )				->					Union[DecoderOutput, torch.FloatTensor]:
										__magic_name__            =     sample
										__magic_name__            =     self.encode(__lowerCamelCase	).latent_dist
										if sample_posterior:
																__magic_name__            =     posterior.sample(generator=__lowerCamelCase	)
										else:
																__magic_name__            =     posterior.mode()
										__magic_name__            =     self.decode(__lowerCamelCase	).sample

										if not return_dict:
																return (dec,)

										return DecoderOutput(sample=__lowerCamelCase	)



